Codenotary, leaders in assuring safe and secure use of AI use, today announced that its AgentMon AI runtime observability platform is now monitoring more than 3 million AI-agent interactions per day across enterprise customer environments, underscoring the rapid operationalization of agentic AI systems. The milestone also revealed that approximately 7% of all monitored AI-agent interactions triggered security, compliance, or operational anomaly detections, representing roughly 210,000 potentially unsafe or non-compliant AI events daily.
AgentMon monitors more than 3 million AI-agent interactions per day across enterprise environments.
Approximately 7% of interactions trigger anomaly detections: roughly 210,000 daily unsafe or non-compliant events.
Observed risks include exposure of passwords, API tokens, financial records, and healthcare data.
Other risks include actions outside operational boundaries, recursive workflows, and excessive token consumption.
Prompt injection attempts and context poisoning indicators were also detected.
Most anomalies originated from unsafe AI behavior inside legitimate enterprise workflows.
The findings reinforce a growing industry reality: enterprise AI systems are introducing a new category of runtime risk that traditional cybersecurity and observability platforms were not designed to detect.
"The emergence of large-scale AI runtime telemetry marks an important milestone in enterprise AI adoption," said Dan Twing, president and chief operating officer, Enterprise Management Associates (EMA). "The challenge with autonomous systems is not simply whether they execute. It is whether they interpret state correctly, operate within established guardrails, and produce the intended outcome. Telemetry of this kind provides important visibility into a problem that enterprises will increasingly need to govern as AI moves deeper into production operations."
"Organizations are rapidly moving from isolated AI experiments to highly interconnected AI ecosystems operating across infrastructure, business systems, APIs, applications, and operational workflows," said Moshe Bar, CEO and co-founder of Codenotary. "What we are observing at scale is that AI runtime behavior itself has become a new operational and security layer that enterprises must continuously monitor, govern, and enforce."
AgentMon provides runtime observability for AI agents, autonomous workflows, and agentic infrastructure by continuously monitoring interactions between AI systems, tools, APIs, infrastructure, and enterprise data environments. The platform identifies unsafe, anomalous, or policy-violating AI behavior in real time.
According to telemetry collected by AgentMon, the majority of detected anomalies were not associated with traditional malware or external attacks. Instead, most originated from unsafe or unexpected AI behavior occurring inside legitimate enterprise workflows.
Observed runtime risks included:
Exposure of sensitive information such as passwords, API tokens, cryptographic material, financial records, healthcare data, and confidential internal documents;
AI agents attempting actions outside approved operational boundaries;
Interactions with unauthorized external services or restricted enterprise systems;
Violations of internal governance controls or industry compliance policies;
Recursive workflows and runaway task execution;
Excessive token consumption and abnormal retry behavior;
Prompt injection attempts and context poisoning indicators;
Unsafe external tool usage and anomalous access patterns.
As enterprises deploy thousands of AI-assisted workflows across departments including finance, customer support, infrastructure operations, legal, manufacturing, and internal knowledge systems, even a relatively small percentage of unsafe behavior can rapidly scale into material operational, financial, or regulatory risk.
Traditional security and observability platforms primarily focus on endpoints, networks, identities, and applications. Agentic AI systems introduce an entirely new execution layer — one driven by autonomy, orchestration logic, context sharing, tool invocation, and machine decision-making behavior.
"Runtime governance for AI systems is quickly becoming foundational enterprise infrastructure," Bar said. "The organizations succeeding with AI adoption are not the ones slowing deployment. They are the ones building visibility, telemetry correlation, policy enforcement, and operational governance directly into their AI runtime environments."
The company said the milestone reflects broader acceleration in enterprise AI adoption, particularly as organizations increasingly deploy autonomous agents and AI-assisted operational systems into production.
AgentMon is part of Codenotary's broader portfolio focused on runtime trust, software supply chain integrity, AI observability, and autonomous infrastructure governance.
About Codenotary
Used by hundreds of customers worldwide – including the world's leading banks, governments, and defense organizations – Codenotary delivers technology that enables secure and trusted agentic networks in the modern organization.
