Full course coming soon

Designing and Deploying AI Agent Workflows

7 chapters · 31 lessons

1. 1. Task Design for Agent Execution

   4 lessons

   Learn to decompose high-level goals into bounded, observable workflows agents can execute reliably.

2. 2. Tool Integration and Action Spaces

   5 lessons

   Design and implement tool interfaces that let agents call APIs, query databases, execute code, and interact with business systems.

3. 3. Agent Loop Architecture

   4 lessons

   Build the core reasoning loop that plans actions, observes results, updates state, and decides when to stop or escalate.

4. 4. Retrieval and Context Grounding

   4 lessons

   Integrate retrieval systems that let agents ground decisions in approved documents, policies, and knowledge bases.

5. 5. Evaluation and Testing Frameworks

   5 lessons

   Design test harnesses and evaluation metrics to measure agent reliability, correctness, and failure recovery.

6. 6. Guardrails and Autonomy Boundaries

   4 lessons

   Implement constraints, approval gates, and human-in-the-loop patterns that balance agent autonomy with safety and control.

7. 7. Production Deployment and Monitoring

   5 lessons

   Deploy agent systems with observability, incident response, and continuous improvement practices for real-world workflows.