Agent Development Workflow

THIS IS AN EXECUTABLE WORKFLOW. DO NOT explore the codebase or read source files. ROUTE to the correct skill IMMEDIATELY.

When this skill is loaded, ALWAYS use the AskUserQuestion tool to present options:

Use AskUserQuestion with these options:
- "Build a new agent" → Then invoke /hive-create
- "Test an existing agent" → Then invoke /hive-test
- "Learn agent concepts" → Then invoke /hive-concepts
- "Optimize agent design" → Then invoke /hive-patterns
- "Set up credentials" → Then invoke /hive-credentials
- "Debug a failing agent" → Then invoke /hive-debugger
- "Other" (please describe what you want to achieve)

DO NOT: Read source files, explore the codebase, search for code, or do any investigation before routing. The sub-skills handle all of that.

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 → /hive-concepts (optional)
2. Build Structure → /hive-create
3. Optimize Design → /hive-patterns (optional)
4. Setup Credentials → /hive-credentials (if agent uses tools requiring API keys)
5. Test & Validate → /hive-test
6. Debug Issues → /hive-debugger (if agent fails at runtime)

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 /hive-test directly
• You know exactly which phase you’re in → use specific skill directly

Quick Decision Tree

"Need to understand agent concepts" → hive-concepts
"Build a new agent" → hive-create
"Optimize my agent design" → hive-patterns
"Need client-facing nodes or feedback loops" → hive-patterns
"Set up API keys for my agent" → hive-credentials
"Test my agent" → hive-test
"My agent is failing/stuck/has errors" → hive-debugger
"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)

Skill: /hive-concepts 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 event loop architecture and client-facing nodes

What This Phase Provides

• Architecture overview (Python packages, not JSON)
• Core concepts (Goal, Node, Edge, Event Loop, Judges)
• Tool discovery and validation procedures
• Workflow overview

Skip this phase if you already understand agent fundamentals.

Phase 1: Build Agent Structure

Skill: /hive-create Input: User requirements (“Build an agent that…”) or a template to start from

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 uv run 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 hive-create 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 (hive-patterns)

If ready to test: → Proceed to Phase 2

Phase 1.5: Optimize Design (Optional)

Skill: /hive-patterns Input: Completed agent structure

When to Use

• Want to add client-facing blocking or feedback edges
• Need judge patterns for output validation
• Want fan-out/fan-in (parallel execution)
• Need error handling patterns
• Want best practices guidance

What This Phase Provides

• Client-facing interaction patterns
• Feedback edge routing with nullable output keys
• Judge patterns (implicit, SchemaJudge)
• Fan-out/fan-in parallel execution
• Context management and spillover patterns
• Anti-patterns to avoid

Skip this phase if your agent design is straightforward.

Phase 2: Test & Validate

Skill: /hive-test Input: Working agent from Phase 1

What This Phase Does

Guides the creation and execution of a comprehensive test suite:

• Constraint tests
• Success criteria tests
• Edge case tests
• Integration tests

Process

1. Analyze agent – Read goal, constraints, success criteria
2. Generate tests – The calling agent writes pytest files in exports/agent_name/tests/ using hive-test guidelines and templates
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 /hive-create
→ Agent structure created
→ Use /hive-test
→ Tests created and passing
→ Done: Production-ready agent

Pattern 1b: Complete New Build (With Learning)

User: "Build an agent (first time)"
→ Use /hive-concepts (understand concepts)
→ Use /hive-create (build structure)
→ Use /hive-patterns (optimize design)
→ Use /hive-test (validate)
→ Done: Production-ready agent

Pattern 1c: Build from Template

User: "Build an agent based on the deep research template"
→ Use /hive-create
→ Select "From a template" path
→ Pick template, name new agent
→ Review/modify goal, nodes, graph
→ Agent exported with customizations
→ Use /hive-test
→ Done: Customized agent

Pattern 2: Test Existing Agent

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

Pattern 3: Iterative Development

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

Pattern 4: Agent with Review Loops and HITL Checkpoints

User: "Build an agent with human review and feedback loops"
→ Use /hive-concepts (learn event loop, client-facing nodes)
→ Use /hive-create (build structure with feedback edges)
→ Use /hive-patterns (implement client-facing + feedback patterns)
→ Use /hive-test (validate review flows and edge routing)
→ Done: Agent with HITL checkpoints and review loops

Skill Dependencies

hive (meta-skill)
    │
    ├── hive-concepts (foundational)
    │   ├── Architecture concepts (event loop, judges)
    │   ├── Node types (event_loop, function)
    │   ├── Edge routing and priority
    │   ├── Tool discovery procedures
    │   └── Workflow overview
    │
    ├── hive-create (procedural)
    │   ├── Creates package structure
    │   ├── Defines goal
    │   ├── Adds nodes (event_loop, function)
    │   ├── Connects edges with priority routing
    │   ├── Finalizes agent class
    │   └── Requires: hive-concepts
    │
    ├── hive-patterns (reference)
    │   ├── Client-facing interaction patterns
    │   ├── Feedback edges and review loops
    │   ├── Judge patterns (implicit, SchemaJudge)
    │   ├── Fan-out/fan-in parallel execution
    │   └── Context management and anti-patterns
    │
    ├── hive-credentials (utility)
    │   ├── Detects missing credentials
    │   ├── Offers auth method choices (Aden OAuth, direct API key)
    │   ├── Stores securely in ~/.hive/credentials
    │   └── Validates with health checks
    │
    ├── hive-test (validation)
    │   ├── Reads agent goal
    │   ├── Generates tests
    │   ├── Runs evaluation
    │   └── Reports results
    │
    └── hive-debugger (troubleshooting)
        ├── Monitors runtime logs (L1/L2/L3)
        ├── Identifies retry loops, tool failures
        ├── Categorizes issues (10 categories)
        └── Provides fix recommendations

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=exports uv run 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 – hive-create 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 /hive-test to debug and iterate
• Fix agent code and re-run tests

“Agent is failing at runtime”

• Use /hive-debugger to analyze runtime logs
• The debugger identifies retry loops, tool failures, and stalled execution
• Get actionable fix recommendations with code changes
• Monitor the agent in real-time during TUI sessions

“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=exports uv run 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

• hive-concepts: See .claude/skills/hive-concepts/SKILL.md
• hive-create: See .claude/skills/hive-create/SKILL.md
• hive-patterns: See .claude/skills/hive-patterns/SKILL.md
• hive-test: See .claude/skills/hive-test/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 /hive-concepts → Understand fundamentals (optional)
2. Build with /hive-create → Get validated structure
3. Optimize with /hive-patterns → Apply best practices (optional)
4. Configure with /hive-credentials → Set up API keys (if needed)
5. Test with /hive-test → Get verified functionality
6. Debug with /hive-debugger → Fix runtime issues (if needed)

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 hive-concepts when:

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

Choose hive-create when:

• Actually building an agent
• Have clear requirements
• Ready to write code
• Want step-by-step guidance
• Want to start from an existing template and customize it

Choose hive-patterns when:

• Agent structure complete
• Need client-facing nodes or feedback edges
• Implementing review loops or fan-out/fan-in
• Want judge patterns or context management
• Want best practices

Choose hive-test when:

• Agent structure complete
• Ready to validate functionality
• Need comprehensive test coverage
• Testing feedback loops, output keys, or fan-out

Choose hive-debugger when:

• Agent is failing or stuck at runtime
• Seeing retry loops or escalations
• Tool calls are failing
• Need to understand why a node isn’t completing
• Want real-time monitoring of agent execution