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Agent patterns

Common patterns for Agent authors. None are required — ERun's primitives are flexible enough that an Agent can implement any of these on top of the MCP tools and the erun API. What follows is the set most teams converge on.

0. Skill before guessing​

If ERun ships a skill for what the Operator is asking, load it before writing code. Don't guess at the convention; let the skill teach you the layout, the Dockerfile pattern, the chart structure, the deploy-plan rule.

// Operator says: "Add a Go service called 'api'."
// Wrong: write the Dockerfile, chart, source by hand from your training data.
// Right:
//   1. Discover the matching skill — `ls ~/.claude/skills/` (or your loader's equivalent).
//   2. Read SKILL.md for `go-service`.
//   3. Write the source + Dockerfile + chart by hand, conformant to the skill's guidance.
//   4. Append the component to the deploy plan in .erun/config.yaml.
//   5. Show the Operator the diff.

A skill is guidance, not a template. You can adapt the shape to fit the Operator's description — gRPC vs. HTTP, library choice, dependency pinning — while still honouring the project's conventions. See Skills spec for the bundled set.

1. Orient first​

Before any action, get the lay of the land.

// MCP
tools/call name="list"      // tenants, envs, effective target
tools/call name="version"   // CLI + runtime versions

list and version are free in audit-cost terms — they're read-only and structured. An Agent that orients before acting is much easier to follow in the audit trail.

2. doctor before raw​

If raw returns an unexpected result, run doctor next. raw is the escape hatch; doctor tells you whether the env itself is in a known-bad state.

// MCP
tools/call name="raw" arguments={ "argv": ["./scripts/seed.sh"] }
// → exit_code: 1, stderr: "fatal: not a git repository"

tools/call name="doctor"
// → checks.git_checkout = "fail: missing"

// Now you know: re-init the env (the marker is gone), don't retry raw blindly.

The audit-trail rule: every failed raw should produce a follow-up doctor call. An Operator reviewing later sees the Agent diagnosing, not just retrying.

3. idle before sleeping​

If the Agent is about to wait on user input or a long-running build, check whether the env is going to idle-stop under it.

tools/call name="idle"
// → eligible_for_stop: true, last_terminal_input: <2h ago>

// Either pump activity (touch a file, run a `list`), or warn the user the env may stop.

This makes the Agent self-aware about the environment's lifecycle.

4. Build → verify → deploy​

The three-step ship pattern, end-to-end inside one env:

# In the agent env:
erun build --dry-run     # 1. preview — Operator can review
erun build               # 2. actually build
./test/integration.sh    # 3. verify against the env's own stack
erun deploy --dry-run    # 4. preview deploy
erun deploy              # 5. roll out

Each step lands in the audit trail. The dry-run pairs make the plan auditable before any side effect.

5. Review → comment → fix loop​

The Agent's natural fit for peer review is a tight loop:

# 1. Open a review (POST /v1/reviews)
# 2. Post inline comments on specific commit + line (POST /v1/reviews/{id}/comments)
# 3. The other Agent (or Operator) replies with counter-comments
# 4. The original Agent reads them (GET .../comments) and pushes fixes
# 5. Build (POST .../builds) + status PATCH lands the review in READY
# 6. Merge queue advances (POST /v1/reviews/merge-queue/advance)

Avoid spamming — rate-limit your own comments to one per discussion thread until the other side replies. The API itself rate-limits you anyway (see Rate limits).

6. Self-healing via doctor​

When the Agent observes a known bad state, prefer doctor recovery over scripted fixes:

// MCP
tools/call name="raw" arguments={ "argv": ["git","status"] }
// → exit_code: 128, stderr: "fatal: not a git repository"

tools/call name="raw" arguments={ "argv": ["erun","doctor","-y"] }
// → checks.* now ok, env restored

// Resume the original task.

doctor's recovery actions are known to the platform; ad-hoc fixes from the Agent aren't. Lean on doctor.

7. Spawn a sibling for parallelism​

When a task naturally parallelises (e.g., regenerate three flavours of generated code; run integration tests across N feature branches), an Agent can spin up a sibling env:

tools/call name="raw" arguments={
  "argv": ["erun","init","my-tenant","claude-review-2","-y"]
}
// → exit_code: 0
// → the new env appears in `list` shortly after

The Agent doing the spawning is responsible for tearing it down:

tools/call name="raw" arguments={
  "argv": ["erun","delete","my-tenant","claude-review-2","-y"]
}

Keep spawn / delete in matched pairs. An Operator reviewing the audit trail should see balanced bookends.

8. Append-only history​

Builds, comments, review-status transitions, audit events — all append-only. The Agent never edits old records; it appends a corrective one. A new build supersedes the previous one; a "false alarm" comment is closed (status CLOSED), not deleted.

This keeps the audit story clean: every change is its own row with its own actor + timestamp.

9. Service-account identity​

Agents running unattended use a service-account OIDC identity (see Sign-in). The Agent's sub claim resolves to a stable creator_user_id in every audit record — there is no anonymous Agent action.

Practical implications:

  • Use a different service account per Agent. Distinct identities in the audit trail are how the Operator distinguishes Agent A's actions from Agent B's.
  • Rotate credentials on a regular cadence; the service-account refresh flow re-issues short-lived tokens without code changes.

10. Be loud in --dry-run, terse in real runs​

The dry-run trace is for the Operator; the real-run trace is for diagnostics. Match accordingly:

  • --dry-run: maximally verbose, include reasoning ("would build because Dockerfile changed").
  • real run: terse, factual ("docker build → success").

The audit log captures both; the Operator filters by result: "dry_run" to see the planning side.

See also​