When a human employee accesses files they should not, sends data to an unauthorized endpoint, or racks up unexpected cloud charges, organizations have mature systems to detect and respond. But when an AI agent does the same thing — and industry analysts predict 40 percent of enterprise applications will use task-specific AI agents by the end of 2026 — most organizations are flying blind. Codenotary has launched AgentMon to address this gap, and the product highlights a truth the industry has been slow to acknowledge: deploying AI agents without monitoring their behavior is operational negligence.
The AI Agent Monitoring Problem
Traditional application performance monitoring (APM) tools were designed for a world of deterministic software. You instrument your code, trace requests through microservices, measure latency, and alert on error rates. The behavior of a well-tested application is predictable — the same input produces the same output, and deviations indicate bugs.
AI agents break this model completely. Their behavior is non-deterministic by design. The same prompt can produce different actions. Agents that interact with external tools — file systems, databases, APIs, web browsers — can take paths that no developer anticipated. An agent tasked with summarizing customer feedback might decide to access the billing database for additional context. An agent writing code might install an npm package that contains a known vulnerability. An agent managing cloud resources might provision expensive GPU instances because its cost optimization reasoning hallucinated a discount that does not exist.
These are not theoretical concerns. Organizations running AI agents in production are already encountering data leaks where agents include sensitive information in outputs sent to third-party APIs, cost overruns from agents making resource allocation decisions without budget awareness, and security policy violations where agents bypass access controls by chaining tool calls in unexpected ways.
What AgentMon Does
Codenotary AgentMon sits between AI agents and the resources they access, monitoring three primary dimensions: behavior patterns, file and data access, and resource consumption. The platform uses its own AI models to establish behavioral baselines for each agent, then flags deviations that may indicate problems — an agent that typically reads three files per task suddenly reading thirty, an agent that normally generates text suddenly making API calls to external services, or an agent whose token consumption spikes by an order of magnitude.
The file access monitoring is particularly relevant for enterprises concerned about data governance. AgentMon tracks which files, databases, and API endpoints each agent accesses, creating an audit trail that maps to existing data classification policies. If an agent accesses files classified as containing PII or financial data, security teams receive alerts and can review whether the access was appropriate for the task the agent was performing.
Resource consumption tracking addresses the cost dimension. AI agents that make multiple LLM calls per task, each consuming thousands of tokens, can generate surprising cloud bills. AgentMon provides per-agent and per-task cost attribution, enabling organizations to identify inefficient agent behaviors and set budget guardrails that halt agent execution when spending thresholds are reached.
Beyond Traditional APM
The emergence of AgentMon and similar tools — several startups and open-source projects are tackling adjacent problems — signals the birth of a new infrastructure category: AI agent observability. This is distinct from traditional APM in several important ways. First, the unit of observation is not a request or transaction but a task, which may span multiple LLM calls, tool invocations, and decision points over minutes or hours. Second, the definition of correct behavior is probabilistic, not deterministic — you cannot simply assert that the output should equal a specific value. Third, the security model must account for agents that autonomously decide which resources to access, rather than following hardcoded paths.
For security teams, AI agent observability fills a critical gap. Traditional SIEM and SOAR platforms can detect unauthorized access by human users and conventional software. But an AI agent that accesses a sensitive file is technically operating under its assigned service account credentials and with permissions granted by the platform team. The access is authorized at the infrastructure level even when it is inappropriate at the task level. AgentMon introduces task-level access control reasoning — asking not just whether the agent has permission, but whether the access makes sense given what the agent was asked to do.
The Organizational Challenge
Technology alone does not solve the AI agent monitoring problem. Organizations deploying agents need to develop new governance frameworks that define acceptable agent behaviors, establish escalation procedures for anomalous actions, and assign accountability for agent decisions. Who is responsible when an AI agent deletes production data — the developer who built the agent, the platform team that granted permissions, or the manager who approved the deployment?
These questions are not new — they mirror debates about autonomous systems in other domains — but the speed of AI agent adoption is outpacing the governance frameworks needed to manage it. Companies are deploying agents to production faster than they are developing policies for monitoring them, creating a risk gap that tools like AgentMon can help close but cannot eliminate on their own.
What Security Teams Should Do Now
For organizations already running or planning to deploy AI agents, the action items are concrete. First, inventory all AI agents in your environment, including their permissions, data access patterns, and external service connections. Second, implement behavioral monitoring — whether through AgentMon, competing products, or custom solutions — before scaling agent deployments. Third, establish cost guardrails that prevent runaway spending from agent behavior loops. Fourth, integrate agent monitoring into existing security operations workflows so that anomalous agent behavior receives the same incident response treatment as anomalous human behavior.
The 40 percent adoption prediction for 2026 may prove optimistic or conservative, but the direction is clear. AI agents will become a standard component of enterprise software architectures. Monitoring their behavior is not an optional add-on — it is a prerequisite for responsible deployment. Codenotary AgentMon is an early entry in what will become a crowded and essential market category.