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name	description	version
multi-angle-orchestration	Spin up 5+ parallel subagents (delegate_task leaf workers, all on this same Hermes session — NO external/separate AIs) on one project goal from different angles, then converge. Single-source orchestration for fast deployment.	1.0.0

Multi-Angle Orchestration (single-source, fast)

When the user gives an end goal and says "call up some subagents," this is the playbook. All work is done by THIS Hermes session's own delegate_task subagents — there is NO opencode / gemini / hermes CLI invocation, no external agent, no second AI. Fast deployment: parallel fan-out, then converge.

Hard rules

≥5 subagents, always. Decompose the goal into at least 5 distinct angles.
All same source. Every subagent is a leaf worker on this session's model. Never call execute_agent / the 3 CLI agents / a separate AI.
Parallel. Launch in one background batch where possible. The tool runs up to 3 concurrently for this user, so issue 5+ as: first batch of 3, then a second batch of the remaining (still effectively parallel, fast).
No human-in-the-loop inside workers. Subagents can't clarify; give them complete context up front.
I converge. When all subagents return, I synthesize their outputs into one result (no separate synthesizer agent needed unless the goal is huge).

The flow

Decompose the goal into ≥5 angles. Good angle variety (pick per goal):
- Implementer (what to build / change)
- Researcher / evidence (facts, refs, prior art)
- Skeptic / risk (failure modes, contradictions, what could break)
- Integrator (how it fits the existing system / repo)
- Communicator / docs (how to explain, UI copy, README, rollout)
- (extra) Tester / QA, Security, Performance, Ops/Deploy
Launch each angle as a delegate_task with FULL context:
- The goal, verbatim.
- Its specific angle + acceptance criteria.
- Relevant repo context (paths, constraints, the established patterns).
- Instruction: return a concise, self-contained result; do not ask questions.
Converge: reconcile contradictions, produce a unified prioritized plan or final answer + the single next step.
Log the run: write data/orchestration-runs/<run_id>.json with {goal, angles, agents:["delegate-x5"], synthesis, timestamp}. Then python3 brain-cli.py ingest so it's searchable in Brain Search (the orchestration source is indexed). Optionally surface results on the Orchestration dashboard page.

delegate_task call shape (per angle)

delegate_task(
  goal="<angle-specific deliverable>",
  context="""GOAL: <full user goal>
ANGLE: <this worker's perspective + acceptance criteria>
CONTEXT: <repo paths, constraints, prior decisions>
OUTPUT: concise, self-contained. No questions.""",
)

Batch up to 3 in one tasks=[...] call; fire the rest in a second call.

Notes

Subagent summaries are self-reports; verify external side-effects (writes, publishes) yourself before claiming success.
Keep angle prompts tight — workers get clean isolated context, no shared state, so everything they need must be in context.
This is the PRIMARY orchestration workflow. The Agentic OS /api/orchestrate backend endpoint (which fans out the 3 CLI agents) is an OPTIONAL headless alternative only — not used for this single-source workflow.

3.3 KiB Raw Blame History