The current generation of AI systems is not primarily failing at the model layer. Most operational and security problems instead emerge at the interaction layer: prompt injection, unauthorized agent behavior, shadow AI usage, deepfake-enabled fraud, and uncontrolled data exposure through external AI services. These risks are fundamentally tied to inputs, orchestration logic, and execution paths rather than the underlying model itself.
Codenotary’s AgentMon focuses on this operational layer by providing runtime monitoring and policy enforcement around AI systems and agentic workflows.
The underlying premise is straightforward: prompts, agent decisions, tool invocations, and outbound interactions should be treated as observable and auditable events. Conventional logging pipelines are often insufficient because they operate retrospectively and typically lack awareness of AI-specific semantics and execution context. AgentMon instead instruments interactions closer to execution time—at prompt ingress, orchestration frameworks such as LangGraph or CrewAI, and tool execution boundaries.
This enables real-time inspection of both the content and behavior of AI-driven systems.
For prompt injection scenarios, the primary concern is often not malicious text itself, but attempts to override instructions, manipulate context, or trigger unintended execution paths. AgentMon analyzes prompt structures and interaction patterns to identify anomalies such as hidden directives, context poisoning, or attempts to extract protected information. When suspicious behavior is detected, the platform can generate compliance or security events for downstream systems such as SIEM or governance platforms. Depending on policy configuration, organizations may also choose to block or terminate high-risk requests before they reach the model.
Autonomous agents introduce additional complexity because risk frequently develops across a sequence of actions rather than a single event. AgentMon maintains contextual visibility into agent execution flows, including tool usage, API activity, and data movement between components. If an agent begins operating outside expected behavioral boundaries—for example, attempting unauthorized actions, generating unverified commitments, or escalating privileges—the activity can be flagged for investigation. Operational and compliance teams can then review the execution chain rather than relying on isolated log entries.
Shadow AI usage presents another challenge for organizations attempting to control how sensitive data interacts with external AI services. AgentMon addresses this through monitoring of outbound AI-related traffic and prompt flows. If sensitive source code, credentials, or internal business data is transmitted to unauthorized public AI endpoints, the system can identify the policy violation and generate alerts in real time. Organizations may also choose to enforce endpoint-level restrictions to prevent such transmissions entirely.
Supply-chain concerns around AI services and external models are similarly tied to visibility and governance. AgentMon tracks which models and endpoints are being used, what categories of data are transmitted, and under which policy constraints. This provides organizations with a clearer operational picture of how external AI dependencies are interacting with internal systems and data.
A key architectural characteristic is immediacy. Rather than relying solely on scheduled scans or post-incident audits, the system operates continuously during runtime. Alerts are enriched with execution context and behavioral traces, allowing security and compliance teams to investigate incidents with greater fidelity and reduced ambiguity.
As AI adoption increasingly shifts toward autonomous and interconnected systems, the operational attack surface becomes less about infrastructure alone and more about interactions, decisions, and execution flows. In that environment, runtime visibility and enforcement become central requirements rather than optional controls.