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agentic-workflow

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Agentic Workflow Pattern

Standard multi-agent pipeline for implementation tasks.

Architecture Principles

  • Use run_in_background: true for all agents to keep main context minimal
  • Use Task tool (never TaskOutput) to avoid receiving full agent transcripts
  • Agents write outputs to .claude/cache/agents/<stage>/ for injection into subsequent agents
  • Main conversation is pure orchestration — no heavy lifting, only coordination

Workflow Stages

1. Research Agent

Task(subagent_type="oracle", run_in_background=true, prompt="""
Query NIA Oracle (via /nia-docs skill) to verify approach and gather best practices.

Output to: .claude/cache/agents/oracle/<task>-research.md
""")
  • Enforce NIA as the research layer
  • Output: Research findings

2. Planning Agent

Task(subagent_type="plan-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/oracle/<task>-research.md
Use RP-CLI to analyze the target codebase section.
Generate implementation plan informed by research.

Output to: .claude/cache/agents/plan-agent/<task>-plan.md
""")
  • Receives: Research agent output as context
  • Output: Implementation plan

3. Validation Agent

Task(subagent_type="validate-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/plan-agent/<task>-plan.md
Read: .claude/cache/agents/oracle/<task>-research.md
Review plan against research findings and best practices.

Output to: .claude/cache/agents/validate-agent/<task>-validated.md
""")
  • Reviews plan against research
  • Output: Validated plan with amendments

4. Implementation Agent

Task(subagent_type="agentica-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/validate-agent/<task>-validated.md
Read: .claude/cache/agents/oracle/<task>-research.md

TDD approach: Write failing tests FIRST, then implement.
Run tests to verify.

Output summary to: .claude/cache/agents/implement-agent/<task>-implementation.md
""")
  • Receives: Validated plan + research context
  • TDD: Failing tests first
  • Output: Implementation + tests

5. Review Agent

Task(subagent_type="review-agent", run_in_background=true, prompt="""
Read: .claude/cache/agents/implement-agent/<task>-implementation.md
Read: .claude/cache/agents/validate-agent/<task>-validated.md
Read: .claude/cache/agents/oracle/<task>-research.md

Cross-reference implementation against plan and research.
Run tests to confirm passing.

Output to: .claude/cache/agents/review-agent/<task>-review.md
""")
  • Cross-references all artifacts
  • Confirms tests pass
  • Output: Review summary

Agent Progress Monitoring

# Watch for system reminders:
# "Agent a42a16e progress: 6 new tools used, 88914 new tokens"

# Poll for output files:
find .claude/cache/agents -name "*.md" -mmin -5

# Check task file size growth:
wc -c /tmp/claude/.../tasks/<id>.output

Stuck detection:

  1. Progress reminders stop arriving
  2. Task output file size stops growing
  3. Expected output file not created after reasonable time

Directory Structure

.claude/cache/agents/
├── oracle/
│   └── <task>-research.md
├── plan-agent/
│   └── <task>-plan.md
├── validate-agent/
│   └── <task>-validated.md
├── implement-agent/
│   └── <task>-implementation.md
└── review-agent/
    └── <task>-review.md

Key Rules

  1. Never use TaskOutput - floods context with 70k+ token transcripts
  2. Always run_in_background=true - isolates agent context
  3. File-based handoff - each agent reads previous agent's output file
  4. Poll, don't block - check file system for outputs, don't wait
  5. TDD in implementation - failing tests first, then make them pass

Source

  • Session 2026-01-01: SDK Phase 3 implementation using this pattern

Source

git clone https://github.com/parcadei/Continuous-Claude-v3/blob/main/.claude/skills/agentic-workflow/SKILL.mdView on GitHub

Overview

Agentic Workflow defines a repeatable, file-based multi-agent pipeline for implementing tasks. It separates research, planning, validation, implementation, and review into distinct agents, whose outputs are stored in .claude/cache/agents for seamless orchestration.

How This Skill Works

Each task runs through five stages with dedicated subagents: oracle for research, plan-agent for planning, validate-agent for validation, agentica-agent for implementation, and review-agent for final review. All agents run in the background and write their outputs to stage-specific files under .claude/cache/agents, enabling pure orchestration without heavy lifting in the main context. A key rule is to never use TaskOutput, ensuring modular, sequential handoffs between stages.

When to Use It

  • To structure complex implementation tasks with clear research, planning, and validation stages
  • When you need auditability and reproducibility through file-based handoffs
  • When you want a TDD-driven implementation flow with tests first
  • To decouple tasks using dedicated subagents (oracle, plan-agent, validate-agent, agentica-agent, review-agent)
  • When you need non-blocking orchestration and progress monitoring via cached artifacts

Quick Start

  1. Step 1: Trigger the Research Agent (oracle) to verify approach and write to .claude/cache/agents/oracle/<task>-research.md
  2. Step 2: Run the Planning Agent to generate a plan from the research output and save to .claude/cache/agents/plan-agent/<task>-plan.md
  3. Step 3: Execute Validation, Implementation (with TDD), and Review agents in sequence; each writes to its respective cache path and validates through tests

Best Practices

  • Enforce run_in_background=true for all agents
  • Store each stage output in its own .claude/cache/agents/<stage>/ path
  • Never use TaskOutput; rely on per-stage markdown artifacts for handoffs
  • Follow TDD in the implementation stage by writing failing tests first
  • Have the Review Agent cross-reference all artifacts and confirm tests pass

Example Use Cases

  • Implementing a new API endpoint by completing research, planning, validation, implementation, and review across agents
  • Refactoring a module with documented decisions and sanity checks via staged outputs
  • Integrating a third-party service with a guided, auditable multi-agent workflow
  • Extending a data pipeline with research-backed best practices and validated plans
  • Auditing a codebase through a structured, artifact-driven agent chain

Frequently Asked Questions

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