The Salesforce-owned messaging platform today announced Slack Code, a new product that embeds AI coding agents — including Anthropic's Claude Code, Cognition's Devin, GitHub Copilot, and Vercel's agent — directly into dedicated Slack channels where entire teams can watch, steer, review, and ship software together. Slack Code is available on any Slack plan at launch, though customers need their own access to the partner agents.

The pitch is deceptively simple: today, most work with AI coding agents happens between one person and one agent, invisible to everyone else. Slack Code makes that work "multiplayer." When someone tags a coding agent from any conversation, the agent spins up a project-specific code channel, does the work in the open — complete with code diffs, live previews, and a running plan visible in dedicated tabs — and archives the channel when the job is done, leaving behind a searchable audit trail.

"One of the things I love about this is that code is no longer the bottleneck," Rob Seaman, Slack's interim CEO, said in a press briefing ahead of the launch. "Ideas, taste, judgment, craft — those are the things that are the bottleneck, and you've effectively extended the population that can contribute ideas, taste, judgment, and craft to anybody that exists in your Slack."

It is a consequential launch for Slack, and a revealing one for the broader enterprise AI market. The AI coding boom has so far been a story of individual productivity — a developer alone with Claude Code or OpenAI's Codex in a terminal window. Slack is betting that the next chapter belongs to whoever owns the collaborative layer around those agents. And it is making that bet at a moment when its parent company badly needs the story to land.

How Slack Code channels put AI coding agents to work in the open

In the interview, which also included executives from Cognition, Slack leaders described a workflow that looks less like pair programming and more like a newsroom.

Jeff Wang, president of new enterprise at Cognition — maker of the Devin coding agent — walked through a live demonstration: someone reports a broken feature in an engineering channel, Devin acknowledges it with an emoji, replies in the thread, investigates, and opens a pull request. "It even knows the code owner, so you can see it tagged Theo into this as well," Wang said. "Every time Devin is doing something like this, it does have its own computer. So here, it's actually using Chrome and the DevTools to test if the feature is working correctly."

From there, the work migrates into a dedicated code channel where anyone — an engineer, a product manager, a designer — can jump in. In Wang's demo, a designer dropped a Figma file into the channel mid-task, and the agent incorporated it without breaking stride. The agent finished by posting the code changes alongside screenshots and a recorded demo proving the feature worked. That verification loop is central to the pitch: cloud-based agents, unlike agents running on a developer's laptop, can generate an auditable record that the work is actually correct. "Scaling things, auditing things, giving it to everybody — that is much easier with these cloud agents form factor than it is with local agents," Wang said.

The launch reaches beyond code channels, too. Slack is shipping a broader rework of how agents live in the product: agent DMs that behave like conversations with a colleague, a new Agents tab that gives every agent session a home base with live status and a stop button, and an "Add to Slack" flow that lets teams deploy agents from platforms including Lovable, n8n, OpenAI, LangChain, and Airtable in a few clicks, with OAuth and configuration automated.

Why Slack says writing code is no longer the bottleneck in software development

The strategic argument underneath Slack Code is that AI has inverted the economics of software development. Writing code used to be the scarce, expensive step. Now, Slack's executives argue, it is the cheap one — and the constraint has moved upstream, to human judgment.

"One of the things I love about this is that code is no longer the bottleneck," Seaman said in the press briefing. "Ideas, taste, judgment, craft — those are the things that are the bottleneck, and you've effectively extended the population that can contribute ideas, taste, judgment, and craft to anybody that exists in your Slack."

Cognition offered internal numbers to back up the velocity claim. "We've seen our internal merged PR count go up 10x in the last few months, versus our headcount has only gone up like 40 percent," Wang said, describing a workflow where engineers fire off a Devin task, move to something else, and launch another — "soon you have everybody working on like dozens of agents at a time."

The pattern extends well beyond engineers, Wang said. "Believe it or not, a lot of our bugs are reported by our sales team. They report it in Slack, and then someone who's technical applies them to fix the bug." Seaman seized on that example as the whole thesis in miniature: "So much of that stuff never even made its way to a product manager into a backlog because the communication vehicles weren't there, the motivation wasn't there, the knowledge that it could actually be fixed so quick wasn't there — and we've effectively knocked all of that down."

Wang went further, sketching where he believes this ends up. Toil work — "fixing bugs, fixing CI/CD, or fixing vulnerabilities, all these things engineers probably don't want to do — we think will be automated away," he said. What remains is the work that "requires creativity, planning, business logic." He added a prediction that will make some engineering leaders uneasy: while a human still gates every merge today, "I suspect maybe in the next year it's just going to go through automatically."

Can working in public solve the AI slop problem?

The obvious objection to democratizing software creation is quality. If anyone in a company can summon a coding agent, does an enterprise drown in what the industry has taken to calling "AI slop" — plausible-looking but poorly conceived output generated at scale by inexperienced users?

Slack's executives argue, somewhat counterintuitively, that visibility is the antidote rather than the accelerant. "The multiplayer part is a guard against that, actually, because people can see your work, people can comment on your work," said Katie Steigman, Slack's VP of product. She contrasted it with the status quo: "If I'm doing God knows what in terminal with an agent, versus being able to do it in a place where people can see my intent and actually change and shape my work — or slap my hand and tell me that's slop, because that's real."

Steigman, a product manager rather than an engineer, described her own practice as a template. "When I put PRs up as a product person, I almost always tag in an engineer from my team. I don't just send a PR and ask for an approval," she said. "Almost every time, an engineer will say something like, 'Come on, you can make that a little bit tighter,' or they'll actually give it some specific technical guidance, and the agent will take one more rev and produce code that has been touched by an engineer to a certain extent."

Seaman framed the argument in grander terms: "I think the moral arc of multiplayer AI bends towards higher quality and less duplication." He pointed to Shopify, where he said CEO Tobi Lütke has written about restricting agentic coding to public channels precisely because it "immediately disseminates every single thing that's happening in the company" and levels the playing field. Still, the skeptics' case has data behind it. 

Gartner predicted last year that more than 40 percent of agentic AI projects will be canceled by the end of 2027, citing escalating costs and unclear business value. And McKinsey's most recent State of AI survey found that while 62 percent of organizations are at least experimenting with AI agents, only about a third have begun scaling AI at all, and just 39 percent report any bottom-line impact. The gap between agent enthusiasm and agent value remains the defining feature of the market Slack is selling into.

Inside Slack Code's security model: no god mode, no new identities

For enterprise buyers, the most consequential design decision in Slack Code may be its permissions model. Asked directly whether agents in code channels could leak access across teams — a finance repo visible to legal, say — Seaman was emphatic that agents inherit the permissions of the human who invokes them, and nothing more.

"Everything is done on behalf of the user, using the user's ACLs, both in Slack and in the systems that they're connecting to," he said. "There's no god permissions or bot-level permissions... Within Slack, the agent has access to information that the user has access to, and access to the channels that it's been added to."

Steigman added that when an agent spins up a code channel, "the only thing that agent gets from the code channel is the context of the conversation" that triggered it. On the execution side, Wang said Devin runs in isolated sandboxes with "minimum viable access" — including an optional mode with no internet access at all. "You've heard all these stories about the agent escaping and causing havoc," he acknowledged, "but we have different security configurations."

This "agents as extensions of existing users" model is a genuine differentiator against standalone agent platforms, which typically force IT departments to provision new service identities and manage a patchwork of one-off permissions. It also answers the shadow-IT question that has dogged agentic tools: because agents produce standard pull requests into GitHub, existing release gates and review processes still apply. "It reduces that barrier upfront to get that initial PR up," Steigman said. "Then the due diligence still happens in GitHub."

What Slack Code means for Salesforce's high-stakes AI turnaround

Slack Code arrives amid a turbulent stretch for its parent company. Salesforce shares fell roughly 18 percent over the year through January, badly lagging the Nasdaq, as Wall Street questioned whether AI would erode demand for traditional enterprise software. In December, OpenAI hired away Slack CEO Denise Dresser as its chief revenue officer, elevating Seaman — then Slack's product chief — to interim CEO.

Salesforce has responded by racing to make Slack the AI front door for work. In January it shipped a rebuilt Slackbot powered by Anthropic's Claude, which the companies said became the fastest-adopted feature in the company's 27-year history. Slack Code extends that strategy from answering questions to producing artifacts: not just messages, but working code, prototypes, and documents generated inside Slack itself.

There is also a notable strategic reversal embedded in today's news. In mid-2025, Reuters reported that Salesforce had moved to block rival AI firms from accessing Slack data — a defensive crouch.

Today's announcement, by contrast, positions Slack as an open platform courting exactly those AI companies as partners, with plans to open the code channel APIs to any developer. Software engineering, the company says, is just the first use case; marketing campaigns and legal document reviews in dedicated agent channels are next. The calculus appears to have shifted from protecting Slack's data to making Slack indispensable as the venue where agents — anyone's agents — do their work.

Partners are, unsurprisingly, saying the right things. "A whole team can gather in one code channel, watch the agent work, steer it together, and ship a preview," said Vercel CTO Malte Ubl. GitHub chief product officer Mario Rodriguez called Slack "a strategic part of a broader GitHub promise: humans set direction, agents close the loop."

The future of AI coding: multiplayer channels and single-player terminals will coexist

None of Slack's executives claim the terminal is dead. Asked whether tools like Claude Code and Codex become obsolete, Seaman predicted a division of labor. "The overwhelming majority of the work is actually going to happen in these multiplayer environments," he said. "But there's going to be deep, immersive, intensive, single-player thought work that's going to happen in terminals." An engineer rethinking a codebase's architecture goes heads-down with an agent; a sales rep flagging a broken button gets a fix in a channel everyone can see.

The trust curve, Seaman suggested, will look familiar to anyone who watched teams adopt earlier waves of automation. "People are going to open these things at the beginning" — reading every diff, every thinking step — "and then build trust in the system and open it less and less over time."

That is the wager, and it is bigger than a product launch. McKinsey's research shows the organizations capturing real value from AI are the ones that redesign workflows around it rather than bolting it onto old processes — and Slack Code is, at bottom, a workflow redesign packaged as a feature, an attempt to make the team rather than the individual the unit of AI adoption. If it works, the company that once changed where colleagues talk will have changed where software gets made. If it doesn't, all that transparency may just mean everyone gets to watch the slop pile up together.

Either way, the era of the lone developer whispering to an agent in a private tab is ending. As Wang put it: "The bottlenecks have shifted." The question Slack Code will answer is whether the crowd makes them smaller — or just louder.

This is not just to avoid vendor lock-in and retain flexibility (although that’s a big part of it). There’s still a lot of uncertainty, even distrust, in vendors’ security and permissioning capabilities. Enterprises want the ability to impose their own.

Microsoft leads on primary usage today, while Anthropic leads by a wide margin in what enterprises are considering next. But enterprises still struggle with many challenges, notably around token usage and visibility into agent spending.

These findings are from an ongoing analysis of how enterprises are actually deploying and using AI: Their platforms of choice, what guides their decision-making, what they prioritize, their AI expectations, how they control costs, and whether their AI is actually agentic or still a chatbot in an "agent" label.

VB Intelligence is getting feedback from builders actually in the trenches: software and machine learning (ML) engineers, product and program managers, and data/AI/analytics VPs and directors.

Concerns around retaining visibility and control

Across 107 enterprises, agentic orchestration has become decidedly plural. The survey found that the majority of enterprises are not committing themselves to any one model: 85% are using two or more orchestration tools; 64% are using three. Just 15% run a single orchestration platform.

Microsoft AI Foundry/Copilot Studio shows up in 70% of stacks, OpenAI’s Agents SDK in 68%, and Anthropic’s Claude Platform in 47%. Builders surveyed are also to some extent using Google’s Enterprise Agent Platform, LangChain/LangGraph, Salesforce Agentforce, Amazon Bedrock, and LlamaIndex. Augmenting vendor tools, 22% of builders run custom in-house orchestration.

This trend of hybridability is only expected to continue. More than half of respondents (53%) said the primary control plane will be hybrid by the end of 2026. Fourteen percent expect to use a provider-managed service, 13% plan on a custom in-house control plane, and 11% are betting on external platforms that are abstracted away from model providers.

Dovetailing with this, more than two-thirds of respondents plan to change platforms within the year: 15% in the next three months (or sooner), 24% in three to six months, and 28% in six to 12 months. Claude Agent SDK is a top tool under consideration; 43% of builders are exploring the Anthropic-built model. Roughly one-third are looking at Google’s Enterprise Agent Platform, another 31% are focused on custom in-house orchestration, and 25% are investigating OpenAI’s options.

Perhaps learning from the lock-in of the early cloud days, enterprises aren’t choosing one “winner.” They are deliberately building for a future where multiple orchestration platforms, models, and agents work with each other across a hybrid control plane.

Generally speaking, respondents are pleased with the platforms they’ve been running, rating them 4.17 out of 5 for overall satisfaction. But they are less satisfied with ease of implementation (rating it 3.91 out of 5) and value for the money (3.63 out of 5). Keep an eye on these ratings as orchestration platforms and AI roadmaps mature.

Where enterprises are putting their money

Enterprise buying logic is now based on a mix of several factors. Beyond flexibility (cited by 29% of respondents), top considerations include security and permissions (17%), production reliability (15%), and control over agent execution (15%). Just one out of 10 identify model gravity — native alignment with a state-of-the-art base model — as important in purchasing decisions; 8% name ease of development, 4% cite total cost of ownership, and just 2% cite latency and memory performance.

Spending also reflects enterprise priority on visibility, security, and control. Builders are investing the most in agent monitoring and debugging (31%) and security and permissions enforcement (30%). Workflow tooling accounts for another 19%. That's a shift from VentureBeat's prior wave a month earlier, when workflow tooling led orchestration spending outright.

Enterprises are largely optimizing for task completion reliability (30%), multi-step workflow management (27%), developer productivity (23%), and operational stability (13%). Just 7% of respondents name end-user experience as a top priority at this point, indicating that many are still focused on orchestration at this point rather than UX.

Essentially, enterprises are signaling that workflow succeeds when it carries multiple steps to completion. Simplifying development and end-user experiences could become a larger concern when platforms are actually in place.

The visibility problem

Builders’ biggest concerns when choosing platforms center around control and oversight. They don’t want vendors to constrain their ability to see what their agents are doing on a given platform. Factors top of mind include security and permissioning limitations (37%), vendor lock-in (23%), limited visibility and observability (22%) and inflexibility around models and tools (16%).

Meanwhile, in these early days of AI agents, enterprises still struggle to control agent token use; one in five still can’t stop a runaway agent’s spending in real time.

Builders are using various strategies to try to keep agent spending in line: 30% rely on native platform controls (built-in budget caps or throttling) and 25% have built custom gateway plumbing (proxy middleware to intercept runaway agents).

A quarter of respondents use dynamic routing to offload heavy work to low-cost models, and 21% still rely solely on reactive monitoring, such as post-hoc logs; these enterprises have no real-time kill switches.

One interesting finding: unlike the prior wave, organization size makes little difference in fiscal control maturity — 18% of enterprises with 10,000-plus employees exercise only reactive control, compared to 23% of smaller ones.

Clearly, while enterprises recognize the problem with spend, many have not yet instrumented their stacks to rein it in.

Most enterprises still aren't running true multi-step agents

Builders polled were asked to honestly assess their tech stacks; the consensus seems to be that ‘agents’ are slowly but surely progressing beyond chatbots wrapped in that fancier label.

Here’s how the numbers break down: A small number of respondents (2%) report that 76 to 100% of their systems are advanced and largely autonomous; 14% say 51 to 75% of their systems are complex, multi-agent pipelines; and 47% report that 26 to 50% of their systems are true orchestration.

On the other end of the spectrum, 35% say just 1 to 25% of their systems are true orchestration; most deployments remain basic assistants, and 3% are still only deploying chatbots.

This is in line with VB’s June Pulse survey: 71% of respondents said a quarter or fewer of their deployed “agents” can autonomously complete multi-step work, and just one-tenth say they have deployed agents at scale.

There’s no doubt that enterprises are building control planes and infrastructures for agents; but for many of them, the true agentic wave is still off on the horizon.

Now NanoCo., the company behind the hit open source, enterprise-friendly, autonomous AI agent harness NanoClaw (a more sandboxed, lower code version of OpenClaw), is hoping to make it just as easy as typing a Slack message. To go one step further: the company's new NanoClaw Slack integration lets human users spin up entire teams of agents with their own specialized skills, workflows, and even custom avatars, all from a single Slack prompt.

"In the next 12 to 18 months, everyone on a team will be a manager of agents," NanoCo CEO and co-founder Gavriel Cohen told VentureBeat in an exclusive interview.

Furthermore, the NanoClaw agents can work together in channels and shared Slack Canvases, and can even be messaged outside of Slack on other platforms like Telegram or WhatsApp, letting their human colleagues ping them across messaging platforms, just as they would their fellow humans.

“I think this is agents arriving natively in Slack for the first time,” Cohen added. “In the past, you had to do all these weird things to try to have multiple different agents behind the scenes using the same bot, and now every agent gets its own identity in Slack — its own avatar, its own face, its own name. You can tag them. They can tag each other.”

For enterprise teams, the more consequential part is persistence and separation. NanoClaw is not presenting the additional workers as invisible subagents that disappear after one task. Each can be given its own role, memory context, instructions and permissions, creating a structure closer to a small digital department than a single chatbot with a long prompt.

As with the original open source version of NanoClaw released in January 2026, developers and enterprises can further choose whichever underlying large language model (LLM) they wish to power their NanoClaw agents, optimizing for performance, cost, or other combinations of factors.

From a single NanoClaw Slack agent to a whole specialized team

For a new installation, NanoClaw’s current setup process starts by cloning the project and running its nanoclaw.sh installer, which walks the user through dependencies, credentials, building the agent container and pairing a first messaging channel. NanoClaw’s website says the installer takes a user “from a fresh machine to a named agent you can message,” with Slack among the supported channels.

Cohen described the Slack-specific flow to VentureBeat as a significant simplification over building a traditional Slack bot. Previously, he said, a user would have to navigate Slack’s administrative and developer interfaces, create an app, collect secrets, API keys and tokens, and then move those credentials into wherever the bot was running.

With the new integration, the NanoClaw setup instead offers a Connect Slack option. The user names the agent, authenticates, chooses the NanoClaw Add to Slack option and goes through Slack’s installation and authorization flow. Once authorized, the first agent can appear in Slack and begin communicating with the user.

The important distinction is that this initial authorization is largely a one-time workspace connection. Slack’s Marketplace listing says users “connect a workspace once,” after which NanoClaw can provision each additional agent as its own Slack bot, complete with its own name, generated avatar and identity.

Those agents continue running on the customer’s infrastructure and connect to Slack over Socket Mode. NanoCo says it does not store the agents’ Slack tokens; according to the Marketplace listing, those tokens remain on the user’s machine.

Slack’s standard administrative controls still sit around that system. Organizations can apply their normal app-approval policies to the NanoClaw integration, while NanoClaw’s Marketplace listing says the app’s Home tab displays the agents provisioned in a workspace and lets users revoke individual agents or disconnect the workspace entirely.

The result is less a one-click replacement for NanoClaw’s underlying infrastructure than a one-time bridge between that infrastructure and Slack: users still own and operate the agent runtime, but once the bridge is authorized, the agents themselves can create and coordinate additional Slack-native colleagues without sending the user back through manual app configuration each time.

Behind the scenes, Cohen said, the lead agent has a Model Context Protocol (MCP) tool that can create new agents and define their instructions, personas, skills and tools; another tool can place them into shared rooms. The agents come prepared to work with Slack Canvas and can communicate with every human user on the Slack Channel, and with one another.

The interaction itself is deliberately simple. Rather than opening a separate agent builder every time a new role is needed, Cohen said users can tell the agent they already have what kind of colleague or team they want.

“Your agent in Slack, you can say, ‘Create me another agent to handle my code reviews. Create another agent to review the contributor articles. Create a team of agents that reviews contributor articles from different perspectives.’ And then your agent can create new agents, and they just pop up in the sidebar and send you messages.”

That means a developer could ask for a product manager, architect, implementation agent, code reviewer and testing agent, then give each a different toolset and have them hand work between one another. Cohen said the testing agent, for example, could have access to a testing environment while the review agent carries code-review-specific skills and the product agent monitors user feedback.

Cohen argues that this division of labor is more than cosmetic role-playing. “There are advantages in terms of giving each one specific skills, instructions, and tools for different tasks,” he said. “I can have, let’s say, a code review agent, a code testing agent, a code writing agent, and I can have them in a loop.” If the implementation agent runs into an ambiguity, he added, it can tag the product or architecture agent for clarification rather than forcing one general-purpose model to hold every responsibility and tool in the same context.

Agents work together with humans on a share Slack Canvas

A supplied demo screenshot shows the same pattern applied to marketing: a lead agent named Nano creates Atlas for strategy, Sage for content, Echo for social, Scout for outreach and Compass for SEO and analytics. The agents introduce themselves in the same Slack conversation and begin coordinating work, with Atlas noting that it had added an item to Canvas so the task would not get lost.

Users do not have to specify every detail up front. Cohen said someone could give the lead agent exact review procedures, priorities and required tools, or leave more of the configuration to the agent based on its existing context and memory.

The design also tries to avoid a familiar multi-agent failure mode: bots endlessly triggering one another. NanoCo says the agents reply only when tagged, while comments left on work in Canvas can be routed back to the agent responsible for that piece.

And the model can extend beyond teams of task-specific bots created by one person. Cohen described a workplace where individual employees each have persistent agents that can communicate with one another under human-defined policies.

“Each person having their own agent means that I could have my agent and you have your agent in Slack, and your agent can ask my agent questions,” he said. “Maybe I’m out of the office for the day. Your agent can ping my agent and ask a question about availability, and I can set some policies about whether my agent can answer or if I need to give approval.”

That pushes the concept closer to organizational delegation: some agents specialize by function, while others effectively represent individual employees and the context they have accumulated. Cohen said the agents can be equipped with browser and internet access, memory, coding capabilities and other tools, while newly created agents arrive with built-in support for Canvas work, agent-to-agent communication and spawning still more agents.

Slack is opening the door to more third-party agents

The underlying Slack change is broader than NanoClaw.

In April, Slack, a Salesforce product, announced the ability to add external AI agents to the messaging platform directly, initially pointing to Vercel and Lovable and saying those integrations were coming in late May.

Slack said the deployment mechanism automates OAuth, manifest configuration and environment setup so an externally built agent can be brought into the workspace without being rebuilt specifically for Slack.

Salesforce’s newly published Slack Code page now names NanoClaw alongside Lovable, Hyperagent, Superhuman, n8n, Vercel, ChatGPT, LangChain, Runlayer and Skydive, and says Add to Slack can bring agents from those platforms into Slack in a few clicks with their own identity.

Slack is already crowded with AI assistants. OpenAI, for example, lets ChatGPT workspace agents be deployed into Slack channels, where they can answer questions, perform tasks through connected systems and output files. Slack also supports Claude and custom Agentforce agents. NanoClaw’s differentiation is therefore not simply “AI in Slack.” It is the ability for an already-running agent to create additional, independently addressable teammates from inside the conversation itself. NanoCo calls that a first for Slack; that specific market-first claim is the company’s.

“Add to Slack means one message can spin up a full team of NanoClaw agents, working right alongside people in Slack,” Josh Milas, director of product management at Slack, said in the supplied announcement.

How NanoClaw differs from Claude Tag, ChatGPT agents and Agentforce in Slack

NanoClaw is not alone in trying to turn AI from a sidebar chatbot into something resembling a persistent Slack colleague.

Anthropic’s Claude Tag, which began rolling out in beta to Claude Team and Enterprise customers in June, may be the closest conceptual comparison.

Administrators can give @Claude access to selected channels, tools, data sources and codebases; everyone in the channel can then delegate work to it by tagging it. Claude remembers relevant information from the channels it inhabits, can work asynchronously over hours or days, and, when administrators enable its “ambient” behavior, can proactively flag information or revive unresolved work without waiting for another prompt.

Anthropic says separate Claude identities can also be scoped to different use cases so that, for example, a sales Claude does not share its memories or tools with an engineering Claude.

The difference is in how those digital coworkers are provisioned and organized. Claude Tag’s documented workflow is administrator-led: admins pair Claude with Slack, decide which channels, tools and information each Claude identity can access, set spending limits and then expose those identities to employees.

Within a given channel, Anthropic describes “one Claude that interacts with everyone.” Its public documentation does not describe an end user asking that Claude to create several new, independently named Slack bots on demand. NanoClaw’s model is almost inverted.

After an organization connects its NanoClaw installation to Slack once, NanoClaw says an existing agent can itself provision additional agents from a conversational request, with each new worker receiving its own Slack bot identity, name, generated avatar and token and running back on the customer’s infrastructure.

OpenAI’s ChatGPT Workspace Agents occupy another point on that spectrum.

Business, Edu and Enterprise customers can build reusable agents in ChatGPT, give them instructions, models, files, apps, custom MCP connections and schedules, and then attach those agents to Slack channels.

Builders assign each agent a unique Slack handle and can configure it either to respond only when mentioned or to respond automatically to relevant messages in a channel.

But the construction still happens primarily through ChatGPT’s agent builder: OpenAI’s setup documentation tells users to create the agent first and then add Slack as a channel. Under the hood, the Slack handles rely on Slack user groups managed by the ChatGPT Agents app, rather than NanoClaw’s model in which every provisioned agent is itself a separate Slack bot.

Salesforce’s Agentforce similarly allows organizations to create multiple specialized agents that employees can DM or @mention inside Slack, and it arguably provides the most conventional enterprise administration model of the group.

Companies build the agents in Agentforce Builder, often starting from Slack-specific templates for jobs such as customer insights, employee help or onboarding, and can add subagents and actions that let them search information, create Canvases or perform other work.

Once configured and activated in Salesforce, administrators bring those agents into Slack for employees to use. That makes Agentforce powerful for organizations already centering identity, data and workflows on Salesforce, but again places agent creation before deployment rather than making creation itself something an existing Slack agent can perform during a conversation.

That distinction helps clarify what NanoClaw is actually adding to an increasingly crowded market. Slack itself now provides an Agent Kit for developers and a deployment standard for agents built on outside platforms, automating pieces such as OAuth, manifests and environment configuration. Claude Tag, ChatGPT Workspace Agents and Agentforce all demonstrate that persistent, specialized AI teammates inside Slack are no longer novel on their own.

NanoClaw’s more unusual bet is recursive provisioning: Slack becomes not merely the place where workers invoke agents, but a place where an existing agent can assemble additional named agents, assign them roles and put them together in a channel as a working team.

There are tradeoffs to the different approaches. Claude Tag comes with Anthropic-managed models and centralized administrative controls, including channel-specific permissions, audit logs and token-spending limits, while also offering proactive “ambient” behavior that NanoClaw’s supplied materials do not claim in the same way.

ChatGPT Workspace Agents offer a managed agent builder, schedules, app connections and organization-level publishing and access controls. Agentforce ties agents closely to Salesforce permissions, enterprise data and predefined business actions.

NanoClaw instead emphasizes self-hosting, open-source modification and separate agent identities, shifting more control — and more operational responsibility — to the organization running it.

The result is less a direct replacement for those systems than a different answer to the same emerging question: whether enterprises want a small number of centrally configured AI assistants, or an environment in which employees and existing agents can continuously create specialized digital colleagues as new work appears.

How NanoClaw got here

NanoClaw began far from the enterprise collaboration market. Cohen, a former Wix engineer, launched it under the MIT License on Jan. 31, 2026, as a deliberately small, security-focused alternative to OpenClaw.

The original pitch was that a personal agent with access to messages, files and tools should run inside an OS-isolated container rather than directly on the host, and that the orchestration layer should remain small enough for a developer or security team to understand — an initial core of roughly 500 lines of TypeScript and a design centered on container isolation and a minimal single-process architecture.

The project then moved steadily toward enterprise infrastructure. In March, NanoClaw partnered with Docker to run agents inside Docker Sandboxes, using stronger MicroVM-backed isolation for workloads that may install packages, modify files and launch processes.

In April, NanoClaw 2.0 added Vercel’s Chat SDK and OneCLI’s credential gateway, allowing organizations to define policies around sensitive actions and require human approval before credentials are injected for protected requests.

By May, Cohen and his brother Lazer Cohen had formed NanoCo around the project and raised a $12 million seed round led by Valley Capital Partners, with Docker, Vercel, monday.com and others participating. The commercial strategy is to keep NanoClaw open source while selling managed, organization-wide deployments and “professional assistant” infrastructure to enterprises. The company now says NanoClaw has surpassed 250,000 downloads and 30,000 GitHub stars.

That open-source structure remains central to Cohen’s pitch as NanoClaw moves deeper into workplace infrastructure.

“You’re really able to now integrate an open-source agent into Slack that you fully control,” he said. “You can change all those configurations. Plus, you can fork NanoClaw and completely rewrite or change behaviors — create your own memory system, your own coding harness, agent harness. Whatever you want to do, you can do. Total freedom.”

Persistent agents, but infrastructure stays under the user’s control

Cohen said NanoClaw remains self-hosted: an organization can run it on a local machine or its own cloud VM, with agent data stored there.

The same agent can also appear across Slack, WhatsApp or Telegram while retaining the same memory, workspace and tools, although each messaging surface uses a separate session.

NanoClaw can pull recent context across those sessions so the agent can maintain continuity without merging every chat history into one stream. NanoClaw’s documentation likewise describes a multi-channel architecture in which the same agent can retain one workspace and memory while maintaining separate per-channel sessions.

“This is all self-hosted,” Cohen said. “You’d be running this on your computer or on your virtual machine in the cloud, and that data is stored on your computer or on your [virtual machine] VM. This could be an open-source model running on your Mac Mini, and your data isn’t going anywhere besides your Mac Mini and then into Slack.”

The cross-channel continuity is also intended to make an agent feel less like a Slack-specific bot and more like a persistent colleague that happens to be reachable through Slack.

Cohen said the same agent could exist in Telegram, WhatsApp and Slack with access to the same memory, files and tools. The conversations remain separate sessions, but they share a workspace and persistent context so the agent can carry knowledge from one surface to another.

That architecture matters when an organization starts creating many agents. Cohen said one agent can see its own sessions across channels, but not another agent’s private sessions by default. NanoClaw’s current documentation likewise describes agents running in their own sandboxes and configurable model providers, with Claude Code as the default and Codex, OpenCode and local Ollama models available as alternatives.

There is one cloud dependency for the new Slack flow. Cohen said NanoCo operates a small service that handles Slack provisioning requests and avatar generation. He said it does not receive users’ messages or agent memory.

Continued commitment to open source

NanoCo is not charging for this community Slack capability, according to Cohen, and is absorbing the provisioning-service and avatar-generation costs. Users can still incur their own model inference and hosting expenses, so that does not make a deployed agent team cost-free in practice.

NanoCo says the integration is available through the Slack Marketplace, subject to normal workspace app approval and governance. Slack says workspace owners and administrators can require apps to be approved before installation.

Cohen framed that decision as part of NanoCo’s broader open-source strategy rather than a standalone monetization play. “We’re not making any money off this one. This one is for the community, really,” he said. “We know that in the long run that’s going to benefit NanoCo as a company. As NanoCo grows and builds out capabilities, those go back to the open source. I think that’s the new model of open source, where we’re not trying to monetize every bit of value we bring to the community.”

Whether companies get there that quickly will depend less on how easily agents can be created than on whether IT teams can govern their permissions, memory, spending and failure modes at the same pace. NanoClaw is betting that the next problem is managing the digital coworkers that appear once that barrier is gone.

Catalyst sits above Serval’s AI-native service management platform as an admin-facing “super agent.” It can inspect ticket history, standard operating procedures or natural-language instructions, identify recurring work, and draft the workflows, skills, forms, access policies, journeys and dashboards needed to automate it.

Serval is also using Catalyst to create background agents that continuously inspect connected systems for emerging problems and propose fixes before an employee files a ticket.

That distinction matters because enterprise service management vendors are rapidly converging on AI-assisted workflow creation.

ServiceNow’s Build Agent can already translate natural-language instructions into full-stack applications, flows, scripts and other platform metadata, while its AI Agent Advisor can analyze instance records to identify automation opportunities. Atlassian’s Rovo can generate Jira automation flows from plain-English requirements, and Freshworks offers Freddy AI Agent Studio for creating service agents that act across Freshservice workflows.

So Serval’s claim to differentiation is narrower — and potentially more consequential — than simply “we use AI to build workflows.” Catalyst is designed as a single administrative layer that can move from discovering an opportunity, to assembling multiple kinds of governed automation, to creating proactive agents that keep looking for new work to automate.

"You just started with a single prompt, and now you’ve got enterprise-grade workflows ready to deploy that are going to solve all password resets for the entire company," Serval co-founder and CEO Jake Stauch told VentureBeat in an interview.

From ticket history to working automation

Serval says Catalyst analyzes existing help desk data before an organization has decided what to automate. If it finds a repetitive category of requests, it can draft the automation required to resolve those requests and stage the result for administrator review. Users can also upload an SOP or spreadsheet and ask Catalyst to turn the documented process into an executable system.

Serval’s documentation says Catalyst can build workflows, author help desk skills, create onboarding and offboarding journeys, configure access-management policies, construct dashboards, investigate operational issues and debug failed workflow runs. Unlike Serval’s earlier workflow builder, Catalyst is intended to become the primary interface for configuring the platform; the company says its long-term goal is that anything an administrator can do through the UI should also be possible through Catalyst.

The actual workflows are code-backed. In a demonstration, Stauch showed Catalyst taking a request to build password-reset workflows, detecting connected systems including Okta, Google Workspace and Microsoft Entra, and generating the underlying TypeScript needed to perform those actions. Administrators could then add approvals or restrict who was allowed to run the workflow.

The models underneath Catalyst are deliberately swappable

Serval is not building its own foundation model. Stauch said in the interview that the company uses models from “frontier labs,” runs evaluations to determine which models work best for particular jobs, and is deliberately model-agnostic. “You can swap different models in,” he said, adding that Serval also works with enterprises that build their own models.

Stauch provided more detail in a May 2026 interview with Sequoia Capital, saying Serval was using both OpenAI and Anthropic models. He said OpenAI’s GPT models had performed best for end-user interactions and tool calling, while Anthropic’s Sonnet and Opus models were producing the strongest results for the code-generation side of Serval’s automation system — the workload most directly relevant to Catalyst. Serval continuously runs evals rather than automatically moving every workload to the newest model release, Stauch said.

That architecture makes the underlying LLM less central to Serval’s differentiation. The company’s own documentation now lets organization administrators supply their own OpenAI or Anthropic API keys, including a compatible custom endpoint, while Stauch said the broader architecture can accommodate different models.

The materials do not, however, establish that every Catalyst user gets a self-service menu for arbitrarily choosing an individual model. Serval’s pitch is instead that its proprietary value sits in the harness around those models: enterprise context and memory, integrations, generated code, permissions, approvals and the controls governing what an agent can actually do.

That code-generation model is central to Serval’s pitch against ServiceNow. Stauch argues that legacy ITSM deployments often accumulate custom tables, business rules, workflows and platform-specific expertise that make seemingly simple automation changes expensive to implement. Serval, by contrast, wants administrators and business teams to describe the outcome they need and let the model generate the implementation.

But ServiceNow is no longer standing still on that front. Its current Build Agent similarly creates applications and code from natural-language prompts, supports flow design and testing, and operates inside ServiceNow’s governance framework. ServiceNow’s AI Agent Studio lets customers create agents and agentic workflows, while AI Agent Advisor is explicitly designed to analyze operational records for automation candidates.

The competitive question is therefore shifting from “who has generative AI?” to how many separate tools, configuration concepts and specialists are required to get from an observed operational problem to a production automation.

Serval is effectively arguing that Catalyst compresses those steps into one conversational surface and a smaller platform model. ServiceNow, by comparison, now has a powerful but broader set of AI and development surfaces spanning Build Agent, AI Agent Studio, AI Agent Advisor, Workflow Studio and AI Control Tower. That breadth is an advantage for customers already deeply invested in ServiceNow, but it also illustrates the complexity Serval is attacking. ServiceNow itself notes that Build Agent is aimed at admins and developers who understand and can support what it generates.

Atlassian is moving in the same direction from a different starting point. Rovo can generate “if this happens, then that happens” automation flows from natural-language descriptions, while Jira Service Management increasingly supports agents that triage, investigate and execute service work.

Freshworks’ Freddy AI Agent Studio likewise emphasizes agents that resolve requests end-to-end, with prebuilt IT and HR agents and more than 30 workflow templates.

Catalyst’s differentiator, then, is not that rivals cannot generate an automation from a sentence. It is Serval’s attempt to make the entire automation lifecycle itself agentic.

Building agents that look for trouble before a ticket exists

That approach becomes clearest with Serval’s background agents.

Rather than waiting for a help desk request, a background agent can run on a schedule across connected systems, correlate signals and draft a remediation. In one customer example provided by Serval, an agent correlated network incidents across two offices using switch telemetry, DHCP data and historical tickets, ruled out hardware and wireless interference, traced the issue to configuration drift, and generated a remediation workflow for an administrator to approve.

“Most AI agents today wait for an employee to ask a question or submit a ticket,” Stauch said. “We believe the future is AI that acts before an employee ever submits a request.”

That framing also highlights a philosophical difference in Serval’s pitch. The startup does not want service management to revolve around creating, routing and tracking better tickets. It wants the system to eliminate as many requests as possible by turning repeated support work into executable automation.

"A lot of the code written in enterprises has nothing to do with software engineering," Stauch explained. "It’s actually internal automations and other scripts for the company, and so we use that technology to build a better service management platform."

Serval's pitch to enterprises is that it can largely automate those scripts. And the governance model is critical because Catalyst can generate code and potentially initiate changes across production systems. Serval says Catalyst inherits the permissions of the user operating it and remains scoped to that user’s team workspace.

Everything it builds starts as a draft, and organizations can restrict publishing privileges or require formal review and approval before an automation becomes active.

Customer data remains customer-owned, with several deployment options

Those controls also extend to the enterprise data Catalyst examines. Stauch said Serval is intended to operate as the customer’s system of record and told VentureBeat that “they own all the data.”

Serval’s current Master Services Agreement is more precise: customers retain rights, title and interest in both their “Customer Materials” — a category that includes records, documents, workflows, prompts, inputs and configurations — and the output Serval generates from them. Serval receives the rights necessary to process that information to provide, maintain, support and secure the service.

Serval also says it does not retain or use customer materials, inputs or outputs to train, fine-tune or improve its own or third-party AI models.

Its Data Processing Addendum identifies Serval as the processor of customer personal data and allows processing for operating the service, responding to support requests, diagnosing issues and protecting the platform, while authorized subprocessors can also be involved. Serval’s acceptable-use terms say it maintains a current list of AI subprocessors and model providers for customers.

Where that data resides can vary by deployment. Stauch said customers can use Serval as a cloud SaaS service, run it on-premises or place it in their own VPC. Serval’s self-hosting documentation now describes two fuller options: a Serval-managed single-tenant deployment inside an AWS account owned by the customer, or a self-managed deployment on the customer’s Kubernetes cluster in any cloud or on-premises environment.

In the AWS option, Serval says it operates the installation without persistent IAM access to the customer’s AWS account.

There are therefore two distinct access boundaries for enterprise buyers to consider.

1. At the Catalyst level, the agent can only reach data, integrations and automations available to the user and team workspace under which it is operating.

2. At the platform level, Serval and authorized subprocessors necessarily process customer information to deliver and support the service, subject to the company’s contractual confidentiality and data-processing terms.

That makes Stauch’s informal statement that Serval “doesn’t touch” customer data better understood as an ownership and deployment claim, rather than a literal assertion that the service never processes it.

Ramp and other customers provide an early test

Customer deployments provide some evidence that the faster-build thesis can translate into operational changes, although the metrics come from Serval’s own case studies.

Corporate expense and financial technology firm Ramp says in a Serval case study that Catalyst has made workflow building 50% faster and helped extend Serval across roughly 10 teams, including IT, finance, facilities, people and talent, legal and business operations. In one hardware replacement program, Serval says Ramp automated 600 laptop replacements and saved 150 hours, leaving approval as the principal human step.

The more telling Catalyst example may be what happened afterward. Ramp had already automated laptop replacement when Catalyst suggested splitting its shipping logic into separate office and home workflows to reduce errors. The company also says employees outside IT now use Catalyst for analytics, bulk ticket operations, workflow troubleshooting and HR process automation.

Other Serval deployments show the broader operating environment Catalyst is meant to configure. Mercor says it has onboarded more than 4,000 external experts through Serval automations and expanded the platform across seven teams. Together AI says Serval automates 95% of its just-in-time infrastructure access requests, with approval and auditing controls around sensitive access. Perplexity says Serval automatically handles more than half of its incoming IT requests and all employee onboarding.

Those deployments extend beyond Catalyst itself, but they demonstrate the type of cross-system automation substrate Catalyst is now being asked to build and maintain.

Serval says more than 90% of customers adopted Catalyst as their starting point for automation during beta. Catalyst is generally available Aug. 20 and will be enabled by default for all Serval organizations.

Pricing and the battle with ServiceNow

Pricing is customized depending on the size of the deployment and is not publicly listed on Serval's website or documentation.

Serval describes a single platform fee and typically runs a pilot to determine expected deployment and usage.

Stauch said the software license can be similar to ServiceNow’s, but argues total cost of ownership can be substantially lower because customers require fewer implementation and maintenance services.

"The total cost of ownership is going to be dramatically less — usually half as much, sometimes 10 to 20% of the total cost of ownership of ServiceNow," Stauch said. "But the actual software license fee is not necessarily going to be all that different."

Serval's origin story and history

Serval was founded in 2024 by Stauch and CTO Alex McLeod, former Verkada product and engineering leaders, after they repeatedly heard IT customers complain about overburdened help desks and the limitations of established IT service-management software.

Serval has positioned itself as an AI-native alternative to platforms such as ServiceNow and Jira Service Management, combining help-desk ticketing, access management, asset management and workflow automation within a single system.

Serval and Sequoia Capital describe the company’s goal as moving IT software beyond merely recording and routing requests toward resolving them automatically.

The company can operate as an organization’s primary IT service-management system or add automation to an existing one. Its publicly identified customers include Perplexity, Mercor, Clay, Verkada and Together AI.

Serval says customers can automatically resolve more than half of their incoming IT requests; its Together AI case study reports automation of 95% of that customer’s just-in-time access requests.

Investor interest accelerated rapidly in late 2025. Serval announced a $47 million Series A led by Redpoint Ventures in October, bringing its funding at that point to $52 million.

In December, it raised another $75 million in a Sequoia-led Series B at a $1 billion valuation, lifting total capital raised to approximately $127 million; Redpoint, Meritech Capital and General Catalyst also participated.

Serval told Reuters that revenue had grown 500% since August 2025 and that it was expanding beyond IT into operational work performed by human resources, finance and legal departments.

The big test for enterprise customers

For enterprise buyers, Catalyst’s biggest test will be whether its compression of the automation lifecycle survives contact with large, messy, highly customized environments.

ServiceNow can now generate applications and discover automation opportunities with AI. Atlassian and Freshworks are adding increasingly capable agentic automation to their own service platforms. Serval therefore cannot rely on natural-language creation alone as its moat.

Its stronger wager is that an AI-native platform can make the administrative layer itself agentic: continuously finding repetitive work, building the necessary resources across the service stack, exposing generated code for review, and proposing the next automation before an administrator has opened a workflow designer.

If Catalyst works at that scope, the competitive unit is no longer the ticket — or even the workflow. It is the system that keeps turning an enterprise’s operational history into new automation.

Only this time, it's one that aims to solve a growing enterprise problem as AI agents proliferate: enabling greater developer control of agents and tools, while reducing cost.

TrueFoundry, a San Francisco B2B machine learning startup co-founded in 2021 by former Meta engineers, has released its own custom TrueForge harness under the permissive MIT License on Github. Thus, it can be used with any of a developer (or their parent enterprise's) preferred AI models, forked, modified, self-hosted and incorporated into commercial products.

The company states in a blog post that when it used TrueForge paired with the open source GLM-5.2 LLM to successfully complete 11 of 14 tasks on DevRev’s Enterprise-Bench — testing multi-step tool use across CRM, issue tracking, and document management systems — it cost 75% less than achieving the same results with Anthropic's Claude Managed Agents harness powered by Claude Opus 4.8 ($2.90 compared to $11.80).

Using the same model in each harness, Opus 4.8, TrueFoundry still claims a cost savings of roughly 30% using TrueForge compared to Claude Managed Agents ($8.50 vs $11.80).

Why is TrueFoundry giving this powerfully efficient harness away for free?

"We’ve had this ask from a bunch of customers," said Anuraag Gutgutia, TrueFoundry’s co-founder and COO, in an exclusive interview with VentureBeat. "You have an ability where you bring in agents and MCPs — can we also get something where you can actually launch these managed agents? I think that is the need we are satisfying. It is not a replacement. People will use this alongside other harnesses, like the cloud-managed ones or the commercial-provider-managed ones, but this will serve as a way for people to use them in a vendor-neutral way and also at a lower cost.”

Indeed, TrueFoundry already offers a paid "AI Gateway" for enterprises centrally controlling model and MCP access, credentials, permissions, budgets and observability. TrueForge, by contrast, handles what happens above that gateway: the loop that lets a model repeatedly reason, call tools, receive results and continue working until a task is complete.

For enterprise developers, the practical proposition is that they can start locally with a single command and SQLite, then move the same agent harness into a shared deployment using Docker Compose or Helm with Postgres and Redis.

TrueFoundry explicitly warns that the local configuration is intended only for use on a developer’s machine, not as an internet-facing production service.

Gutgutia said the company ultimately wants its AI Gateway to become the common layer beneath whichever agents and harnesses an enterprise chooses.

“There will be a set of companies that will use our harness as the way to launch managed agents,” he said, while others may continue using Claude, other open-source harnesses or internal systems. “But all that traffic should still be flowing through our gateway.”

Context management is where TrueForge tries to cut waste

TrueForge’s architecture centers on context engineering — controlling how much information gets sent back into the model on every step of an agent run.

That includes delaying the loading of MCP tool schemas until they are needed, delegating isolated tasks to subagents, moving oversized tool results into files instead of stuffing them into the active context window, processing structured results through code, and automatically compacting long-running conversations.

The documentation sets the default compaction threshold at 50,000 tokens, though it can be changed per agent.

TrueForge also treats the sandbox differently from runtimes that keep an agent inside an isolated environment throughout its run. The core agent loop remains on the TrueForge server; a sandbox is provisioned as a tool only when the agent needs to execute code or work with files. TrueFoundry says that reduces unnecessary compute and allows a server to run more agents concurrently.

The company argues those choices directly reduce model spending.

How TrueForge compares to Claude Managed Agents and other leading orchestration harnesses

Type / focus

• TrueFoundry TrueForge: General-purpose production agent harness designed for enterprise deployments.

• DeepSeek Harness: Open-source agent harness, currently positioned as a developer preview.

• OpenAI Codex CLI: Coding-focused agent harness designed primarily for software-engineering workflows.

• LangChain Deep Agents: General-purpose agent harness built on LangGraph.

• Anthropic Claude Managed Agents: Fully managed production agent runtime operated by Anthropic.

License

• TrueFoundry TrueForge: MIT.

• DeepSeek Harness: MIT.

• OpenAI Codex CLI: Apache 2.0.

• LangChain Deep Agents: MIT.

• Anthropic Claude Managed Agents: Proprietary.

Price

• TrueFoundry TrueForge: The open-source harness itself is free. Model, sandbox and infrastructure costs are separate. TrueFoundry also offers an optional commercial governance layer through its broader platform.

• DeepSeek Harness: No harness license fee. Users separately pay for whatever model providers and infrastructure they use.

• OpenAI Codex CLI: The CLI is open source. Underlying model/API or subscription costs are separate, OpenAI says around $100–$200 per developer per month, although actual spending varies substantially with model choice

• LangChain Deep Agents: Open source, with model and infrastructure expenses separate. LangChain also offers optional commercial services through LangSmith.

• Anthropic Claude Managed Agents: Claude tokens consumed plus $0.08 per running session-hour, with runtime metered to the millisecond.

Model flexibility

• TrueFoundry TrueForge: Vendor-neutral and designed around bring-your-own-model support.

• DeepSeek Harness: Multi-provider and not restricted to DeepSeek models.

• OpenAI Codex CLI: Supports configurable inference endpoints, including OpenAI-compatible services and local-model options.

• LangChain Deep Agents: Broad multi-provider support through the LangChain ecosystem.

• Anthropic Claude Managed Agents: Claude-centric.

Deployment

• TrueFoundry TrueForge: Can run locally as a single process with SQLite, then move into a production deployment using Docker Compose or Helm with Postgres and Redis.

• DeepSeek Harness: Designed for local or self-hosted operation.

• OpenAI Codex CLI: Primarily a local CLI experience, alongside OpenAI-hosted Codex products and services.

• LangChain Deep Agents: Can be self-hosted or deployed through LangChain and LangSmith infrastructure.

• Anthropic Claude Managed Agents: Anthropic manages the runtime and infrastructure.

Key features

• TrueFoundry TrueForge: MCP and tool orchestration, subagents, human approval checkpoints, persistent sessions, context compaction, large-result offloading, Code Mode, generative UI, tracing and a sandbox-as-a-tool architecture.

• DeepSeek Harness: Pluggable models, tools, session storage and agent loops, along with sandboxing, permissions, approval gates and skills.

• OpenAI Codex CLI: Agent loop, repository and file operations, shell execution, MCP tools, sandboxing, permissions, approvals and context management.

• LangChain Deep Agents: Planning, subagents, skills, filesystem-based context management, persistent memory, human-in-the-loop controls, MCP support and multiple sandbox backends.

• Anthropic Claude Managed Agents: Managed execution environments, persistence, tools, sandboxing and infrastructure for long-running agents.

Key differentiator

• TrueFoundry TrueForge: Its strongest distinction is the combination of an open-source, vendor-neutral harness with a clear path from local development to a shared production runtime, plus an optional enterprise governance plane through TrueFoundry.

• DeepSeek Harness: Emphasizes deep modularity. Major parts of the runtime, including models, tools, storage and the agent loop, are designed to be replaceable plugins.

• OpenAI Codex CLI: Stands out as a highly developed software-engineering-specific harness rather than a general-purpose enterprise agent server.

• LangChain Deep Agents: Benefits from the broader LangChain and LangGraph ecosystem and offers a mature open-source path for building general-purpose agents.

• Anthropic Claude Managed Agents: Minimizes operational burden by having Anthropic manage the runtime, but trades that convenience for tighter model and platform coupling.

Open source does not automatically mean governed

For enterprise buyers, one of the most important distinctions is between TrueForge by itself and TrueForge connected to TrueFoundry’s commercial AI Gateway.

The open-source harness can run independently. But it does not magically inherit an organization’s enterprise access policies on its own.

“If you are using just the open source version of our agent harness, yes, you will need to put the right controls therein or in front of some other internal control system,” Gutgutia told VentureBeat.

When paired with TrueFoundry’s gateway, the company says agents can inherit the identities and access controls already attached to models, MCP servers, tools, skills and other agents. Gutgutia described the gateway as the place where enterprise SSO, identity providers and granular permissions can be centrally enforced rather than reimplemented separately for every agent.

That distinction is likely to be important for platform engineering teams evaluating the project. TrueForge is free software; TrueFoundry’s governance layer is the commercial control plane around it.

TrueFoundry says NetApp was a beta user of the harness and contributed requirements during development. Gutgutia said NetApp’s IT organization has used the technology for incident response and faster ticket triage, while also exposing internal agents as self-service tools for developers. He also identified Automattic as an early user.

Background on TrueFoundry and its business to date

TrueFoundry was founded in 2021 to help enterprises deploy and operate machine-learning models, including Kubernetes-based model serving, training and infrastructure management.

Its three co-founders — Nikunj Bajaj, Abhishek Choudhary and Anuraag Gutgutia — previously worked at Meta and WorldQuant, respectively.

Gutgutia said the founders' common experience was working around mature systems where infrastructure and controls were designed to prevent costly mistakes — an idea they believed would become increasingly important as AI moved into production inside large companies.

As generative AI spread through enterprise software, TrueFoundry expanded from that MLOps foundation toward managing LLM applications and, increasingly, the models, tools and agents around them.

By 2025, the company had made its AI Gateway a central part of the business: a layer sitting between enterprise applications and model providers that handles routing, authentication, access controls, observability, budgets, guardrails and failover.

That evolution has been backed by roughly $21 million in outside financing. TrueFoundry raised a $19 million Series A in February 2025 led by Intel Capital, with participation from existing investors Eniac Ventures and Peak XV's Surge, as well as Jump Capital and angel investors including Gokul Rajaram and Mohit Aron. The round brought total financing to about $21 million, according to Intel Capital's announcement.

At the time, TrueFoundry said its customer base had grown fourfold year over year and that it was managing more than 1,000 clusters for machine-learning workloads.

The business has since become increasingly oriented around large-scale enterprise AI traffic. In VentureBeat's January 2026 coverage of TrueFoundry's TrueFailover launch, the company said it had more than 30 paid customers worldwide, had exceeded $1.5 million in annual recurring revenue during the prior year and was processing more than 10 billion requests per month through its AI Gateway.

Customers and deployments cited by TrueFoundry have included NetApp, Siemens Healthineers, ResMed, Automation Anywhere, Nvidia, Games24x7 and others; Gutgutia also named NetApp, Siemens, Synopsys and Automation Anywhere among Fortune 1000 organizations working with the company in his interview with VentureBeat.

TrueFoundry has also been expanding through acquisition. In June 2026 it acquired UK-based Seldon AI, a longtime MLOps vendor whose Seldon Core software has been used for production model serving and inference.

As the acquisition shows, rather than treating traditional ML, LLMs, tools and agents as separate infrastructure categories, TrueFoundry is trying to put them behind a common deployment and governance layer.

TrueForge extends that strategy upward into the agent runtime itself. Until now, TrueFoundry's commercial center of gravity has largely been the control plane underneath enterprise AI workloads — deciding which users and applications can access which models and tools, routing requests, enforcing policy, monitoring spend and keeping services available.

TrueForge gives the company an open-source runtime above that layer where agents can actually execute. Gutgutia described the relationship as complementary: organizations can run TrueForge independently or continue using other agent harnesses, while TrueFoundry's longer-term business opportunity is to provide the common governance and infrastructure underneath whichever agents enterprises choose.

The enterprise AI stack is being rewritten in real time, and the decision-makers I talk with are starved for objective, defendable data. Rob Strechay has the mix of technical rigor and operating experience needed to dissect the architecture behind the next phase of enterprise AI deployment.

The questions enterprise technology leaders are asking have changed. As organizations move past experimentation with generative AI toward production deployment, they want to know how to orchestrate multi-vendor environments, where the security gaps in their agentic pipelines sit, and how to fix the utilization problems draining their infrastructure budgets. Answering those questions requires more depth than news coverage alone provides, and that is the gap this research offering is built to fill.

An analyst who has sat on every side of the table

Strechay brings nearly three decades of experience as a practitioner, product executive, and industry analyst. Before becoming an analyst, he was an executive at numerous startups, including Zerto; he joined Amazon Web Services to help build a new analytics service; and he held executive roles across enterprise infrastructure. He later served as a senior analyst at Enterprise Strategy Group and most recently as managing director and principal analyst at theCUBE Research and SiliconANGLE, where he hosted executive interviews and analyzed the evolution of cloud, data, and AI infrastructure.

Strechay will initially focus his coverage on cloud infrastructure, advanced data infrastructure, platform engineering and DevOps orchestration and observability, and the intersection points where AI and enterprise security collide.

Already at work: GPU utilization and the VB Pulse surveys

Strechay has already been contributing to VentureBeat's research. In May he published an analysis of enterprise GPU utilization, examining the compute waste sitting inside enterprise AI infrastructure, and he provided a substantive review of our AI Infrastructure & Compute survey before it went into the field.

His infrastructure-level focus complements the research engine VentureBeat has built around its monthly VB Pulse surveys, which track five areas of enterprise AI adoption: agentic orchestration, agent reliability and evals, agentic security and identity, AI infrastructure and compute, and context layers, including retrieval-augmented generation (RAG). Our June report on agentic orchestration, drawn from a survey of 145 enterprises, found that two-thirds of those enterprises had hedged their AI model strategy rather than committing to a single provider — a posture whose value the June outage of Anthropic's Claude models made plain.

VB In Conversation: The first vehicle

A core vehicle for this expanded research footprint will be a deepening of VentureBeat's existing VB In Conversation video interview series, which Strechay will host. Rather than high-level industry overviews, the series will bring architectural blueprints, actual deployment barriers, and back-end infrastructure realities to light through in-depth technical interviews with the architects and product leaders behind leading enterprise AI systems — an unvarnished look at which tools perform under production-grade pressure.

"VentureBeat has built an audience of enterprise builders and technology buyers that any analyst would want to serve," Strechay said. "My goal is to use deep empirical metrics and VentureBeat's proprietary tracking data to help enterprise buyers and the people building for them make sound platform and infrastructure decisions during the most disruptive transition enterprise technology has seen."

The expanded VB In Conversation series will appear on VentureBeat and on VentureBeat's YouTube channel, alongside Rob's written analysis on the site. Enterprise practitioners who want to take part in our monthly VB Pulse surveys, or arrange an analyst briefing with Rob, can reach the research team here.

Developers who previously subscribed to a GLM Coding Plan are currently limited to the OpenAI Chat Completions-compatible protocol. Z.ai said it plans to make the model's weights openly available, but a precise date and licensing remain to be seen.

On the API, the price is unchanged from GLM-5.2: $1.40 per million input tokens and $4.40 per million output tokens. Cached input costs $0.26 per million tokens, while Z.ai currently lists cached-input storage as free for a limited time.

That means developers can move to the new generation without taking a higher posted per-token rate from Z.ai, even as the company claims substantially stronger coding and long-horizon agent performance. At those rates, GLM-5.3 sits well below several of the highest-end frontier APIs.

Using the simple VentureBeat comparison of one million input tokens plus one million output tokens, GLM-5.3 comes to $5.80, versus $8 for Grok 4.6 at its lower context rate, $18 for Kimi K3, $30 for Claude Opus 5 and $35 for GPT-5.6 Sol.

That is not a workload-cost estimate — real bills depend heavily on the input/output mix, caching and token consumption — but it makes the relative API price tier easy to see.

GLM-5.3 is not the cheapest capable model available. Google’s current introductory price for Gemini 3.7 Flash is $0.75 per million input tokens and $3.75 per million output tokens through Dec. 31, 2026, while OpenAI’s GPT-5.6 Luna is priced at $0.20 input and $1.20 output.

Still, Z.ai’s price puts GLM-5.3 into a notably lower cost band than the premium frontier models it is increasingly benchmarked against.

That comparison has become more relevant following the latest independent results. Artificial Analysis gives GLM-5.3 a score of 60 on its Intelligence Index, tying Kimi K3 as the top performing open weights model in the world, and scoring seven points higher than GLM-5.2.

Its analysis also estimates GLM-5.3 at about $0.68 per Intelligence Index task, versus roughly $0.44 for GLM-5.2, despite the identical API token prices.

The difference underscores an important caveat in headline API pricing: Artificial Analysis found GLM-5.3 more verbose than its predecessor, so flat per-token rates do not necessarily mean flat costs for a completed workload.

For developers, though, the immediate change is straightforward: GLM-5.3 is now callable through Z.ai’s API at the same $1.40/$4.40 per-million-token rate as GLM-5.2, giving teams another relatively low-cost option for testing frontier-class coding and agent workloads.