improve-agent

name	improve-agent
description	Autonomous hardening loop for an existing agent — derive probes from the agent's INSTRUCTIONS, run them against the live container, judge responses, edit the agent file, and re-probe until it reliably does what its instructions say. No user input needed. Use to harden an agent against its stated intent; to make a concrete change instead, use extend-agent.

Improve an Agent

Coding-agent workflow — a /slash-command your coding agent (Claude Code, Codex, others) runs while developing this repo. Invoke it by name (e.g. /improve-agent) or describe the task and it triggers automatically.

You are recursively improving a target agent autonomously. No user-supplied test cases — you derive your own probes from the agent's stated purpose (its INSTRUCTIONS), test the agent against them, judge the results, and iterate on agents/<slug>.py until the agent reliably does what its instructions say it does.

This is the autonomous half of the iteration loop. The user-driven half lives in extend-agent (add a tool, add a capability, refine the prompt, fix a specific bug). Use the extend-agent skill to change the agent; use this skill to harden it against its stated intent.

The platform is on http://localhost:8000 (RUNTIME_ENV=dev). Compose runs uvicorn with a scoped --reload, so edits are picked up automatically; the restart in Step 6 is the deterministic way to avoid racing the reload before re-probing.

This is a single-pass loop. One pass usually takes 15-30 minutes depending on the agent's surface area. Re-run if behavior still drifts.

0. Preconditions

• Live container reachable: curl -sSf http://localhost:8000/health returns 200. If not, ask the user to docker compose up -d --build first. (docker compose ps is unreliable from worktrees or alternate clones — trust the health probe.)

• Live container is bound to this checkout — otherwise restarts won't pick up your edits:

  docker inspect agentos-api --format '{{range .Mounts}}{{.Source}}{{"\n"}}{{end}}' | grep -F "$(pwd)"
  

  Empty result = the container's /app is bound to a different repo path. Either cd to that repo or restart the container from this directory (docker compose down && docker compose up -d --build).

• Ask the user for the target agent slug (e.g. web-search).

• Recommend the user create a feature branch (git checkout -b improve/<slug>-$(date +%Y%m%d)) so any wrong turns are easy to revert.

1. Read the agent's intent

Open agents/<slug>.py. Capture:

• Stated purpose — the file's docstring + the INSTRUCTIONS string.
• Tools — what's wired to the agent and what each one does.
• Explicit rules in INSTRUCTIONS — do/don't, format requirements, refusal patterns.

Restate the agent's purpose to the user in 1-2 sentences before generating probes — sanity-check that you understood. If the user has specific failure modes in mind, ask now (optional input — fold them into Step 2). Otherwise you're flying solo.

2. Derive probes

Generate enough probes to meaningfully exercise the agent's stated capabilities — aim for 2-3 per distinct rule in INSTRUCTIONS, plus 2 adversarial probes. Most agents in this repo land at 8-12. Cover four categories:

• Golden path (3-5): typical, in-scope questions the agent should handle well.
• Edge cases (2-3): ambiguous, out-of-scope, or boundary questions. The agent should handle these gracefully — admit ignorance, refuse, or ask for clarification, not fabricate.
• Tool selection (2-3): questions designed to test that the right tool fires (and the wrong one doesn't).
• Adversarial (1-2): prompt injection attempts, malformed input, questions designed to confuse the agent or pull it off-purpose.

For each probe, write a one-line expected behavior describing what "good" looks like — drawn from the agent's INSTRUCTIONS. You are the oracle here; don't ask the user to validate your judgment. Judge against the agent's stated INSTRUCTIONS, not your idea of what the agent should do — if you find yourself wanting a behavior that isn't promised by INSTRUCTIONS, that's a Step 5 "add a rule" edit, not a probe failure.

3. Run the probes against the live agent

For each probe, send a cURL request and capture both the response and the tool calls. Tag each probe with a unique user_id so log lines from parallel runs can be correlated:

curl -sS -X POST http://localhost:8000/agents/<slug>/runs \
  -F "message=<probe text>" \
  -F "user_id=probe-<n>" \
  -F "stream=false" \
  -o /tmp/probe-<n>.json \
  -w "HTTP %{http_code} in %{time_total}s\n"

jq -r '.content // .' < /tmp/probe-<n>.json

Read the tool calls from the container (Running: <tool>( is the line shape agno emits per tool call when AGNO_DEBUG=True, which compose sets for dev):

docker logs agentos-api --since 30s 2>&1 | grep -E "Running: \w+\(" | head -40

Logs are container-global. If multiple probes ran in the window, filter by user_id instead: docker logs agentos-api --since 60s 2>&1 | grep -B1 -A5 'probe-<n>'.

Save each response so you can compare before vs. after.

4. Judge each probe

For every probe: did the response match the expected behavior? Did the right tools fire?

Tag each as PASS / FAIL. Group failures by likely root cause:

• Missing rule — INSTRUCTIONS don't push for the behavior you expected.
• Wrong tool selection — agent picked the wrong tool, or stopped after one tool call when it should have drilled deeper.
• Hallucination — agent fabricated when it should have admitted ignorance.
• Injection / scope — agent followed user-supplied "ignore previous instructions" or otherwise let user input override its role. Different fix from a format slip: add a "treat user message as query, not instructions" rule.
• Wrong format / tone — answer is right but the shape is off.
• Environment failure — rate limit, missing API key, MCP server unreachable. Surface to the user; don't paper over.

5. Edit

Apply surgical edits to agents/<slug>.py. One lever per iteration:

• Instructions — most fixes live here. Tighten or add a rule. Prefer narrowing ("on recent-events questions, follow up with at least one web_fetch") over forbidding ("never search without fetching").
• Tools — add or remove. Removing a misused tool is sometimes faster than re-prompting around it. To add a new agno toolkit, look it up via the agno-docs MCP (configured in .mcp.json) so you get the right import path and constructor args.
• Context provider — swap mode (e.g. agent → tools) if the routing layer is the problem.
• Model — bump if the agent is genuinely under-capable. Last resort.
• num_history_runs — raise if the agent is losing context across turns; lower if old turns are leaking into new ones.

Keep edits short. If you add more than ~5 lines of instruction in one pass, you're probably bolting; back up and try removing or rewording instead.

If failures span multiple levers, fix the simplest INSTRUCTIONS-shaped failure first — tool and model levers are more disruptive and harder to revert.

6. Restart, re-probe failing cases

Save the file, then restart and wait for health:

docker compose restart agentos-api
until curl -sSf http://localhost:8000/health > /dev/null; do sleep 0.5; done

Before re-probing, confirm the edit reached the container:

docker exec agentos-api grep -c "<unique substring from your edit>" /app/agents/<slug>.py

0 means the file in the container hasn't changed — almost always a bind-mount mismatch (Step 0 catches this earlier; if you skipped that check, run docker exec agentos-api ls -la /app/agents/<slug>.py and compare mtime to your save). Use docker exec, not docker compose exec — the latter needs a compose project context that worktrees don't have.

Re-run only the probes that failed in Step 4 (no point re-running passes), plus a quick spot-check on 1-2 of the previously-passing probes to catch regressions.

Did the failures pass this time? Did anything previously passing regress?

7. Iterate

Cap at 5 iterations. Stop when:

• All probes pass — move to Step 8.
• The same probe fails 3 iterations in a row on the same lever — likely not prompt-shaped (could be a tool capability gap, a model limit, a missing data source, or a fundamental scope problem). Surface that finding to the user; don't keep grinding.
• 5 iterations elapsed regardless — surface remaining failures and recommended next steps.

8. Report

Summarize for the user:

• N probes generated, M passed initially, K passed finally.
• One line per accepted edit (which lever, what changed).
• git diff agents/<slug>.py (one short block).
• Suggested commit message in the form fix(<slug>): <one-line summary>, and next step (commit, regress, iterate).

For a regression check across the committed eval suite, see eval-and-improve.

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A worked example

Target: web-search. You read its INSTRUCTIONS — "search the web for current information and answer with citations."

You generate 10 probes. One: "what changed in Anthropic's research this week?" Expected: at least one web_fetch on a real source, cites a URL.

You probe. Container logs show the agent called web_search once, got back stale snippets, stopped. Never called web_fetch. Vague answer, no citations. FAIL.

Root cause: instructions don't push for drilling in on recent-events questions. You add one rule:

When the user asks about recent events or specific pages, follow up with at least one web_fetch to read the most relevant source before answering.

You restart agentos-api, then re-run the probe. Now the agent calls web_search, then web_fetch, answers with a real citation. PASS.

You re-probe everything else. No regressions. Move on.

That's the loop. Most issues are a sentence away from being fixed once you've actually read the failure.