agent-workflow-designer

name agent-workflow-designer description Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs. Agent Workflow Designer Tier: POWERFUL Category: Engineering Domain: Multi-Agent Systems / AI Orchestration Overview Design production-grade multi-agent workflows with clear pattern choice, handoff contracts, failure handling, and cost/context controls. Core Capabilities Workflow pattern selection for multi-step agent systems Skeleton config generation for fast workflow bootstrapping Context and cost discipline across long-running flows Error recovery and retry strategy scaffolding Documentation pointers for operational pattern tradeoffs When to Use A single prompt is insufficient for task complexity You need specialist agents with explicit boundaries You want deterministic workflow structure before implementation You need validation loops for quality or safety gates Quick Start # Generate a sequential workflow skeleton python3 scripts/workflow_scaffolder.py sequential --name content-pipeline # Generate an orchestrator workflow and save it python3 scripts/workflow_scaffolder.py orchestrator --name incident-triage --output workflows/incident-triage.json Pattern Map sequential : strict step-by-step dependency chain parallel : fan-out/fan-in for independent subtasks router : dispatch by intent/type with fallback orchestrator : planner coordinates specialists with dependencies evaluator : generator + quality gate loop Detailed templates: references/workflow-patterns.md Recommended Workflow 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.