# How to Share an AgentOps Session Replay With Your Team

Canonical: https://commareports.com/agents/share-agentops-report
Published: 2026-09-14

> AgentOps session replays live behind a dashboard login and tell you what the agent did, not what it produced. Publish a run summary anyone can read and reply to.

# Share an AgentOps session

AgentOps gives you the thing that makes agents debuggable: a full replay of the
session. Every LLM call, every tool invocation, tokens, latency, cost, and the
exact point where the agent looped three times before giving up.

It is the right tool for working out *why* a run went wrong. It is the wrong
artifact to send the person who asked what the agent found.

## Two different questions

**"What did the agent do?"** — a debugging question, asked by whoever owns the
agent, answered by the replay.

**"What did the agent produce, and can we trust it?"** — asked by everybody
else, and not answered by a trace at all. A replay shows forty tool calls; it
does not show whether the conclusion was correct.

Handing a stakeholder a session replay is handing them a flight recorder when
they asked where the plane landed.

## Publish the outcome, link the trace

At the end of a run, render a summary:

```python
import agentops

session = agentops.start_session()
result = run_agent(task)
stats = agentops.end_session("Success")

publish_report(
    title=f"Agent run — {task_name}",
    html=render_summary(
        task=task,
        conclusion=result.summary,
        artifacts=result.outputs,
        cost=stats.cost,
        duration=stats.duration,
        replay_url=session.session_url,
    ),
)
```

The page carries, in this order: what was asked, what the agent concluded, the
evidence, what it cost, and — at the bottom — the AgentOps replay link for
whoever has access and wants the trace.

That ordering is the whole design. The reader who needs the answer gets it
first; the engineer who needs the trace is one click away.

## Cost belongs on the page

Agent runs cost real money, and that cost is usually invisible outside the team
running them until somebody reads a bill.

Putting `$0.84 · 4m 12s · 37 tool calls` on every run summary changes the
conversation from "is this agent good?" to "is this agent worth it?" — which is
the question that actually decides whether the workflow ships. It also makes
regressions visible: a run that cost $0.84 last week and $3.10 today is a
problem you find now rather than at month end.

## Feedback closes the loop

The reason to publish rather than post is that the reply becomes input. A
reviewer who comments "this conclusion ignores the Q2 data" on the relevant
paragraph is giving the agent its next instruction — and the agent can read it:

> Fetch open comments on report `<id>`, address them, update the report, and
> reply in each thread.

See [letting an agent respond to comments](/agents/let-an-agent-respond-to-comments).

## Worth knowing

- **Do not paste the trace into the report.** Forty tool calls rendered inline
  buries the conclusion; the replay link covers it.
- **`reports:write`, plus `comments:read` for the loop** — see
  [scoped tokens for AI agents](/agents/scoped-tokens-for-ai-agents).
- **One report per recurring task**, updated per run, so the URL tracks the
  latest outcome with its history attached.
- **5 MB per report body.**

## Try it

Free — unlimited reports, commenters and revisions.

**[Read the API reference →](/docs/api)**

### Related

- [Where should my agent post?](/agents/where-should-my-agent-post) ·
  [Let an agent respond to comments](/agents/let-an-agent-respond-to-comments)
- [Share a Phoenix trace report](/agents/share-phoenix-trace-report) ·
  [Share a Langfuse eval result](/agents/share-langfuse-eval-report)
- [Context budgeting for AI reports](/agents/context-budgeting-ai-reports) ·
  [The API](/docs/api)