Agent Runtime Telemetry: Fields, Formats, and Retention

What Counts as Agent Runtime Telemetry

Agent runtime telemetry refers to the data an AI agent framework emits while executing a task: the identity of the agent and the principal that invoked it, the sequence of tool calls it makes, the inputs and outputs of those calls, and the permission scope exercised at each step. This differs from standard application logging in one important respect. An agent's behavior is not a fixed code path executed the same way each time; it is a chain of decisions, often involving multiple tool invocations, that can vary based on model output. Reconstructing that chain after an incident requires telemetry that preserves sequence and causality, not just isolated events.

No single ratified standard currently defines a complete agent telemetry schema. OpenTelemetry has published semantic conventions for generative AI systems covering model calls, token usage, and emerging support for agent and tool invocation spans, but this specification is explicitly marked experimental and subject to change. Teams building telemetry pipelines today are working against a moving target, which makes deliberate field selection more important than waiting for a settled standard.

Fields Required to Reconstruct a Decision Path

Investigating an agent incident, whether a security event or a compliance question, depends on being able to answer three things: who or what acted, what it did, and what the outcome was. Applied to agents, this translates into a specific set of fields.

• Identity fields establish attribution: agent ID, model and version, the invoking user or service principal, and a session or task ID that groups related actions together.
• Tool-call fields capture the mechanics of each action: the function or tool name invoked, the input parameters passed to it, the returned result, and the permission scope the call exercised.
• Sequence fields preserve order and hierarchy, typically using a trace ID, span ID, and parent span ID, so a chain of tool calls within a single agent task can be reassembled in the correct order, including nested sub-calls.

This structure is not unique to agents. NIST SP 800-53's audit and accountability controls require audit records to include timestamps, source and destination identities, and event outcomes for information systems generally. Cloud provider audit logs, such as AWS CloudTrail's event structure, follow a similar pattern: identity, event source, event time, and request or response parameters. Agent telemetry applies the same principles to a system where a single user action can trigger many downstream tool calls, which is why sequence and permission-scope fields carry more weight than in conventional application logging.

Formats: Structured Logs, Traces, and Emerging Conventions

Two structural approaches dominate current practice. The first is structured, JSON-based logging, where each event is a self-contained record with defined fields. This is straightforward to implement and widely supported, but on its own it does not naturally express the parent-child relationships between a task and its tool calls.

The second is distributed tracing, using OpenTelemetry's trace and span model. A trace represents an end-to-end operation, and each span within it, identified by a trace ID, span ID, and parent span ID, can represent a discrete tool call or model invocation. This model is structurally well suited to agent workflows because each step in a multi-step agent task can be modeled as a child span of the overall task trace, preserving both the sequence and the hierarchy of tool calls.

OpenTelemetry's GenAI semantic conventions attempt to standardize the attribute names used within these spans for LLM requests, responses, and token usage, with emerging coverage for tool and agent spans. Adopting these conventions where feasible improves interoperability with existing observability stacks, but because the specification is experimental, teams should expect field names and structures to change and should avoid hard-coding assumptions about long-term stability.

The Cross-Framework Interoperability Gap

Different agent orchestration frameworks currently implement logging differently, and there is no standardized schema that all frameworks conform to for tool-call metadata, agent identity, or permission representation. This creates a practical problem for platform teams operating multiple agent frameworks side by side: telemetry from one framework may not map cleanly onto telemetry from another, complicating both real-time monitoring and after-the-fact investigation across a mixed environment.

A collector-based architecture, where telemetry from multiple frameworks is centralized and normalized before being sent to a backend, is one common approach to addressing this gap. This typically requires custom instrumentation to map each framework's native output into a consistent schema, since no framework's default logging currently aligns fully with OpenTelemetry's GenAI conventions or any other common standard.

Setting Retention Policy

Retention decisions should follow organizational records-retention policy and applicable legal or regulatory requirements rather than a fixed universal duration. NIST SP 800-53's AU-11 control requires that audit records be retained consistent with an organization's records-retention policy to support after-the-fact investigations, without prescribing a specific retention period. No source reviewed here identifies a binding regulation that sets an agent-telemetry-specific retention duration; current guidance is extrapolated from general audit-log retention principles.

In practice, this means separating high-volume operational telemetry from lower-volume security- and audit-relevant events, and applying different retention tiers to each. Operational telemetry used for debugging may have a short useful life and can move to cheaper cold storage quickly. Security and audit-relevant events, such as permission-scope usage and tool-call records tied to sensitive actions, typically warrant longer retention to support investigation timelines and compliance reporting. This tiering approach balances storage cost against the practical reality that investigations and audits often occur well after the events in question.

Telemetry as an Input to Policy Enforcement

Telemetry that is only available in batch form, hours or days after the fact, cannot support real-time detection of anomalous agent behavior. Anomaly detection and policy enforcement systems depend on tool-call and permission-use events being emitted synchronously or near-real-time so that unusual patterns, such as an agent invoking a tool outside its expected permission scope, can be flagged as they occur rather than discovered later.

This is where telemetry design intersects with governance. OWASP's guidance for LLM applications identifies insufficient logging and monitoring of model inputs, outputs, and tool invocations as a named risk affecting both incident detection and forensic capability. The NIST AI Risk Management Framework similarly treats traceability and record-keeping as a core risk management function supporting incident response, though it stops short of specifying technical fields. Structured, real-time telemetry is what allows these governance principles to be operationalized as actual enforcement, rather than remaining a documentation exercise. Trussed AI's runtime governance approach applies this by using agent identity, tool-call, and permission telemetry as direct inputs to policy enforcement, tool approval workflows, and audit logging at runtime.