The retro loop

A manifesto for self-improving AI coding agents.

Every sub-agent you spawn finishes its work and disappears. Whatever it figured out - which command failed three times before the right one, which file actually mattered, which approach to skip next time - dies with it. The next sub-agent rediscovers it from scratch. Your own next session does too.

This is not a memory problem. Memory is what you remember. Retro is what you reflect on, structure, and turn into the next bet. The agent stack has compute, tools, and logs. It does not have a reflection loop.

ax is that loop. Before any session ends - main or sub-agent - ax asks the agent for a structured retro: what was tried, what worked, what failed, what to try next. Each retro becomes an experiment in a typed local graph. Each experiment gets a verdict the next time the same situation appears. Over weeks, the graph carries the signal your sessions otherwise dropped on the floor.

This is verbal self-reflection at the engineering layer. Reflexion for software. The closed loop is the product.

What's in the stack today

Look at any production AI-agent setup in 2026. You have models - Claude 4.7, GPT-5, the open-weights crowd. You have inference platforms. You have a tool layer: shells, editors, MCP servers, IDEs. You have memory bolt-ons: a vector store, maybe Letta, maybe just a long context window. You have observability at the API level - LangSmith, Langfuse, Phoenix - telling you what the agent did.

What none of them have is a reflection step. Nothing in the stack asks the agent at end-of-session: what did you learn? and turns the answer into something the next session can read.

The closest analog is the human side: code review, retros, post- mortems. Engineers do them because the act of structuring what happened is what makes the next iteration better. Without it the team just does the same thing again.

Agents are now in the same place. They have the intelligence. They lack the reflection loop.

What the loop has to do

A real retro loop for AI coding agents needs five things:

Fire at the right time. The Stop hook on session-end is the only moment the agent has all its context loaded. Five seconds later it's gone. The retro has to be enforced there, not opt-in.
Structured by default. Free-text retros don't compound. JSON with four fields - tried, worked, failed, next - lets the graph index, query, and diff each piece independently. Free-form is an opt-in escape hatch, not the default.
Cover sub-agents. Main-session retros are nice. Sub-agent retros are the unlock. Sub-agents fan out, finish fast, and die with everything they learned. The hook has to fire for them too, and the retro has to roll up to the parent session.
Become a proposal. "That worked, do it more" isn't useful. "That worked, here is the skill / hook / guidance candidate that captures it, ranked by how often it would have fired" is. The retro feeds a deduplicated proposal queue the user accepts, rejects, or skips.
Become an experiment. Accepted proposals get scaffolded as real artifacts and tracked as experiments. Checkpoints at +3 / +10 / +30 sessions after accept close the verdict: did this thing actually get used, did it improve outcomes, did it regress, did the pattern self-resolve. Sessions, not calendar days - a weekend doesn't delay the verdict, a productive afternoon doesn't rush it. Every verdict is itself signal for next time.

That's the spec. Ingest -> reflect -> propose -> experiment -> verdict -> next session reads what worked. Nobody is shipping all of it.

What `ax` is

ax is the reference implementation. Local typed graph, agent-readable queries, React dashboard, MIT licensed. Runs on your laptop.

The surface is small on purpose:

ax install wires the Stop hook into your Claude Code and Codex configs. One command, one time.
ax retro is what the hook calls at session-end. The agent emits the JSON. ax indexes it.
ax improve list shows the proposal queue derived from accumulated retros and friction signals.
ax improve accept | reject triages it. Acceptance scaffolds the artifact and opens an experiment.
ax improve verdict shows the checkpoint state and locks the outcome.
ax retro (the slash-command skill) walks you through the same triage interactively in Claude Code.

That's the loop. Everything else - the dashboard, the typed graph, the search, the skill-taste scoring - is in service of those six commands.

Why this matters now

The interesting agent work in 2026 - inside labs and out - is multi-agent. Sub-agent fan-out, role delegation, parallel worktrees. Every additional sub-agent multiplies the amount of evidence silently thrown away. The marginal cost of not closing the loop goes up with every Task tool call you make.

The labs know this. Internal evals at OpenAI and Anthropic absolutely track this stuff. What's missing publicly is an open, local-first reference for what the loop should look like at the end-user side - on the developer's laptop, with their data, in their hands.

ax exists because I needed it. I'm publishing it because the shape of the loop matters more than the implementation, and the shape gets locked in early.

What's next

Read the README. Install ax. Let it watch a week of your sessions. Run ax retro at the end of one. Look at what falls out.

Then tell me what's wrong with the shape - there are nineteen ADRs in docs/adr/ arguing with my past self about exactly that. If you want to argue with the framing, open an issue. If you want to extend it, read CONTRIBUTING.md.

If you're building agent infrastructure inside an AI lab right now - and I know you are - you're building some version of this. Your version will be better-funded, more polished, deeper-integrated. It should be. What it won't be is open, local-first, and shape-of-the- problem first. That's what an open reference does - it shapes the problem statement before the closed implementations lock in proprietary vocabularies.

The stack is missing a reflection step. Let's build it.