Agent Workflow Designer

Overview

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

references/workflow-patterns.md

Recommended Workflow

1. Select pattern based on dependency shape and risk profile.
2. Scaffold config via

   scripts/workflow_scaffolder.py

   .
3. Define handoff contract fields for every edge.
4. Add retry/timeouts and output validation gates.
5. 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

1. Start with the smallest pattern that can satisfy requirements.
2. Keep handoff payloads explicit and bounded.
3. Validate intermediate outputs before fan-in synthesis.
4. Enforce budget and timeout limits in every step.