TL;DR

We’ve extracted and documented 5 foundational agentic design patterns from a 482-page research PDF, creating 3,017 lines of actionable guidance for how AI agents should work. These patterns (ReAct, Planning, Reflection, Tool Use, Multi-Agent Collaboration) provide the structured decision-making foundation for Phase 1 of transforming .pip into a complete agentic development system.

The Problem: From Chaos to Structure

AI agents are powerful, but without structured approaches they can:

We needed to move from ad-hoc agent behavior to systematic, repeatable patterns that would enable .pip agents to deliver consistent, high-quality results.

The Solution: Design Patterns for Agents

Just as software engineering has design patterns (Factory, Observer, Strategy), agentic systems need their own patterns. We’ve documented five core patterns that address the most critical agent capabilities:

1. ReAct Pattern: Think, Then Act

The 4-step loop: Reason → Act → Observe → Reflect

This pattern prevents agents from blindly taking actions without thinking through the implications. Every action is preceded by reasoning, followed by observation of results, and reflection on whether to continue or adjust.

Example: CTO agent writing code

1. REASON: Need authentication. Use Supabase per tech stack.
2. ACT: Install @supabase/supabase-js
3. OBSERVE: Installed but TypeScript errors on missing env vars
4. REFLECT: Progress made but incomplete. Need env setup. ITERATE.

The agent catches the missing environment variables immediately rather than committing broken code.

2. Planning Pattern: Break It Down

The 5-stage process: Understand Goal → Decompose → Sequence → Execute → Validate

For complex tasks, agents need to plan before executing. This pattern ensures large features don’t become chaotic by forcing structured decomposition with clear dependencies.

Example: CPO planning a feature

DECOMPOSE:
1. CPO: Define requirements and wireframes
2. CTO: Implement backend + frontend
3. CISO: Security review
4. COO: Launch coordination
5. CMO: Blog post and communication

SEQUENCE: 1 → 2 → 3 → (4 + 5 parallel)

Each agent knows exactly what they’re responsible for and when.

3. Reflection Pattern: Learn from Experience

The 5-step cycle: Collect → Analyze → Identify → Document → Apply

Agents that don’t reflect repeat mistakes. This pattern formalizes retrospectives so every completed task generates documented lessons that improve future work.

Example: CTO reflecting on feature that ran over timeline

COLLECTION: Estimated 8 days, took 14 days (75% overrun)

ANALYSIS: Database migration underestimated (4h actual vs 1h expected)
ROOT CAUSE: No migration rehearsal in staging

LESSONS:
- STOP: Estimating migrations without rehearsal
- START: Mandatory migration rehearsal in staging
- CONTINUE: High unit test coverage (worked well)

APPLICATION: Created bin/rehearse-migration.sh script

VALIDATION: Next migration was 95% accurate (vs 40% before)

The agent doesn’t just document the problem—it implements a solution and validates it worked.

4. Tool Use Pattern: Extend Capabilities

The 6-step process: Identify Need → Select Tool → Configure → Execute → Validate → Chain

Agents shouldn’t try to do everything manually. This pattern helps them choose the right tools and use them effectively.

Example: CTO running tests before commit

1. IDENTIFY: Need to verify code changes don't break tests
2. SELECT: pnpm test (standard for this project)
3. CONFIGURE: pnpm test --run --coverage --reporter=verbose
4. EXECUTE: Run command, capture output
5. VALIDATE: 1 test failing, reveals real bug (not tool error)
6. CHAIN: Fix bug → Re-run tests → Commit when passing

The agent systematically uses tools rather than guessing at commands.

5. Multi-Agent Collaboration: Divide and Conquer

The 5-stage workflow: Decompose → Route → Execute → Coordinate → Integrate

Complex features require multiple agents with different expertise. This pattern ensures clean handoffs and coordination.

Example: Feature delivery

DECOMPOSE:
- CPO: Requirements (2 days)
- CTO: Implementation (6 days)
- CISO: Security review (1 day)
- COO: Launch (2 days)
- CMO: Blog post (1 day, parallel with CTO)

COORDINATE:
- Day 2: CPO → CTO handoff (requirements doc)
- Day 8: CTO → CISO handoff (PR for security review)
- Day 9: CISO → COO handoff (security approval)
- Day 11: COO launches, CMO publishes

INTEGRATE: All deliverables merged, feature launched ✓

No confusion about who’s responsible for what or when handoffs occur.

The Implementation: Actionable, Not Academic

These aren’t abstract theories—each pattern includes:

  1. Concrete examples with real code and commands
  2. Integration with .pip showing how CTO/CPO/COO agents use them
  3. Automation opportunities with example bash scripts
  4. When NOT to use guidance (patterns have trade-offs)
  5. Related patterns showing how they work together

For instance, the Tool Use pattern includes:

The Numbers

What we created:

Pattern breakdown:

How Patterns Work Together

The magic happens when patterns combine:

CTO implements feature:

1. PLANNING PATTERN
   - Decompose into subtasks
   - Sequence with dependencies

2. REACT PATTERN (for each subtask)
   - Reason about approach
   - Act on implementation
   - Observe test results
   - Reflect on progress

3. TOOL USE PATTERN (throughout)
   - git for version control
   - pnpm test for validation
   - Linear API for updates

4. MULTI-AGENT COLLABORATION
   - Receive from CPO
   - Hand to CISO
   - Hand to COO

5. REFLECTION PATTERN (after completion)
   - Analyze what worked
   - Document lessons
   - Apply improvements

Each pattern addresses a specific capability, but together they create a complete workflow.

What’s Next: Phase 1 Continues

This pattern extraction is the first major deliverable for Phase 1 (v1.1.0): Pattern Library & Resources.

Remaining Phase 1 work (per ROADMAP.md):

Then Phase 2: Vector Memory System where we’ll:

Why This Matters

These patterns transform .pip from “documentation framework with agent roles” to “agentic development system with structured intelligence.”

Before patterns: Agents worked but inconsistently After patterns: Agents follow proven workflows that improve over time

Before patterns: Knowledge lost after each task After patterns: Every task generates reusable lessons

Before patterns: Multi-agent coordination ad-hoc After patterns: Clear protocols for handoffs and integration

This is the foundation that enables everything else in the roadmap—you can’t build intelligent automation without structured decision-making patterns.

Resources

Pattern Library: resources/agentic-design-patterns/extracted-patterns/

Individual patterns:

Catalog: Pattern Library README

Roadmap: ROADMAP.md

Linear: MSTUDIO-56

Contributing

Have ideas for improving these patterns? Found a use case we didn’t cover? The patterns are living documents that will evolve through:

  1. Real-world usage in .pip and organism projects
  2. Deep PDF analysis adding specific page references
  3. Research updates from new agentic design publications
  4. Community feedback from teams using .pip

Submit issues or PRs to help improve the pattern library!

Phase 1 Progress: Pattern extraction complete ✓
Next: Agent workflows and decision frameworks
Timeline: Phase 1 targeting completion by end of Q1 2026
Vision: Transform .pip into complete agentic development system

The journey from documentation framework to agentic intelligence has begun. 🚀