Definition
Telemetry for agents is the continuous capture of every input, output, and interaction an AI agent produces, creating a complete, structured record of its behavior over time so it can be inspected, analyzed, and trusted.
Quick breakdown
| Capture | Record every request, response, and tool interaction. |
|---|---|
| Context | Preserve prompts, memory, and environment state. |
| Sequence | Link actions into a timeline of behavior. |
| Storage | Persist data in a durable, queryable format. |
| Analysis | Enable inspection, anomaly detection, and learning. |
Why agents need dedicated telemetry
We built agents to act, but we didn’t build systems to understand what they did.
So when something goes wrong, you’re left reconstructing the story from fragments: partial logs, missing context, unclear decision paths.
You don’t see behavior. You see traces of behavior. In production, that’s not enough.
The moment an agent touches something real—customer data, APIs, systems—the question changes:
What exactly did it do?
Telemetry for agents exists to answer that question completely.
Why logs, metrics, and tracing fall short for AI agents
We already collect data: logs capture events, monitoring tracks metrics, tracing follows requests.
But agents don’t behave like traditional systems. They generate outputs, not just return values. They evolve based on context. They interact with multiple tools. They produce non-deterministic results.
So traditional systems give you fragments: what ran, how long it took, whether it failed. But not what the agent saw, what it decided, or how it got there.
How agent telemetry differs from logs and traces
| System | What it shows | What it misses |
|---|---|---|
| Logs | Discrete events | Full context and decision flow |
| Monitoring | Metrics and performance | Behavior and reasoning |
| Tracing | Request paths | Inputs, outputs, and intent |
| Telemetry for agents | Complete behavioral record | — |
What agent telemetry captures
Telemetry for agents is not just data collection—it’s behavioral recording. You’re capturing decision-making over time.
That means every input is preserved, every output is recorded, every step is linked, and every interaction is reconstructable.
”Did it fail?”
becomes:
“What did it see, what did it decide, and why?”
How agent telemetry works
Agent Behavior ├── Inputs (prompts, user data, context) ├── Actions (tool calls, intermediate steps) ├── Outputs (responses, decisions) └── Timeline (sequence of events)
Telemetry systems collect and structure this into a continuous record. That lets you inspect behavior step-by-step, analyze patterns across runs, and reconstruct entire sessions.
How tapes captures agent telemetry
Paper Compute treats telemetry as a foundational layer of agent systems:
Agent Systems ├── Telemetry → tapes └── Runtime → stereOS
- tapes captures agent telemetry at the network layer, recording every interaction without requiring instrumentation
- stereOS provides the runtime environment where agent behavior can be isolated and reproduced
Telemetry is not an add-on. It’s the system of record everything else depends on.
What agent telemetry makes possible
- answer exactly what an agent did
- debug failures without guessing
- detect loops, drift, and anomalies
- compare behavior across runs
- build systems you can actually trust
Without it, you’re operating on incomplete information.
Frequently asked questions
What is telemetry for agents? +
Why do AI agents need dedicated telemetry? +
How does agent telemetry work? +
How does agent telemetry differ from logs and tracing? +
How does tapes capture agent telemetry? +
Where to go next
- Continuous agent improvement — the loop that consumes telemetry concept
- AI agent skills — what telemetry gets extracted into concept
- AI session capture — the archive telemetry reads from concept
- Agent session replay — step through a captured agent run concept
- How to capture and replay agent telemetry tapes.dev
- Why runtime design determines what you can observe stereos.ai