There is a category of production incident that engineering teams are not tracking yet — because it doesn't fit any existing postmortem template.

The centerpiece of the release is a new multi-agent investigation system developed by Resolve AI's in-house research lab. Instead of deploying a single AI agent to diagnose a production failure — analogous to a lone engineer pulling an on-call shift — the platform now dispatches a coordinated team of specialized agents that pursue multiple hypotheses in parallel, independently verify each other's conclusions, and construct complete causal chains from root cause to symptom. The company says the architecture delivers more than a twofold improvement in root cause accuracy on its internal evaluation benchmarks compared to earlier versions of its platform.

Less than a week after completing the largest tech IPO of 2026, Cerebras Systems is making its most aggressive play yet to dominate the fast-growing AI inference market. On Monday, the Sunnyvale-based chipmaker announced that it is now running Kimi K2.6 — a trillion-parameter open-weight model developed by Beijing-based Moonshot AI — for enterprise customers at nearly 1,000 tokens per second, a speed no GPU-based provider has come close to matching.

For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor, a few typed words, and a list of blue links. On Tuesday, Google will formally retire that paradigm.

Google on Tuesday unveiled Gemini Spark, a personal AI agent designed to work around the clock — drafting emails, assembling documents, monitoring inboxes, and eventually making purchases — even when a user's laptop is closed and their phone is locked.

Over the past two decades, technical debt meant outdated architecture, messy code, and poorly maintained documentation. That definition is no longer sufficient in the AI era, where failure modes are more subtle and often non-linear. AI systems are introducing new layers of technical debt that live across prompts, models, and data dependencies — making these layers less visible, harder to measure, and often more dangerous than traditional debt.

The platform arrives at a moment when every major technology vendor — from Microsoft and Salesforce to Google and ServiceNow — is racing to become the default infrastructure for enterprise AI agents. Kore.ai's answer to that crowded field is a bet on neutrality, a proprietary intermediary language for defining agents, and a philosophy that AI, not human developers, should do most of the heavy lifting.

The centerpiece of the release is a new multi-agent investigation system developed by Resolve AI's in-house research lab. Instead of deploying a single AI agent to diagnose a production failure — analogous to a lone engineer pulling an on-call shift — the platform now dispatches a coordinated team of specialized agents that pursue multiple hypotheses in parallel, independently verify each other's conclusions, and construct complete causal chains from root cause to symptom. The company says the architecture delivers more than a twofold improvement in root cause accuracy on its internal evaluation benchmarks compared to earlier versions of its platform.