Select pattern based on dependency shape and risk profile.
Scaffold config via scripts/workflow_scaffolder.py.
Define handoff contract fields for every edge.
Add retry/timeouts and output validation gates.
Dry-run with small context budgets before scaling.
Common Pitfalls
Over-orchestrating tasks solvable by one well-structured prompt
Missing timeout/retry policies for external-model calls
Passing full upstream context instead of targeted artifacts
Ignoring per-step cost accumulation
Best Practices
Start with the smallest pattern that can satisfy requirements.
Keep handoff payloads explicit and bounded.
Validate intermediate outputs before fan-in synthesis.
Enforce budget and timeout limits in every step.
1---2name: agent-workflow-designer3description: Agent Workflow Designer4---56## Internal Safety Override78- Status: active for local use.9- Keep operation local by default. Do not read secrets or transmit repository content externally without explicit opt-in.10- Audit categories: none detected.1112# Agent Workflow Designer1314**Tier:** POWERFUL 15**Category:** Engineering 16**Domain:** Multi-Agent Systems / AI Orchestration1718---1920## Overview2122Design production-grade multi-agent workflows with clear pattern choice, handoff contracts, failure handling, and cost/context controls.2324## Core Capabilities2526- Workflow pattern selection for multi-step agent systems27- Skeleton config generation for fast workflow bootstrapping28- Context and cost discipline across long-running flows29- Error recovery and retry strategy scaffolding30- Documentation pointers for operational pattern tradeoffs3132---3334## When to Use3536- A single prompt is insufficient for task complexity37- You need specialist agents with explicit boundaries38- You want deterministic workflow structure before implementation39- You need validation loops for quality or safety gates4041---4243## Quick Start4445```bash46# Generate a sequential workflow skeleton47python3 scripts/workflow_scaffolder.py sequential --name content-pipeline4849# Generate an orchestrator workflow and save it50python3 scripts/workflow_scaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.json51```5253---5455## Pattern Map5657- `sequential`: strict step-by-step dependency chain58- `parallel`: fan-out/fan-in for independent subtasks59- `router`: dispatch by intent/type with fallback60- `orchestrator`: planner coordinates specialists with dependencies61- `evaluator`: generator + quality gate loop6263Detailed templates: `references/workflow-patterns.md`6465---6667## Recommended Workflow68691. Select pattern based on dependency shape and risk profile.702. Scaffold config via `scripts/workflow_scaffolder.py`.713. Define handoff contract fields for every edge.724. Add retry/timeouts and output validation gates.735. Dry-run with small context budgets before scaling.7475---7677## Common Pitfalls7879- Over-orchestrating tasks solvable by one well-structured prompt80- Missing timeout/retry policies for external-model calls81- Passing full upstream context instead of targeted artifacts82- Ignoring per-step cost accumulation8384## Best Practices85861. Start with the smallest pattern that can satisfy requirements.872. Keep handoff payloads explicit and bounded.883. Validate intermediate outputs before fan-in synthesis.894. Enforce budget and timeout limits in every step.
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