zjunlp/agent-workflow

Agent Development Workflow

Complete Standard Operating Procedure (SOP) for building production-ready goal-driven agents.

Overview

This workflow orchestrates specialized skills to take you from initial concept to production-ready agent:

1. Understand Concepts → /building-agents-core (optional)
2. Build Structure → /building-agents-construction
3. Optimize Design → /building-agents-patterns (optional)
4. Setup Credentials → /setup-credentials (if agent uses tools requiring API keys)
5. Test & Validate → /testing-agent

When to Use This Workflow

Use this meta-skill when:

• Starting a new agent from scratch
• Unclear which skill to use first
• Need end-to-end guidance for agent development
• Want consistent, repeatable agent builds

Skip this workflow if:

• You only need to test an existing agent → use /testing-agent directly
• You know exactly which phase you're in → use specific skill directly

Quick Decision Tree

"Need to understand agent concepts" → building-agents-core
"Build a new agent" → building-agents-construction
"Optimize my agent design" → building-agents-patterns
"Set up API keys for my agent" → setup-credentials
"Test my agent" → testing-agent
"Not sure what I need" → Read phases below, then decide
"Agent has structure but needs implementation" → See agent directory STATUS.md

Phase 0: Understand Concepts (Optional)

Duration: 5-10 minutes Skill: /building-agents-core Input: Questions about agent architecture

When to Use

• First time building an agent
• Need to understand node types, edges, goals
• Want to validate tool availability
• Learning about pause/resume architecture

What This Phase Provides

• Architecture overview (Python packages, not JSON)
• Core concepts (Goal, Node, Edge, Pause/Resume)
• Tool discovery and validation procedures
• Workflow overview

Skip this phase if you already understand agent fundamentals.

Phase 1: Build Agent Structure

Duration: 15-30 minutes Skill: /building-agents-construction Input: User requirements ("Build an agent that...")

What This Phase Does

Creates the complete agent architecture:

• Package structure (exports/agent_name/)
• Goal with success criteria and constraints
• Workflow graph (nodes and edges)
• Node specifications
• CLI interface
• Documentation

Process

1. Create package - Directory structure with skeleton files
2. Define goal - Success criteria and constraints written to agent.py
3. Design nodes - Each node approved and written incrementally
4. Connect edges - Workflow graph with conditional routing
5. Finalize - Agent class, exports, and documentation

Outputs

• ✅ exports/agent_name/ package created
• ✅ Goal defined in agent.py
• ✅ 3-5 success criteria defined
• ✅ 1-5 constraints defined
• ✅ 5-10 nodes specified in nodes/init.py
• ✅ 8-15 edges connecting workflow
• ✅ Validated structure (passes python -m agent_name validate)
• ✅ README.md with usage instructions
• ✅ CLI commands (info, validate, run, shell)

Success Criteria

You're ready for Phase 2 when:

• Agent structure validates without errors
• All nodes and edges are defined
• CLI commands work (info, validate)
• You see: "Agent complete: exports/agent_name/"

Common Outputs

The building-agents-construction skill produces:

exports/agent_name/
├── __init__.py          (package exports)
├── __main__.py          (CLI interface)
├── agent.py             (goal, graph, agent class)
├── nodes/__init__.py    (node specifications)
├── config.py            (configuration)
├── implementations.py   (may be created for Python functions)
└── README.md            (documentation)

Next Steps

If structure complete and validated: → Check exports/agent_name/STATUS.md or IMPLEMENTATION_GUIDE.md → These files explain implementation options → You may need to add Python functions or MCP tools (not covered by current skills)

If want to optimize design: → Proceed to Phase 1.5 (building-agents-patterns)

If ready to test: → Proceed to Phase 2

Phase 1.5: Optimize Design (Optional)

Duration: 10-15 minutes Skill: /building-agents-patterns Input: Completed agent structure

When to Use

• Want to add pause/resume functionality
• Need error handling patterns
• Want to optimize performance
• Need examples of complex routing
• Want best practices guidance

What This Phase Provides

• Practical examples and patterns
• Pause/resume architecture
• Error handling strategies
• Anti-patterns to avoid
• Performance optimization techniques

Skip this phase if your agent design is straightforward.

Phase 2: Test & Validate

Duration: 20-40 minutes Skill: /testing-agent Input: Working agent from Phase 1

What This Phase Does

Creates comprehensive test suite:

• Constraint tests (verify hard requirements)
• Success criteria tests (measure goal achievement)
• Edge case tests (handle failures gracefully)
• Integration tests (end-to-end workflows)

Process

1. Analyze agent - Read goal, constraints, success criteria
2. Generate tests - Create pytest files in exports/agent_name/tests/
3. User approval - Review and approve each test
4. Run evaluation - Execute tests and collect results
5. Debug failures - Identify and fix issues
6. Iterate - Repeat until all tests pass

Outputs

• ✅ Test files in exports/agent_name/tests/
• ✅ Test report with pass/fail metrics
• ✅ Coverage of all success criteria
• ✅ Coverage of all constraints
• ✅ Edge case handling verified

Success Criteria

You're done when:

• All tests pass
• All success criteria validated
• All constraints verified
• Agent handles edge cases
• Test coverage is comprehensive

Next Steps

Agent ready for:

• Production deployment
• Integration into larger systems
• Documentation and handoff
• Continuous monitoring

Phase Transitions

From Phase 1 to Phase 2

Trigger signals:

• "Agent complete: exports/..."
• Structure validation passes
• README indicates implementation complete

Before proceeding:

• Verify agent can be imported: from exports.agent_name import default_agent
• Check if implementation is needed (see STATUS.md or IMPLEMENTATION_GUIDE.md)
• Confirm agent executes without import errors

Skipping Phases

When to skip Phase 1:

• Agent structure already exists
• Only need to add tests
• Modifying existing agent

When to skip Phase 2:

• Prototyping or exploring
• Agent not production-bound
• Manual testing sufficient

Common Patterns

Pattern 1: Complete New Build (Simple)

User: "Build an agent that monitors files"
→ Use /building-agents-construction
→ Agent structure created
→ Use /testing-agent
→ Tests created and passing
→ Done: Production-ready agent

Pattern 1b: Complete New Build (With Learning)

User: "Build an agent (first time)"
→ Use /building-agents-core (understand concepts)
→ Use /building-agents-construction (build structure)
→ Use /building-agents-patterns (optimize design)
→ Use /testing-agent (validate)
→ Done: Production-ready agent

Pattern 2: Test Existing Agent

User: "Test my agent at exports/my_agent"
→ Skip Phase 1
→ Use /testing-agent directly
→ Tests created
→ Done: Validated agent

Pattern 3: Iterative Development

User: "Build an agent"
→ Use /building-agents-construction (Phase 1)
→ Implementation needed (see STATUS.md)
→ [User implements functions]
→ Use /testing-agent (Phase 2)
→ Tests reveal bugs
→ [Fix bugs manually]
→ Re-run tests
→ Done: Working agent

Pattern 4: Complex Agent with Patterns

User: "Build an agent with multi-turn conversations"
→ Use /building-agents-core (learn pause/resume)
→ Use /building-agents-construction (build structure)
→ Use /building-agents-patterns (implement pause/resume pattern)
→ Use /testing-agent (validate conversation flows)
→ Done: Complex conversational agent

Skill Dependencies

agent-workflow (meta-skill)
    │
    ├── building-agents-core (foundational)
    │   ├── Architecture concepts
    │   ├── Node/Edge/Goal definitions
    │   ├── Tool discovery procedures
    │   └── Workflow overview
    │
    ├── building-agents-construction (procedural)
    │   ├── Creates package structure
    │   ├── Defines goal
    │   ├── Adds nodes incrementally
    │   ├── Connects edges
    │   ├── Finalizes agent class
    │   └── Requires: building-agents-core
    │
    ├── building-agents-patterns (reference)
    │   ├── Best practices
    │   ├── Pause/resume patterns
    │   ├── Error handling
    │   ├── Anti-patterns
    │   └── Performance optimization
    │
    └── testing-agent
        ├── Reads agent goal
        ├── Generates tests
        ├── Runs evaluation
        └── Reports results

Troubleshooting

"Agent structure won't validate"

• Check node IDs match between nodes/init.py and agent.py
• Verify all edges reference valid node IDs
• Ensure entry_node exists in nodes list
• Run: PYTHONPATH=core:exports python -m agent_name validate

"Agent has structure but won't run"

• Check for STATUS.md or IMPLEMENTATION_GUIDE.md in agent directory
• Implementation may be needed (Python functions or MCP tools)
• This is expected - building-agents-construction creates structure, not implementation
• See implementation guide for completion options

"Tests are failing"

• Review test output for specific failures
• Check agent goal and success criteria
• Verify constraints are met
• Use /testing-agent to debug and iterate
• Fix agent code and re-run tests

"Not sure which phase I'm in"

Run these checks:

# Check if agent structure exists
ls exports/my_agent/agent.py

# Check if it validates
PYTHONPATH=core:exports python -m my_agent validate

# Check if tests exist
ls exports/my_agent/tests/

# If structure exists and validates → Phase 2 (testing)
# If structure doesn't exist → Phase 1 (building)
# If tests exist but failing → Debug phase

Best Practices

For Phase 1 (Building)

1. Start with clear requirements - Know what the agent should do
2. Define success criteria early - Measurable goals drive design
3. Keep nodes focused - One responsibility per node
4. Use descriptive names - Node IDs should explain purpose
5. Validate incrementally - Check structure after each major addition

For Phase 2 (Testing)

1. Test constraints first - Hard requirements must pass
2. Mock external dependencies - Use mock mode for LLMs/APIs
3. Cover edge cases - Test failures, not just success paths
4. Iterate quickly - Fix one test at a time
5. Document test patterns - Future tests follow same structure

General Workflow

1. Use version control - Git commit after each phase
2. Document decisions - Update README with changes
3. Keep iterations small - Build → Test → Fix → Repeat
4. Preserve working states - Tag successful iterations
5. Learn from failures - Failed tests reveal design issues

Exit Criteria

You're done with the workflow when:

✅ Agent structure validates ✅ All tests pass ✅ Success criteria met ✅ Constraints verified ✅ Documentation complete ✅ Agent ready for deployment

Additional Resources

• building-agents-core: See .claude/skills/building-agents-core/SKILL.md
• building-agents-construction: See .claude/skills/building-agents-construction/SKILL.md
• building-agents-patterns: See .claude/skills/building-agents-patterns/SKILL.md
• testing-agent: See .claude/skills/testing-agent/SKILL.md
• Agent framework docs: See core/README.md
• Example agents: See exports/ directory

Summary

This workflow provides a proven path from concept to production-ready agent:

1. Learn with /building-agents-core → Understand fundamentals (optional)
2. Build with /building-agents-construction → Get validated structure
3. Optimize with /building-agents-patterns → Apply best practices (optional)
4. Test with /testing-agent → Get verified functionality

The workflow is flexible - skip phases as needed, iterate freely, and adapt to your specific requirements. The goal is production-ready agents built with consistent, repeatable processes.

Skill Selection Guide

Choose building-agents-core when:

• First time building agents
• Need to understand architecture
• Validating tool availability
• Learning about node types and edges

Choose building-agents-construction when:

• Actually building an agent
• Have clear requirements
• Ready to write code
• Want step-by-step guidance

Choose building-agents-patterns when:

• Agent structure complete
• Need advanced patterns
• Implementing pause/resume
• Optimizing performance
• Want best practices

Choose testing-agent when:

• Agent structure complete
• Ready to validate functionality
• Need comprehensive test coverage
• Debugging agent behavior