onewave-ai/sub-agent-orchestrator

Sub-Agent Orchestrator

Design and execute multi-agent pipelines where each step is a different agent that depends on the previous one. Define roles, dependencies, and handoffs in YAML, then run sequential, parallel, conditional, loop, and map-reduce workflows with retry, timeout, and validation.

Unlike Agent Army (homogeneous parallel code changes) and Agent Swarm (homogeneous parallel data processing), this orchestrator coordinates heterogeneous pipelines where the output of A feeds the input of B.

Contents

• references/patterns.md -- The six workflow patterns and the comparison to Agent Army/Swarm.
• references/workflow-schema.md -- Full YAML workflow definition language.
• references/examples.md -- Complete worked workflows (research-to-proposal, lead scoring).
• references/execution-engine.md -- Per-step execution model, retry, timeout, validation, edge cases.
• references/templates.md -- Reusable workflow scaffolds.
• references/visual-and-reporting.md -- Text diagrams and the execution report template.

Workflow

1. Determine the mode from the request:

   • Run a workflow file: read the YAML at the given path.
   • Define and run inline: convert the natural-language description into a workflow YAML (see references/workflow-schema.md), then show it for approval.
   • Dry run: parse, validate, resolve inputs, and show the execution plan without deploying agents.
   • Inspect: parse the YAML and produce a human-readable description plus a text diagram (see references/visual-and-reporting.md).

2. Parse and validate the workflow: confirm required fields, that agent IDs resolve, and that there are no circular dependencies. Report syntax or reference errors with the offending line. See references/execution-engine.md.

3. Resolve inputs: collect every required input from the user before starting; apply defaults for optional inputs.

4. Build the execution DAG and run each step in topological order using the matching execution model (sequential, parallel, conditional, loop, map). See references/execution-engine.md.

5. After each agent completes, validate its output against the agent's schema and rules. On failure, apply the retry/timeout/failure policy (skip, abort, or fallback).

6. On completion, present results using the execution report template in references/visual-and-reporting.md. For partial or failed runs, report what completed, what failed, and any collected partial output.

Choosing a pattern

Match the task shape to a pattern, then scaffold from references/templates.md:

• Strict ordering of distinct steps: sequential chain.
• One input scored or analyzed from multiple angles: parallel fan-out/fan-in.
• Input routed by classification: conditional routing.
• Output must meet a quality bar: loop with a validator.
• Large input chunked and recombined: map-reduce.
• A step needs a backup approach on failure: pipeline with fallback.

See references/patterns.md for diagrams and examples of each.