Full course coming soon

Agent System Design: Building Reliable AI Decision Loops

7 chapters · 32 lessons

1. 1. Agent Control Loops and System Boundaries

   4 lessons

   Understand agent architecture fundamentals and how to decompose AI applications into observe-decide-act cycles.

2. 2. State Management and Context Tracking

   4 lessons

   Design state representations that capture user goals, prior actions, tool results, and constraints across agent iterations.

3. 3. Structured and Typed Output Design

   5 lessons

   Move beyond free-form text to constrained, parseable outputs that integrate cleanly with software systems.

4. 4. Tool Orchestration and External Integration

   5 lessons

   Connect agents to databases, APIs, search systems, and business tools with reliable call patterns and error handling.

5. 5. Guardrails, Validation, and Safety Layers

   4 lessons

   Implement input validation, output checks, policy enforcement, and escalation rules to prevent unsafe agent behavior.

6. 6. Testing, Observability, and Debugging

   5 lessons

   Build testable agent systems with clear metrics, logging, and debugging workflows for production reliability.

7. 7. Production Patterns and Deployment

   5 lessons

   Deploy agent systems with considerations for latency, cost, auditability, and real-world operational constraints.