The funding will be used to expand engineering and accelerate the company’s deployment.
ChatSee.ai, which provides a fault intelligence layer for autonomous AI systems, announced a $6.5 million funding round led by True Ventures with participation from First Rays Venture Partners, Seven Hills Ventures, and industry veterans.
Autonomous AI agents are rapidly moving from experimentation to production within the enterprise. Custom agents built on models from OpenAI, Gemini, and Anthropic, along with agents built into platforms like Microsoft 365 Copilot, Salesforce Agentforce, Snowflake, and the Databricks Agent platform, are increasingly powering customer interactions, operational workflows, analytics, and enterprise decision-making systems. At the same time, developers are building increasingly complex autonomous multi-agent systems using frameworks such as LangChain, Microsoft AutoGen, and emerging open projects such as OpenClaw.
But as these systems move into production, new trust gaps are emerging. Agents that appear competent during testing often experience repeated operational failures when deployed in a live environment. Unlike traditional software failures, many AI failures depend on context, intent, policy interpretation, and business outcomes, making them difficult to detect using static rules or traditional monitoring alone.
Observability tools help humans examine the interactions of individual agents, but they do not preserve the failure intelligence necessary for systems to learn from repeated mistakes.
Businesses need a way to understand the context surrounding operational failures, understand how the problem was fixed, and determine whether similar problems continue to occur. Without this organizational memory, agents cannot effectively learn from previous mistakes, causing the same mistakes to reoccur across interactions, workflows, and business processes, from missed escalation triggers and unintentional disclosures to incorrect policy decisions, tool misuse, workflow drift, and entire operational process breakdowns over time.
“Many of the most significant AI risks emerge at runtime, as agents operate autonomously,” said Dr. Eduard Amoroso, CEO of TAG-infosphere (former CISO of AT&T). “These systems are probabilistic and adaptive, so static testing alone is not enough. This drives the need for continuous execution time guarantees across enterprise workflows using platforms like ChatSee, which allow organizations to observe and improve AI behavior over time.”
Also read: AiThority interview with Matej Bukovinski, Chief Technology Officer, Nutrient
Gartner Industry Analyst® identified the need for a new control plane, called the Guardian Agent, focused on monitoring and protecting these systems. ChatSee was recently featured in the Business Collaboration and Performance Optimization category of the Guardian Agent Market Guide for Guardian Agents. We believe this highlights the growing need for technology that monitors the behavior of production AI agents and aligns them with business outcomes.
ChatSee was co-founded by Sekhar Sarukkai, a serial entrepreneur who co-founded Skyhigh Networks (acquired by McAfee), Securent (acquired by Cisco), and Confluent Software (acquired by Oracle). He will be joined by co-founder Dr. Sanjay Agrawal (Stanford University), whose research and engineering work focuses on large-scale distributed systems and enterprise AI infrastructure.
“When we started analyzing agent failures, we found that although the problem appeared chaotic, it actually fell into a repeatable pattern,” Sarukkai says. “That’s where observability falls short. It tells you what happened, but it doesn’t tell you whether the behavior was actually correct. We found that these failures fall into repeatable patterns that can be classified, remediated, and continuously fed back into both human and AI workflows. This allows the system to learn and improve over time. This allows the AI to operations will move from humans simply monitoring agents to humans and agents working together to improve outcomes, and reactive monitoring will become continuous, managed AI operations at scale.”
Taking a first-principles approach, ChatSee introduces a failure intelligence layer to enterprise AI systems. While observability platforms help teams monitor agent behavior, ChatSee focuses on understanding behavioral failures, preserving surrounding context, gaining knowledge for remediation, and tracking relapses over time.
As a result, memories of failure are shared and the organization’s record of what went wrong, why it went wrong, how it was fixed, and whether it has happened again continues to grow. This allows companies to not only investigate failures one interaction at a time, but also to continually improve the behavior of AI systems in production.
“AI agents are rapidly becoming operational infrastructure within enterprises,” said Puneet Agarwal, Partner at True Ventures. “However, enterprises still lack the tools to understand when these agents misbehave in production and how to fix those failures at scale. ChatSee addresses this critical gap in the emerging AI stack.”
