{"id":72498,"date":"2026-06-13T04:24:55","date_gmt":"2026-06-13T04:24:55","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/72498\/"},"modified":"2026-06-13T04:24:55","modified_gmt":"2026-06-13T04:24:55","slug":"chatsee-ai-raises-6-5m-led-by-true-ventures-to-tackle-the-growing-problem-of-ai-agent-failures-2","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/72498\/","title":{"rendered":"ChatSee.ai Raises $6.5M led by True Ventures to Tackle the Growing Problem of AI Agent Failures"},"content":{"rendered":"

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\n The funding will be used to expand engineering and accelerate enterprise deployments<\/p>\n

ChatSee.ai<\/a>, which provides the failure 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.<\/p>\n

Autonomous AI agents are rapidly moving from experiments to production in enterprises. Custom agents built on top of models from OpenAI, Gemini, and Anthropic\u2014along with embedded agents in platforms such as Microsoft 365 Copilot, Salesforce Agentforce, Snowflake and Databricks Agent platforms\u2014are increasingly powering customer interactions, operational workflows, analytics, and enterprise decisioning 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 like OpenClaw.<\/p>\n

But as these systems move into production, a new confidence gap is emerging: agents that appear capable during testing often exhibit recurring behavioral failures once deployed into real-world environments. Unlike traditional software failures, many AI failures depend on context, intent, policy interpretation, and business outcomes, making them difficult to detect through static rules or conventional monitoring alone.<\/p>\n

Observability tools help humans investigate individual agent interactions, but they do not preserve the failure intelligence needed for systems to learn from recurring mistakes.<\/p>\n

Enterprises need a way to capture the context surrounding behavioral failures, understand how they were remediated, and determine whether similar issues continue to recur. Without this organizational memory, agents cannot effectively learn from prior failures, causing the same mistakes to recur across interactions, workflows, and business processes\u2014from missed escalation triggers and unintended disclosures to incorrect policy decisions, tool misuse, workflow drift, and breakdowns across long-running operational processes.<\/p>\n

\u201cMany of the most significant AI risks emerge at runtime as agents operate autonomously,\u201d said Dr. Eduard Amoroso, CEO of TAG-infosphere (and former CISO of AT&T). \u201cBecause these systems are probabilistic and adaptive, static testing alone is insufficient. This is driving the need for continuous runtime assurance across enterprise workflows, with platforms like ChatSee helping organizations observe and improve AI behavior over time.\u201d<\/p>\n

Also Read:\u00a0AiThority Interview with Matej Bukovinski, Chief Technology Officer at Nutrient<\/a><\/p>\n

Industry analysts at Gartner\u00ae\u00a0have identified the need for a new control plane, called Guardian Agents, focused on observing and protecting these systems. ChatSee was recently included in the Gartner Market Guide for Guardian Agents in the business alignment and outcome optimization category, which we believe highlighted the growing need for technologies that monitor and align the behavior of production AI agents with business outcomes.<\/p>\n

ChatSee is co-founded by serial entrepreneur Sekhar Sarukkai, who co-founded Skyhigh Networks (acquired by McAfee), Securent (acquired by Cisco), and Confluent Software (acquired by Oracle). He is joined by co-founder Sanjay Agrawal, PhD (Stanford), whose research and engineering work has focused on large-scale distributed systems and enterprise AI infrastructure.<\/p>\n

\u201cWhen we started analyzing agent failures, we realized the problems seem chaotic but actually fall into repeatable patterns,\u201d said Sarukkai. \u201cThat\u2019s where observability falls short\u2014it shows what happened, but not whether the behavior was actually correct. We\u2019re discovering that these failures fall into repeatable patterns that can be classified, remediated, and continuously fed back into both human and AI workflows so systems learn and improve over time. This shifts AI operations from humans merely supervising agents to humans and agents collaboratively improving outcomes, turning reactive oversight into continuous, governed AI operations at scale.\u201d<\/p>\n

Taking a first-principles approach, ChatSee introduces a failure intelligence layer for enterprise AI systems. While observability platforms help teams monitor what agents do, ChatSee focuses on understanding behavioral failures, preserving the context surrounding them, capturing remediation knowledge, and tracking recurrence over time.<\/p>\n

The result is a shared failure memory\u2014a continuously growing organizational record of what failed, why it failed, how it was fixed, and whether it happened again. This allows enterprises to move beyond investigating failures one interaction at a time and continuously improve how AI systems behave in production.<\/p>\n

\u201cAI agents are quickly becoming operational infrastructure inside enterprises,\u201d said Puneet Agarwal, Partner at True Ventures. \u201cBut companies still lack tools to understand when those agents behave incorrectly in production and how to correct these failures at scale. ChatSee is addressing this critical gap in the emerging AI stack.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"The funding will be used to expand engineering and accelerate enterprise deployments ChatSee.ai, which provides the failure intelligence…\n","protected":false},"author":2,"featured_media":72499,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[405,7537,6623,872,39927,40023,40024,40025,8172,33997,7899],"class_list":["post-72498","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-ai-agents","tag-artificial-intelligence-agents","tag-autonomous-ai-systems","tag-ceo","tag-chatsee-ai","tag-dr-eduard-amoroso","tag-failure-intelligence-layer","tag-first-rays-venture-partners","tag-microsoft-365-copilot","tag-salesforce-agentforce","tag-snowflake"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/72498","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=72498"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/72498\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/72499"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=72498"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=72498"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=72498"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}