Brevet

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AI agents now change their own behaviour while they work. They save memories, write themselves new skills and edit their own instructions. Many of these changes help. Yet none of them passes through the steps an organisation expects when a person changes how work is done. Nobody writes the change down, nobody approves it, and when it turns out to be wrong there is no earlier version to go back to.

Brevet adds those steps. It is a local-first Python runtime that wraps the agent you already have and runs its learning as a governed evolution loop. When an expert corrects the agent's draft, Brevet records the correction and its reason as an override. Offline, in the dream cycle, overrides that keep recurring become candidate capabilities: proposed rules and other learned behaviour, with no authority. At the dawn gate, a named human or mission group (the accountable review board) decides which candidates to promote. The overrides then replay as evals, and the conservative gate stops a release that makes either half of them worse. Promoted capabilities ship in a signed release, listed in capabilities.lock, and a capability that proves wrong can be recalled, with every release that shipped it flagged. Every step is recorded on a hash-linked evidence chain.

Agents propose deltas; evidence tests them; humans promote them;

the runtime only ever executes signed versions.

Three questions Brevet answers

A concrete example

A quality reviewer at a pharmaceutical plant checks an agent's severity rating for each equipment problem. The agent rates pump vibration during cleaning as *minor*. She overrides it to *major* every time, because that vibration is an early sign of seal wear. After four overrides, the dream cycle proposes a candidate rule. At dawn her mission group promotes it, and release 0.2.0 ships with the rule in its capabilities.lock. Months later, engineers trace the vibration to a faulty sensor, so the mission group recalls the rule and Brevet flags release 0.2.0.

The same story in code

The agent here is a plain Python function; with a real framework you pass your agent object instead.

import brevet

agent = brevet.wrap(triage_agent) # wrap the agent you already have

Work and override: the agent drafts; the reviewer corrects the draft and says why.

result = agent.run("Pump P-301: vibration high during cleaning", task_family="equipment_triage") agent.record_final(result.task_id, "severity: major", participant="human:qa.reviewer@example.com", rationale="Vibration during cleaning is an early sign of seal wear.", tags=["vibration-during-cleaning"])

Dream: once the same override keeps recurring, it becomes a candidate.

agent.dream() candidates = agent.dawn() # the dawn queue rule = next(c for c in candidates if c.kind == "prompt_rule")

Dawn: a named mission group promotes the rule and the eval cases compiled

with it. A dream:* approver is rejected.

for cap in candidates: agent.dawn(decide=(cap.capability_id, "promote"), approver="mission_group:quality_team")

Evals: replay the overrides as tests, on the current release and on the next.

before = agent.evaluate(baseline=True)

...the agent now receives the promoted rule with each task...

after = agent.evaluate()

Release: bound to those two runs, and refused unless the conservative gate passes.

agent.release(to_version="0.2.0", channel="trial", approver="mission_group:quality_team", evals=(before, after))

Recall: the rule proves wrong. Then verify the whole evidence chain.

agent.recall(rule.capability_id, reason="The vibration came from a faulty sensor.", issued_by="mission_group:quality_team") agent.verify()

The full script is examples/pump_vibration.py, and ABOUT.md shows what it prints.

Quickstart

pip install brevet

brevet demo # the whole loop as one command brevet playground # step through the loop in your browser

Everything runs on your own machine, with no model or network connection. To run the example above, install Brevet, clone the repository and run python examples/pump_vibration.py. For a guided, clickable tour, open docs/demo.html from your clone in a browser.

Works with the agent you already have

brevet.wrap() recognises agents built with LangGraph, the Claude Agent SDK, DeepAgents, AutoGen, LlamaIndex, Pydantic AI, the Google Agent Development Kit, CrewAI and the OpenAI Agents SDK, and it accepts any Python function. Your framework keeps running the agent; Brevet serves it its governed rules with each task and, wherever the framework exposes its tools, checks each tool call against the tiers its release declares. uvx brevet mcp offers capture, review, release and recall to any MCP client (ABOUT.md shows the setup), and examples/claude-cowork uses it to govern what Claude itself learns.

Built for production

Every decision can require the signatures of approvers whose keys are checked against your allowed-signers file or GitHub. Releases are bound to the eval runs behind their numbers, and any release can be rolled back. A recalled rule is withheld from running agents, which confirm it on the record. A tool broker checks the agent's tool calls against the tiers the manifest grants, and the evidence chain is anchored outside the workspace, so even a rewritten chain is caught. ABOUT.md shows how to switch each one on: see Signed approvals and Running in production.

Learn more

• [ABOUT.md](ABOUT.md): the seven stages, the authority ladder, supported frameworks, the MCP server and commands, how Brevet fits with CHAP and Metis, and how the repository is organised.
• [GLOSSARY.md](GLOSSARY.md): every term, with its plain meaning first.
• [SPEC.md](SPEC.md): the rules any implementation must follow.
• [BENCHMARK.md](BENCHMARK.md): the governed-adaptation benchmark; brevet benchmark scores a workspace on its four axes.

Citation

If you use Brevet in research, please cite it as CITATION.cff describes. The accompanying paper is *Brevet: Change Control for What Self-Evolving AI Agents Learn* (Shahid, Suttie and Black, 2026).

License

Apache-2.0. See LICENSE. Brevet is a Brightbeam project.