From Prototype to Production
Building a demo AI agent is easy. Building one that handles edge cases, scales reliably, and earns user trust is another challenge entirely. This guide covers the patterns we've learned from deploying agentic systems across industries.
Architecture Patterns
The Orchestrator Pattern
A central orchestrator agent decomposes tasks and delegates to specialized sub-agents. This provides clear separation of concerns and makes debugging straightforward.
The Pipeline Pattern
Agents are chained in a directed graph where each agent's output becomes the next agent's input. Best for well-defined, sequential workflows.
The Swarm Pattern
Multiple agents collaborate dynamically, sharing context and negotiating task allocation. Ideal for complex, emergent problem-solving.
Testing Strategies
- Scenario testing: Define expected outcomes for representative scenarios
- Adversarial testing: Deliberately provide ambiguous or contradictory inputs
- Regression testing: Ensure agent improvements don't break existing capabilities
- Human evaluation: Regular review of agent outputs by domain experts
Deployment Considerations
Production agents need robust monitoring, graceful degradation, and clear escalation paths to human operators.
