Three AI Agent Architecture Patterns That Actually Work in Production

Building AI agents that work reliably in production requires more than just connecting an LLM to your application. After working with dozens of AI agent implementations, three architectural patterns consistently deliver results: reactive agents, planning agents, and multi-agent systems.

Let's examine each pattern with concrete examples and implementation guidance.

Pattern 1: Reactive Agents

When to use: Simple, deterministic tasks with clear input-output relationships.

Reactive agents respond directly to user input without maintaining complex state or planning ahead. They excel at focused, single-turn interactions.

Architecture Components

Input processor: Validates and normalizes user requests
Tool selector: Chooses appropriate tools based on input
Execution engine: Runs selected tools and formats responses
Response formatter: Structures output for the user

Example: Code Review Agent

A code review agent that analyzes pull requests:

User Input → Input Processor → Tool Selection → Execute Tools → Format Response
    ↓              ↓              ↓              ↓              ↓
"Review this    Parse diff    [Static        Run analysis   Generate
 Python code"   Extract       analysis,      tools in       formatted
               language      Security       parallel       review
                            scan]

Implementation considerations:

Keep tool inventory small and focused
Implement robust error handling for tool failures
Cache common analysis results
Use structured output formats (JSON, markdown tables)

Pattern 2: Planning Agents

When to use: Multi-step tasks requiring sequential reasoning or when the solution path isn't immediately obvious.

Planning agents break down complex requests into executable steps. They maintain state across interactions and can adapt their approach based on intermediate results.

Architecture Components

Task planner: Decomposes requests into steps
Execution tracker: Monitors progress and manages state
Tool orchestrator: Executes planned steps
Plan adapter: Modifies plans based on results

Example: Data Pipeline Agent

An agent that builds data transformation pipelines:

1. Planning Phase:
   User: "Transform customer data from CSV to analytics format"
   Plan: [Analyze schema] → [Design transformations] → [Generate code] → [Test pipeline]

2. Execution Phase:
   Step 1: Schema analysis tool examines CSV structure
   Step 2: Transformation designer creates mapping rules
   Step 3: Code generator produces transformation script
   Step 4: Validator tests pipeline with sample data

3. Adaptation:
   If Step 4 fails → Revise transformations → Regenerate code → Retest

Implementation considerations:

Design plans as modifiable data structures
Implement checkpointing for long-running tasks
Build retry mechanisms for failed steps
Log plan evolution for debugging

Pattern 3: Multi-Agent Systems

When to use: Complex domains requiring specialized expertise or when tasks benefit from collaborative approaches.

Multi-agent systems deploy specialized agents that collaborate on complex problems. Each agent handles specific aspects of the overall task.

Architecture Components

Agent registry: Manages available agents and their capabilities
Task dispatcher: Routes work to appropriate agents
Communication layer: Enables inter-agent messaging
Coordination engine: Manages workflows and dependencies

Example: Software Development Team

A system with specialized development agents:

Project Manager Agent
├── Requirements Analyst Agent
├── Architecture Agent
├── Implementation Agent
│   ├── Frontend Specialist
│   └── Backend Specialist
├── Testing Agent
└── Documentation Agent

Workflow:
Requirements → Architecture → Implementation → Testing → Documentation
     ↓             ↓              ↓           ↓           ↓
User stories   System design   Code files   Test suites  User guides

Communication patterns:

Direct messaging: Agent A sends specific requests to Agent B
Broadcast updates: Agents notify others about state changes
Shared workspace: Agents collaborate through shared data structures

Implementation considerations:

Define clear agent responsibilities and interfaces
Implement message queuing for reliable communication
Build monitoring for agent health and performance
Design fallback mechanisms when agents become unavailable

Choosing the Right Pattern

Start with Reactive Agents when:

Tasks are well-defined and predictable
Response time is critical
You need high reliability
The problem domain is narrow

Move to Planning Agents when:

Tasks require multiple steps
You need to handle edge cases gracefully
The solution path varies based on input
Users expect progress updates

Consider Multi-Agent Systems when:

Tasks require diverse expertise
You want to scale different capabilities independently
Complex workflows benefit from specialization
You need to maintain multiple concurrent sessions

Implementation Lessons

Error Handling

Every pattern needs robust error handling, but the approaches differ:

Reactive: Fail fast with clear error messages
Planning: Retry with modified plans
Multi-agent: Route around failed agents

Observability

Instrument your agents for production debugging:

Log all tool invocations and results
Track execution time and resource usage
Monitor failure rates by task type
Capture user satisfaction metrics

Testing Strategies

Unit tests: Verify individual tools work correctly
Integration tests: Test agent workflows end-to-end
Load tests: Ensure agents handle concurrent requests
A/B tests: Compare agent performance against baselines

Conclusion

These three patterns cover most production AI agent scenarios. Start simple with reactive agents, evolve to planning agents when complexity demands it, and consider multi-agent systems only when specialization provides clear benefits.

The key to success isn't choosing the most sophisticated pattern—it's matching the pattern to your specific use case and implementing it with production-grade reliability.