Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Fundamentals of Agentic AI

• Understanding autonomous agents: definitions and taxonomy.
• The agent loop: the perceive, decide, act, observe cycle.
• Design patterns for defining agent responsibilities and scope.

Python Tooling and Agent SDKs

• Leveraging LangChain and similar SDKs to bootstrap agents.
• Async programming, task queues, and subprocess management.
• Packaging, virtual environments, and establishing reproducible development workflows.

Integrating External Tools and APIs

• Designing tool interfaces and implementing safe invocation patterns.
• Connecting to web APIs, databases, and internal services.
• Managing credentials, secrets, and ensuring least-privilege access.

Memory, State, and Context Management

• Managing short-term context windows and employing prompt engineering techniques.
• Architecting long-term memory: Redis, vector stores, and retrieval augmentation.
• Ensuring consistency, optimizing caching strategies, and maintaining memory hygiene.

Orchestration, Planning, and Multi-Step Workflows

• Chaining actions, managing subagents, and decomposing tasks.
• Comparing planning algorithms with heuristic orchestration approaches.
• Handling failures, implementing retries, and executing compensating actions.

Safety, Testing, and Observability

• Developing threat models, conducting red-teaming, and sanitizing input/output.
• Conducting unit, integration, and end-to-end testing for agents.
• Implementing logging, metrics, tracing, and alerting for agent behavior.

Deployment, Scaling, and MLOps for Agents

• Utilizing containerization, CI/CD pipelines, and defining rollout strategies.
• Managing cost control, rate limiting, and resource optimization.
• Establishing monitoring, governance, and operational playbooks.

Summary and Next Steps