Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Agentic AI Fundamentals

• Defining autonomous agents: concepts and classification.
• The agent loop: the cycle of perception, decision-making, action, and observation.
• Design patterns for defining agent responsibilities and operational scope.

Python Ecosystem and Agent SDKs

• Utilizing LangChain and comparable SDKs to initialize agent development.
• Async programming, task queuing, and subprocess management.
• Best practices for packaging, virtual environments, and reproducible workflows.

External Tool and API Integration

• Crafting tool interfaces and secure invocation patterns.
• Establishing connections to web APIs, databases, and internal services.
• Managing credentials, secrets, and enforcing least-privilege access.

Memory, State, and Context Handling

• Managing short-term context windows and prompt engineering methods.
• Long-term memory structures: Redis, vector stores, and retrieval augmentation.
• Ensuring consistency, applying caching strategies, and maintaining memory hygiene.

Orchestration, Planning, and Multi-Step Processes

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

Safety, Testing, and Observability

• Threat modeling, red-teaming, and input/output sanitization.
• Conducting unit, integration, and end-to-end testing for agents.
• Implementing logging, metrics, tracing, and alerting for agent performance.

Deployment, Scaling, and MLOps for Agents

• Containerization, CI/CD pipeline integration, and rollout strategies.
• Controlling costs, applying rate limiting, and optimizing resources.
• Establishing monitoring, governance, and operational playbooks.

Conclusion and Future Directions