ChatSee.AI Inc. raised $6.5 million in seed funding Thursday to build a failure intelligence layer for autonomous AI systems. True Ventures led the round, addressing a growing operational challenge as companies move AI agents from controlled pilots into production environments with real customers.
Enterprise teams are already adopting AI agents through platforms from Microsoft, Databricks, Snowflake, Workday, OpenAI, and Anthropic, alongside various open-source projects. As these systems handle core operations, the challenge shifts from testing in simulation to managing real-world execution. "They all realize that it's a nondeterministic infrastructure, and they cannot test their way out of failures," co-founder and Chief Executive Sekhar Sarukkai said.
Building a failure memory
ChatSee aims to close the confidence gap by observing when AI Agents & Automation fail, preserving the context, and recording how human operators fixed the problem. The system then feeds this knowledge back into the platform so future agent actions can avoid repeating the same mistake. This approach moves beyond basic observability to provide self-learning and adaptivity at scale.
The company built its taxonomy by collecting more than 10,000 grounded examples of enterprise agent failures. It classifies these into 157 categories, covering tool-call failures and breakdowns across scoping, reasoning, and execution phases. This broadens the focus from merely monitoring for hallucinations to catching equally subtle operational errors.
Correcting errors at scale
Business teams increasingly use agents to drive long-horizon activities in sectors like e-commerce and financial services. Tasks include catalog validation, pricing, transaction labeling, and merchant code classification. "These are not classic conversational support kind of agents," Sarukkai said. "These are really supporting core business."
If an agent makes a subtle error, such as misclassifying a merchant code, a human correction must propagate across the entire system. ChatSee functions as a centralized failure knowledge base that agents reference at the platform level. When an agent fails a tool call or breaks an API pattern, it self-corrects, and critical fixes are written to a central authority for other agents to adopt as best practices. "Intelligence is not lost," Sarukkai said.
Runtime assurance over static testing
Governance tooling is still catching up to the volume of deployed agents. While startups like Voker and Respan focus on performance tracking and root-cause analysis, ChatSee positions itself specifically as a memory layer for recurring failures. For leaders overseeing these deployments, AI for Management requires shifting focus from pre-deployment testing to continuous runtime assurance.
"Many of the most significant AI risks emerge at runtime as agents operate autonomously," said Dr. Eduard Amoroso, CEO of research and advisory firm TAG-infosphere Inc. "Because these systems are probabilistic and adaptive, static testing alone is insufficient. This is driving the need for continuous runtime assurance across enterprise workflows."
Why this matters for management
Managers deploying autonomous agents must assume failures will occur in production. Investing in systems that capture, centralize, and distribute human corrections prevents isolated errors from scaling into systemic operational risks.
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