| name | agency-workflow-patterns |
| description | Master orchestration patterns, multi-agent coordination, and effective workflow composition using the Agency plugin's 51+ specialized agents. Activate when planning complex implementations, coordinating multiple agents, or optimizing development workflows. |
| triggers | agency patterns, workflow orchestration, using agency, agent coordination, how to use orchestrator, multi-agent workflow, parallel execution, agency best practices |
Agency Workflow Patterns
Master the art of orchestrating 51+ specialized agents to automate your complete software development lifecycle. This skill teaches you when to use the orchestrator, how to coordinate multiple agents, and patterns for maximum efficiency.
Core Orchestration Principles
When to Use the Orchestrator
Use orchestrator for:
- Multi-step workflows spanning multiple domains
- Complex tasks requiring different specialist expertise
- Tasks where parallel execution provides value
- Workflows needing strategic planning before execution
Use specialist agents directly for:
- Single-domain tasks with clear scope
- Quick focused work (< 30 minutes)
- Tasks where you know exactly which specialist to use
The Orchestration Flow
User Command → Orchestrator → Planning → Specialist Selection → Parallel Execution → Synthesis
- Intake: Orchestrator classifies request type
- Planning: Decomposes into discrete, delegatable tasks
- Selection: Matches tasks to optimal specialists
- Execution: Spawns agents (parallel when possible)
- Synthesis: Collects outputs and presents unified results
Common Workflow Patterns
Pattern 1: Investigation & Research
Use Case: Understanding existing code or systems
Flow:
User Question → Orchestrator → Explore Agent → Summary
Example:
- User: "How does authentication work in this codebase?"
- Orchestrator spawns
Exploreagent - Explore agent finds relevant files, traces flow
- Returns summary with key file references
Key Insight: Use Explore agent to keep main context clean
Pattern 2: Issue Implementation (Full Cycle)
Use Case: GitHub/Jira issue from start to finish
Flow:
Issue → Fetch Details → Plan → Review Plan → Implement → Test → Review → PR
Agents Involved:
- Orchestrator (coordination)
- Plan agent (planning phase)
- Specialist reviewer (architecture review)
- Specialist coder (implementation)
- Reality Checker (quality verification)
Example Command: /agency:work 123
Checkpoints:
- After planning (confirm approach)
- After implementation (verify changes)
- Before PR creation (final approval)
See Full Issue Implementation Flow for details.
Pattern 3: Parallel Implementation
Use Case: Multiple independent features or bug fixes
Flow:
Identify Parallel Tasks → Spawn Specialists Simultaneously → Integrate Results
Example:
- User: "Implement user dashboard with charts, filters, and export"
- Orchestrator identifies 3 independent tracks:
- Track A: Frontend Developer → Chart components
- Track B: Frontend Developer → Filter components
- Track C: Backend Architect → Export API
- All spawn simultaneously
- Integrate when complete
Benefits:
- 3x faster than sequential
- Specialists work in isolation
- Clean context separation
See Parallel Execution Strategies for details.
Pattern 4: Quality Review Pipeline
Use Case: Comprehensive code review before release
Flow:
Code → Security Review → Quality Review → Performance Review → Test Review → Synthesis
Agents in Parallel:
- Legal Compliance Checker (security)
- Reality Checker (quality & bugs)
- Performance Benchmarker (performance)
- Test Results Analyzer (test coverage)
Example Command: /agency:review
Output: Unified report with all findings
Pattern 5: Sprint Automation
Use Case: Implement entire sprint
Flow:
Fetch Sprint Issues → Prioritize → Find Parallel Work → Execute in Batches → Monitor Progress
Example Command: /agency:gh-sprint
Orchestrator Actions:
- Fetch all sprint issues
- Analyze dependencies
- Group into parallel batches
- Execute batches sequentially
- Report progress and blockers
See Sprint Automation Workflow for details.
Agent Selection Guidelines
Engineering Agents
| Agent | Best For | Avoid For |
|---|---|---|
| Frontend Developer | React, Vue, UI components | Backend logic, databases |
| Backend Architect | APIs, databases, system design | UI implementation |
| AI Engineer | ML pipelines, model integration | General web development |
| DevOps Automator | CI/CD, Docker, infrastructure | Application code |
| Senior Developer | Complex logic, difficult problems | Simple CRUD |
| Rapid Prototyper | MVPs, proof-of-concepts | Production systems |
Design Agents
| Agent | Best For | Avoid For |
|---|---|---|
| UI Designer | Visual design, component libraries | User research |
| UX Researcher | User needs, usability testing | Visual design |
| UX Architect | Information architecture, flows | Implementation |
Testing Agents
| Agent | Best For | Avoid For |
|---|---|---|
| Reality Checker | Bug finding, edge cases | Performance testing |
| API Tester | API contract validation | UI testing |
| Performance Benchmarker | Load testing, optimization | Functional testing |
See Complete Agent Roster for all 51+ agents.
Context Management
Keep Orchestrator Context Lean
Retain:
- Task status (pending/in-progress/complete)
- Output artifact locations (file paths)
- Key decisions made
- Blockers requiring user input
Discard:
- Full file contents from agents
- Implementation details (live in agent outputs)
- Verbose logs and debug output
Target Context Sizes:
- Investigation: < 2K tokens
- Quick Fix: < 1K tokens
- Feature Plan: < 4K tokens
- Full Implementation: < 6K tokens
Why This Matters
Benefits of Lean Context:
- Faster responses
- Lower costs
- Better focus
- Can handle more complex workflows
How to Achieve:
- Use
Exploreagent for research - Agents return summaries, not full outputs
- File paths instead of contents
- Key decisions, not reasoning traces
Parallel vs Sequential Execution
When to Execute in Parallel
✅ Parallelize When:
- Tasks are independent (no shared state)
- Different domains/specialists
- No sequential dependencies
- Time-sensitive work
Example: UI components + API endpoints + Database schema
When to Execute Sequentially
⛔ Sequential When:
- Tasks have dependencies (A must finish before B)
- Shared state mutations
- Need output from previous task
- Risk of conflicts
Example: Plan → Review Plan → Implement → Test
See Execution Strategies for decision trees.
Checkpoint Strategy
User Approval Points
Always pause for approval:
- After planning phase (before implementation)
- Before destructive operations (database migrations)
- When ambiguity detected
- When user explicitly requested ("plan only")
Skip approval for:
- Simple fixes (< 20 LOC)
- User said "just do it" or "auto-approve"
- Similar patterns previously approved
Present concisely:
📋 Plan: Dark Mode Implementation
I'll use 4 agents across 3 phases:
Phase 1: Research
└── Explore → Find existing theme patterns
Phase 2: Implementation (Parallel)
├── Frontend Developer → Theme provider + toggle
├── Frontend Developer → Component style updates
└── Backend Architect → User preference API
Phase 3: Verification
└── Reality Checker → Visual regression tests
Checkpoints: After Phase 1 (confirm approach), After Phase 3 (final review)
Approve? (y/auto/modify)
Advanced Patterns
Pattern: Iterative Refinement
Use Case: Complex feature needing multiple refinement cycles
Flow:
Initial Plan → Review → Refine Plan → Implement → Test → Refine → Test → Ship
When to Use: Novel features, unclear requirements, high uncertainty
Pattern: Research-First Implementation
Use Case: Working with unfamiliar codebase or technology
Flow:
Research → Document Findings → Plan → Implement
Research Phase:
- Explore agent finds relevant code
- Documents patterns and conventions
- Identifies dependencies
- Creates knowledge base
Then: Implementation uses research as foundation
Pattern: Specialist Cascade
Use Case: Progressive deepening of expertise
Flow:
Senior Developer (initial) → Specialist (refinement) → Another Specialist (integration)
Example:
- Senior Developer: Initial implementation
- Performance Benchmarker: Optimization pass
- Security Compliance: Security hardening
See Advanced Orchestration for more patterns.
Troubleshooting
Issue: Too Many Context Switches
Symptom: Orchestrator slow, losing track Solution: Reduce parallel agents, increase batch size
Issue: Wrong Agent Selected
Symptom: Agent struggles with task Solution: Review agent selection criteria, may need different specialist
Issue: Workflow Stalled
Symptom: Agent waiting for input, unclear blocking Solution: Check for missing user approval, clarify requirements
See Troubleshooting Guide for common issues.
Best Practices
- Start with Planning: Always create plan before implementation
- Use Orchestrator for Complexity: Let orchestrator handle multi-step workflows
- Parallelize Aggressively: Identify independent work
- Keep Context Lean: Use agents to isolate work
- Checkpoint Strategically: Pause for approval at key points
- Match Agent to Task: Use right specialist for the job
- Document Decisions: Capture key choices in artifacts
- Iterate on Feedback: Refine based on results
Quick Reference
Investigation: Explore agent → summary
Issue Implementation: /agency:work {number}
Sprint Automation: /agency:gh-sprint
Code Review: /agency:review
Parallel Work: /agency:gh-parallel
Planning Only: /agency:plan
Related Skills
github-workflow-best-practices- GitHub-specific workflowscode-review-standards- Review criteria and quality gatestesting-strategy- Testing approaches and standards
Remember: The orchestrator is a strategic coordinator, not an executor. Let specialists do the work while orchestrator manages the big picture.