As enterprises accelerate adoption of AI agents, visibility into how these systems behave—and what they cost—is becoming a growing concern. Codenotary has introduced AgentMon, a platform designed to monitor agent-driven environments, aiming to bring operational clarity to a rapidly evolving and often opaque layer of enterprise infrastructure.

The launch reflects a broader shift: as agentic AI systems move from experimentation to production, organizations are grappling with governance, security, and cost management challenges that traditional observability tools weren’t built to handle.

Bringing Observability to Agentic AI

Agentic systems—software agents capable of acting semi-autonomously on behalf of users or applications—are increasingly embedded across enterprise workflows. These agents can orchestrate tasks, interact with APIs, and even make decisions based on context. But their distributed and dynamic nature introduces new operational risks.

Codenotary’s AgentMon aims to address this by providing continuous monitoring across agent networks. The platform aggregates telemetry on agent behavior, resource consumption, and interactions with other systems, offering what the company describes as a unified operational view.

This includes visibility into agent health, communication paths, and model usage patterns, along with insights into inference latency and token consumption. For enterprises managing large-scale AI deployments, these metrics are quickly becoming as critical as CPU or memory monitoring in traditional systems.

The platform also focuses heavily on security and compliance. It tracks behaviors such as file access, handling of secrets, and data access patterns that could signal leakage or policy violations. By correlating these signals with behavioral baselines and data lineage, AgentMon attempts to surface anomalies that would otherwise remain hidden in complex agent workflows.

Codenotary positions this approach as analogous to observability in distributed systems—where understanding interactions between microservices is key—but applied to AI agents that operate with greater autonomy and less predictability.

A New Layer of Risk—and Cost

The rise of agentic AI introduces not just technical complexity but also financial unpredictability. Unlike static applications, agents dynamically select models, generate variable workloads, and consume tokens in ways that can be difficult to forecast.

This creates a new cost management challenge for CIOs and platform teams. Without granular visibility, organizations may struggle to understand which agents are driving usage—or whether those costs align with business value.

AgentMon addresses this by tying cost metrics directly to agent activity. By linking token usage and model selection to specific workflows, teams can begin to map spending to outcomes, a capability that is increasingly important as enterprises scale generative AI.

At the same time, security concerns are intensifying. Agents often interact with sensitive data and external services, raising questions about data exposure and policy enforcement. Traditional security tools, which focus on static applications or network boundaries, may not capture the dynamic behavior of AI agents.

Codenotary’s approach emphasizes continuous monitoring rather than point-in-time checks, effectively treating agent ecosystems as living systems that require ongoing oversight.

Positioning in a Crowded AI Stack

AgentMon enters a growing ecosystem of AI observability and governance tools. While platforms like model monitoring solutions focus on performance and drift, and cloud-native observability tools track infrastructure, fewer solutions are tailored specifically to agent-based architectures.

This distinction could become more important as enterprises adopt multi-agent systems, where coordination between agents—and their cumulative impact—becomes harder to track. In such environments, visibility into inter-agent communication and system-wide behavior is critical.

Codenotary’s broader background in software supply chain security also signals an emphasis on trust and verification, extending those principles into the AI runtime layer.

For enterprises already investing in cloud native and Kubernetes-based platforms, the emergence of agent-focused monitoring suggests a new operational layer is forming—one that sits above infrastructure and even above applications.

AgentMon is available now.