Records the measured numbers and syncs every document that names the delegation script paths, then bumps the framework to 4.3.0 in the three-way lockstep (lib.sh, VERSIONS.md current + matrix, eight SKILL.md frontmatters). phase1-benchmark.md carries both arms. The deterministic fake transport is what CI asserts on: 50 publishes cost 50 connections one-shot and 1 persistent, p50 54.813ms -> 0.002ms. The real broker run is informational, since the PoC default is a public broker: p50 1892.728ms -> 275.992ms, about 85% off. It also corrects the plan's own estimate rather than repeating it. D8 predicted the new shell path would drop from 3 forks to 2; the implementation is still 3, because keeping the `_delegate_script publish_event.py` lookup is the only thing that produces the "missing script tree does nothing" non-fatal contract that C-02 pins. Saving that fork would change the contract, so it is deferred to phase 4 rather than taken quietly. SKILL.md gains the context table saying which tree wins where, so an operator reading it knows a frozen loop runs frozen delegation code. The script paths in every document remain correct and every quoted command still works verbatim. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UrDNJkHmm18uu7n7JwV5Qd
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name, description, version, author, license, platforms, environments, metadata
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| multi-agent-mux-delegate-job | Delegate a unit of work to any autonomous agent (claude-code, hermes, agy, grok-build, codex, or a human) and observe it asynchronously over an MQTT event channel. Supported roles include orchestrator, worker, and reviewer. | 4.3.0 | godopu | MIT |
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multi-agent-mux-delegate-job — Async Job Delegation over MQTT
Delegate a unit of work to any autonomous agent, then observe it asynchronously instead of blocking. Every job gets a unique ID and a registry record. The worker agent publishes lifecycle events (started, permission_required, progress, completed, error) to a per-job MQTT topic, and the delegator/orchestrator subscribes to verify the final state.
This skill allows any agent (claude-code, hermes, agy, grok-build, etc.) to play any role: Orchestrator/Delegator, Worker/Implementer, or Reviewer.
Roles in Multi-Agent Mux
- Orchestrator (Delegator): Initiates the job, coordinates other agents, handles loops and reviews, and commits final changes.
- Worker (Implementer): Receives the brief file or task prompt, performs the implementation, and emits started/completed/error events.
- Reviewer: Evaluates git diffs or artifacts produced by the worker, and responds with a
completedevent containing"PASS"or feedback.
Core Commands (CLI)
The multi-agent-mux-delegate-job bash wrapper handles job registration, subscriber management, agent session targeting, and validation hooks:
# 1) Submit a new job to a targeted agent session (e.g. herdr session name 'demo')
multi-agent-mux-delegate-job submit \
--agent <claude-code|hermes-agent|agy-agent|grok-build|human> \
--agent-session herdr:<session_name> \
--prompt "Task description or instructions here" \
--role <Worker|Planner|Reviewer> \
--timeout 3600 --idle-timeout 120
# 2) Submit a job with a feedback loop (Worker-Reviewer Loop)
multi-agent-mux-delegate-job submit \
--agent <worker_agent> --agent-session herdr:<worker_session> \
--type loop --reviewer <reviewer_agent> --reviewer-session herdr:<reviewer_session> \
--prompt "Task description"
# 3) Check job status and audit logs
multi-agent-mux-delegate-job status --job <JOB_ID>
multi-agent-mux-delegate-job logs <JOB_ID> # Chronological log of events
multi-agent-mux-delegate-job list # Summary of all registered jobs
# 4) Verify job artifacts with a validation script
multi-agent-mux-delegate-job verify --job <JOB_ID> --validate ./validate.sh
Task Delegation Types
Supported job types include:
direct(default): Single agent execution (direct tasking).loop(Worker-Reviewer Loop): Alternates worker execution and reviewer evaluation until reviewer approves (PASS) or iterations run out.discuss(Research & Discussion): Collaboration between two agents to reach a consensus (e.g., agreeing on a design or plan).
For detailed state machine diagrams and configurations, see DELEGATION_TYPES.md.
Where the Implementation Lives (v4.3.0)
The implementation moved to the lib_py.delegation package in issue #5 phase 1.
scripts/{mqtt_common,registry,publish_event,job_subscriber}.py remain as
compatibility shims and their paths are unchanged -- every command in this
document still works verbatim. Each shim binds the lib_py.delegation package
to the tree next to itself, once per process, without touching sys.path, so
import mqtt_common yields the same module object as
lib_py.delegation.mqtt_common.
Which tree wins, by execution context:
| Context | Resolved by | Tree that wins |
|---|---|---|
Loop process and its children (frozen lib.sh sourced) |
PYTHONPATH |
the frozen snapshot |
Agent session running a frozen script path while PYTHONPATH still points live |
the shim, from its own location | the frozen snapshot (B-13 preserved) |
| Live helpers (create/stop/resume/reconcile) | PYTHONPATH or the shim (same tree) |
the live tree |
Standalone, no PYTHONPATH |
the shim's fallback append | the shim's own tree |
lib.sh calls the package through python -m lib_py.delegation.cli. Setting
MAM_DELEGATION_PY=0 switches the shell helpers back to their preserved legacy
bodies with no redeploy.
The Event Protocol Contract
Every agent participating in the delegation contract must follow the same lifecycle publishing protocol using publish_event.py:
- On Start: Publish
startedevent.python3 .agents/skills/multi-agent-mux-delegate-job/scripts/publish_event.py --job "$JOB_ID" --event started - On Tool/Permission Prompt: Publish
permission_requiredevent.python3 ... --job "$JOB_ID" --event permission_required --detail "<tool>:<reason>" - On Progress Update (Optional): Publish
progressevent.python3 ... --job "$JOB_ID" --event progress --detail "<status_update>" - On Success: Publish
completedevent.python3 ... --job "$JOB_ID" --event completed --detail "<summary>"(Reviewer should include"PASS"in the detail to approve). - On Failure/Feedback: Publish
errorevent.python3 ... --job "$JOB_ID" --event error --detail "<reason_or_feedback>"
Audit Logs
Job lifecycle execution events are persistently mirrored to an append-only log under .mam/delegate_job_logs/<job_id>/ (containing meta.json, events.ndjson, and status.json). Use multi-agent-mux-delegate-job logs <job_id> to view the timeline.
Best Practices and Pitfalls
- Subscribe-Before-Publish: The subscriber must be running before the agent starts publishing. The
submitcommand handles this automatically by launching the subscriber in the background first. - Fresh job_id Propagation: Make sure the worker agent receives the correct
JOB_IDgenerated for the current run, rather than reusing stale IDs from previous sessions. - Brief delivery via file path: For long or complex prompts, write the instructions to a file (e.g.
/tmp/task-brief.md) and pass a short prompt pointing to the file path to prevent terminal buffer overflows. - Prompts injected into a live agent session MUST be English, ASCII-only, and short — this is exactly what
--prompt/theinstructionsstring sent torun_agent()end up as. Any Korean (or other non-ASCII) content the task needs to convey must go in a markdown brief file (e.g..mam/jobs/<id>/brief.md, written in Korean is fine) that the injected prompt merely tells the agent to read. Two independent bugs insend_keys_safe's paste-verification (inlib.sh) made this matter in practice: (a) its marker was taken with a byte-basedtail -c 24, which can slice a multi-byte UTF-8 (e.g. Korean) character in half; (b) the rendered pane soft-wraps long lines at the terminal width, which can split the marker across two visual lines. Both are now fixed at the source (character-safe truncation + newline-stripped matching before comparison), but keeping injected prompts short/English/file-referencing is still the cheapest way to avoid ever exercising this edge case at all — it's also simply whatsubmit's own default instruction template already does (seeCore Commandsabove). - Batch Grouping: Group non-overlapping tasks into batches to parallelize execution across multiple agent sessions, reducing overhead.