AI coding tools already generate new code faster than enterprise teams can evaluate it.
Every new application and code change still needs monitoring, incident response, security fixes and release checks, which is exactly why Autoheal, a new San Francisco startup, has developed a platform specifically to account for all of this additional labor.
Autoheal says teams can use the platform for incident investigation, vulnerability remediation, release preparation, support escalations and managing the costs of AI coding. It offers prebuilt agents as well as tools for teams to create their own.
Autoheal calls the product a "self-improving software factory," according to its website.
The phrase describes a system that gives multiple specialized agents access to the same engineering context, evaluates what they accomplish and proposes changes when their performance slips.
The company today announced a $7.9 million seed round led by Innovation Endeavors alongside the general availability of its platform.
The firm's website directs prospects to book a demo and describes a three-week evaluation led by an embedded engineer: scope the desired outcomes, connect the customer’s systems, run agents against live work, and review results before deciding whether to proceed.
Connecting disparate coding agents to reduce fragmentation
The practical problem is fragmentation. A production failure may require an engineer to piece together monitoring alerts, recent code changes, cloud activity, deployment records and internal documentation. An agent that sees only one of those sources can produce a plausible but incomplete diagnosis.
Autoheal connects coding agents, repositories, build and deployment tools, monitoring systems, cloud environments and issue trackers into a shared context layer. Its agents can then work across those sources under an organization’s access rules, the company says.
The incident-response page shows what this would look like in practice. Autoheal says it groups alerts with conventional rules before assigning investigation to an agent. The agent correlates logs, traces, recent deployments and code differences, then posts evidence and a proposed root cause to the incident channel. For lower-severity incidents, the company says, it can test a fix in a sandbox and open a pull request for the owning team; engineers retain control of the merge. It can also draft a postmortem from the incident timeline. These are described product behaviors, not independently observed results for every deployment.
“Our experience taught us that while building the first version of an AI agent is easy, scaling it consistently across the enterprise SDLC is the real challenge,” co-founder and CEO Sid Choudhury said in a written press release shared with VentureBeat.
Autoheal’s answer is to give platform teams a common way to deploy, govern and improve agents across different engineering groups.
Agents that review other agents
The system’s distinguishing feature is its proposed feedback loop.
An Evaluator agent scores the work of other agents, using signals such as code review comments, failed build checks and production incidents to assess the output of a coding agent.
A Healer agent can then open a pull request to change an underperforming agent’s instructions, skills, tools or choice of model. It checks proposed changes against historical tests before an engineer reviews them. According to Autoheal, behavioral changes are tracked in Git and require human approval.
Autoheal’s product page describes two parts to that loop: updating an agent’s instructions and tools, and improving the shared context it draws on. A human correction during an incident, for example, can become information for later runs.
The page also says teams can set budgets and confidence thresholds per agent, replay and compare runs, and inspect cost, latency and accuracy by agent and team. Agents can be invoked through a command-line tool, API or webhook, MCP, Slack or Microsoft Teams.
That setup could help with a common maintenance problem: an agent that works well for one team may falter as applications, tools and organizational rules change.
It also puts a testable question at the center of Autoheal’s pitch. Can the platform show that its proposed changes improve outcomes across teams without introducing new failures? The company describes private evaluations and regression checks, but the materials supplied to VentureBeat do not include an independent benchmark or comparative results for that feedback loop.
Autoheal says the platform provides secure sandboxes, audit trails, cost controls and model routing. Choudhury said in a written company backgrounder that it can direct high-volume tasks to less expensive open-weight models while reserving costlier frontier models for more complex orchestration.
Customers that bring their own model API keys receive those routing savings, he said. The company’s commercial model is primarily consumption-based and tied to agent use; it did not provide a rate card or a typical customer bill.
The coding-cost page adds a more specific workflow: read execution traces from a team’s existing coding agents, turn actual coding sessions into evaluation tasks, compare models and settings on those tasks, then propose a skill or configuration change through a pull request.
It illustrates a 30% cost-per-task reduction from lowering the model’s effort setting and a further 10% from assigning routine work to a smaller companion model.
The page does not identify the underlying workload, sample or customer for those figures, so they should be treated as an example of the approach, not a measured saving buyers can expect.
The product is also designed to work with existing coding agents. Choudhury named Claude Code, Codex and GitHub Copilot among the outside tools whose workflows Autoheal aims to evaluate and improve. He positioned Factory.ai and Cognition’s Devin as competitors, while arguing that coding agents alone do not cover the repeated, cross-team operational work his company targets.
Those comparisons reflect Autoheal’s view; VentureBeat has not independently tested the products against one another.
The website additionally names Cursor and custom agents as possible inputs. Autoheal says it supports SaaS, hybrid and isolated deployments in a customer’s own cloud, using approved models. It describes policy-limited access, temporary credentials and logs of agent actions.
The site displays ISO 27001, SOC 2 Type II and zero-data-retention labels, but the provided materials do not establish the scope or independent status of those assurances; an enterprise buyer would need to review the underlying documentation.
The pricing question
Autoheal advertises fast agent setup, but that is distinct from deploying and evaluating the platform across an enterprise. The site does not list self-service pricing or a standard trial plan.
Asked directly about specific costs and pricing plans, a spokesperson said the company charges in "dollars per agent session."
An administrator at the customer company controls a budget for each session. But each session's total budget can be used by in different ways — for example, a complex production incident response could consume $20, while a simple vulnerability fix could cost $2.
The range matters for enterprise buyers: the examples differ tenfold, so the number and complexity of sessions will affect spending even if administrators set budgets.
Autoheal has not specified how a session starts and ends, what happens if an agent reaches its budget before finishing, whether unsuccessful or repeated attempts are charged, or whether charges for the underlying models are included.
It also has not disclosed minimum commitments, volume discounts, platform or support fees, or the cost of its three-week evaluation. Those details would be needed to forecast a monthly bill or compare the service with an in-house system.
Autoheal says its routing can steer high-volume work toward cheaper open-weight models and reserve frontier models for harder orchestration. In deployments where customers bring their own model API keys, the company says it passes on those routing savings at no additional charge. That addresses one part of the operating cost, but does not establish a guaranteed net saving after Autoheal’s per-session charges.
The website’s separate 30% cost-per-task illustration concerns tuning an existing coding agent; it is not a discount on Autoheal’s fees.
What customers say
Autoheal supplied named customer examples that make its proposed utility more concrete. It says Nomura reduced its average incident resolution time from two hours to 15 minutes by using the platform to examine information across monitoring, code, cloud systems, deployment pipelines and knowledge bases. Sameer Jain, Nomura’s CIO for wholesale, said in a statement supplied by Autoheal that the platform takes investigations “from hours to minutes” and runs within the bank’s own cloud controls. The company did not provide the period, sample size or methodology behind the two-hour-to-15-minute figure.
AvidXchange uses Autoheal for incident response, release-readiness reviews and onboarding new engineers, according to the backgrounder. Autoheal says that work saves thousands of engineering hours per month. AvidXchange CTO and senior vice president Krish Shetty said the incident tool gets engineers to a likely root cause in minutes. These are company-provided customer accounts, rather than independently audited measurements.
The use cases extend beyond production incidents. Autoheal says Nauto, which operates a fleet safety platform, uses it to connect device logs, warehouse data, recent releases and internal issue records when customers report a problem. The company claims Nauto’s on-call engineers close those issues 50% faster. In a separate statement supplied by Autoheal, Oscilar vice president Joby Babu described an agent that pulls together information from Grafana, Slack, ClickHouse, product documents and Pylon to triage support tickets. Empiric Earth also supplied a statement about troubleshooting and monitoring costs. Autoheal did not include independently verifiable baselines for those examples.
That distinction matters because the product spans several different kinds of work. A faster first diagnosis does not necessarily mean a faster complete fix, and saved engineering time depends on how teams count work before and after deployment. Prospective buyers will want to see task-level results, the cost per successful task and how often engineers reject an agent’s recommendation.
The public site also presents before-and-after illustrations for change lead time, incident volume and security remediation. It does not label those illustrations as measured customer outcomes. They should therefore be read separately from the named customer statements and the company’s more specific Nomura claim.
Funding, rollout and what comes next
Innovation Endeavors led the seed round, and its Harpinder Singh joined Autoheal’s board. The company also listed Emergent Ventures, U&I Ventures, Darkmode Ventures, Batch Ventures and Param Hansa Values as investors. Choudhury said Autoheal has 13 engineers across Silicon Valley and Bengaluru. It began selling the platform three months ago and expects revenue to reach seven figures by year-end, according to his written responses; the company did not disclose current annual recurring revenue, margins or cash flow.
The company’s about page identifies Choudhury as a former Harness and Yugabyte executive, CTO Utkarsh Ohm as a former AI and machine-learning engineering leader at ThoughtSpot, and chief development officer Puneet Saraswat as a former Harness and Microsoft engineering leader.
The longer-term plan is to train smaller models on each customer’s private engineering data, using feedback from repeated tasks such as incident analysis. Autoheal argues that these models could eventually reduce operating costs and improve performance on organization-specific work.
That training capability is a stated direction, not a demonstrated result in the materials provided. The current product page also displays a “Zero model training” label in its deployment section without explaining its scope or how it relates to the future plan for customer-specific models. The company also sees potential applications in security, data, support and sales engineering.
For now, the immediate proposition is narrower and easier to judge: whether an engineering team can give agents the right context, keep their actions governed and improve their performance as its systems change. Autoheal’s general availability push gives enterprises a chance to test that proposition against the real work piling up after code is written.