AI Orchestration Patterns

Production-ready implementations of 8 AI orchestration patterns — from deterministic workflows to autonomous multi-agent systems.

Each pattern uses the same use case (tennis court booking) to highlight architectural differences, not domain complexity.

📖 Reference

This repo implements the patterns described in my blog post:

AI Orchestration Deep Dive: From No-Agent to Multi-Agent and Beyond

Read the blog first for architecture diagrams, trade-offs, and decision guides.

Patterns

Implementation Status

✅ Local Patterns: A → B → C → D → E → F → G → H (all done!)
☁️ AWS Deployed:   A → B → C → D → E → F → G → H (live on Lambda)

The Spectrum

Control ←——————————————————————————————————————————→ Autonomy

  A       B       C       D       E       F       G       H
  │       │       │       │       │       │       │       │
  No    Workflow Workflow Function Single Multi  Multi  Bedrock
 Agent  (Single) (Multi)  Calling  Agent  Agent  Agent  (Managed)
  │       │       │       │       │       │       │       │
 You    Fixed   Fixed    LLM     Agent  Manager Manager  AWS
control steps   steps  suggests controls routes  routes manages
 all   (single) (multi)  you      loop
                        control

Live Demo

Try the deployed patterns (AWS Lambda + API Gateway):

Tech Stack

• Language: Python
• Package Manager: UV
• AI Providers: OpenAI, Anthropic Claude, AWS Bedrock
• Agent Framework: OpenAI Agents SDK
• API: FastAPI
• Infrastructure: AWS Lambda, Terraform

Use Case

All patterns implement a tennis court booking system:

check_availability(date, time) → returns available slots
book(slot_id, user_id)         → reserves a slot

The difference: who decides which function to call and when.

Repo Structure

The repository is organized into three main areas:

• pattern-*/ folders - Each contains a complete implementation of one orchestration pattern with source code, dependencies, and sequence diagrams
• terraform/ folder - AWS infrastructure (Lambda + API Gateway) to deploy each pattern. One subfolder per pattern.
• shared/ folder - Common booking service logic reused across all patterns to keep the focus on orchestration differences, not business logic

ai-orchestration-patterns/
├── README.md
├── pattern-a-ai-as-service/
├── pattern-b-workflow-single-process/
├── pattern-c-workflow-multi-process/
├── pattern-d-function-calling/
├── pattern-e-single-agent/
├── pattern-f-multi-agent-single-process/
├── pattern-g-multi-agent-multi-process/
├── pattern-h-bedrock-agent/
├── scripts/
│   ├── package_lambda.py     # Build tool for patterns A-F
│   └── requirements-lambda.txt
├── shared/
│   └── booking_service.py    # Mock booking service (all patterns)
└── terraform/                # Infrastructure (Lambda + API Gateway)
    ├── pattern_a/
    ├── pattern_b/
    ├── ...
    └── pattern_h/

Getting Started

Prerequisites

Required:

• Python 3.12+
• UV (package manager)
• Docker (for AWS Lambda builds)

For AWS deployment:

• AWS CLI configured
• Terraform 1.5+
• OpenAI API key (patterns A-G)
• AWS account with Bedrock access (pattern H)

Local Development

Run patterns locally without AWS:

# Install dependencies
cd pattern-d-function-calling
uv sync

# Run demo
uv run src/demo.py

AWS Deployment

Step 1: Configure Secrets

# Create .env file (gitignored)
cat > .env << EOF
OPENAI_API_KEY=sk-...
EOF

Step 2: Build Lambda Package

For Patterns A-F (single Lambda):

python scripts/package_lambda.py pattern-a-ai-as-service

For Pattern G (3 Lambdas - manager, availability, booking):

cd pattern-g-multi-agent-multi-process
./build.sh

For Pattern H (2 Lambdas - action, invoker):

cd pattern-h-bedrock-agent
./build.sh

Step 3: Deploy with Terraform

cd terraform/pattern_a

# First time: copy example config
cp terraform.tfvars.example terraform.tfvars

# Edit terraform.tfvars:
# - Add your OpenAI API key
# - (Pattern H only) Choose foundation model

# Deploy
terraform init
terraform apply

Step 4: Test Deployment

# Get endpoint
terraform output api_endpoint

# Test health
curl $(terraform output -raw api_endpoint)/health

# Test chat
curl -X POST $(terraform output -raw api_endpoint)/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Book tomorrow at 3pm"}'

Pattern-Specific Notes

Pattern H (Bedrock Agent):

• Uses AWS Nova Pro by default (no agreement required)
• Alternative models require Bedrock model access:
  1. AWS Console → Bedrock → Model access
  2. Enable desired model (e.g., Claude Haiku 4.5)
  3. Update foundation_model in terraform.tfvars

Why This Repo?

• Real infrastructure — not just pseudocode
• Clear progression — deterministic → controlled → autonomous
• Trade-off analysis — when to use each pattern
• Production patterns — what actually works in enterprise

Author

Moss Gu

• 🌐 Blog
• 💼 LinkedIn
• 🐙 GitHub

License

MIT