Agent artifact
An agent artifact is the durable output of an AI agent run — a findings report, a dependency audit, a migration plan, an evaluation summary — as distinct from the transcript of the run itself. It is the part a human is meant to read, and the part that should still exist next month.
The distinction matters because agents produce both and the tooling usually only keeps the transcript.
Why chat is the wrong container
A long agent answer is a document wearing a message costume:
- Length ceilings. Output gets truncated, or split across messages that then scroll away (too long to paste).
- No rendering. A 300-row table, a diff, a chart, a collapsible finding list — markdown in a chat window renders none of these well.
- No address. You cannot link a teammate to "the middle of that reply".
- Nowhere to respond. A colleague's reaction goes into a different thread from the thing it is about.
- No persistence. Clearing the session deletes the deliverable.
The tell is behavioural: when a person's first move after reading agent output is to copy it somewhere else, the output was in the wrong place.
What a real artifact has
| Property | Why it matters |
|---|---|
| Stable URL | Linkable from a ticket, a PR, an incident doc |
| Own rendering | Tables, charts, drill-downs — an HTML report |
| Access setting | Unlisted, or restricted — an audit is not a public page |
| Revisions | Run 14 is diffable against run 13 |
| Anchored comments | "This finding is a false positive" sits next to it |
| API access | The agent can read the reaction back |
The loop that closes
The last two rows are what turn an artifact into a workflow:
- The agent finishes a run and publishes a report, returning a URL.
- A reviewer opens it, selects one finding, and comments — "already accepted, tracked in PLAT-441".
- On the next run, the agent fetches the comment threads, sees the decision attached to that finding, and suppresses it — with the reasoning preserved next to the evidence (let an agent respond to comments).
Nobody retyped the feedback into a prompt, and nobody has to remember the decision in six months.
This is what an agent-facing publishing API is for: an agent creates the report as an ordinary step of its run, over MCP or plain REST, authenticated with a scoped, revocable token rather than a human session (scoped tokens for AI agents).
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