Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

• Defining autonomous agents: concepts and classifications
• The agent loop: the cycle of perception, decision, action, and observation
• Establishing design patterns for agent responsibilities and boundaries

Python Tooling and Agent Frameworks

• Utilizing LangChain and comparable SDKs to initiate agent development
• Asynchronous programming, task queuing, and subprocess handling
• Managing packaging, virtual environments, and consistent development workflows

Connecting External Tools and APIs

• Crafting tool interfaces and secure invocation protocols
• Linking web APIs, databases, and internal service endpoints
• Handling credentials, secrets, and enforcing least-privilege access

Managing Memory, State, and Context

• Optimizing short-term context windows through prompt engineering
• Long-term memory structures: Redis, vector databases, and retrieval augmentation
• Ensuring consistency, caching techniques, and maintaining memory integrity

Orchestration, Planning, and Complex Workflows

• Sequencing actions, sub-agents, and breaking down tasks
• Comparing planning algorithms with heuristic orchestration methods
• Managing failures, implementing retries, and executing compensating actions

Safety, Validation, and Observability

• Developing threat models, red-teaming strategies, and input/output filtering
• Executing unit, integration, and end-to-end tests for agents
• Implementing logging, metrics, tracing, and alerts for agent behavior

Deployment, Scaling, and Agent MLOps

• Containerization, CI/CD pipelines, and phased rollout strategies
• Controlling costs, rate limiting, and optimizing resource usage
• Establishing monitoring, governance, and operational runbooks

Wrap-up and Future Directions