Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Core Concepts of Agentic AI

• Defining autonomous agents: concepts and classification
• The agent loop: the cycle of perceiving, deciding, acting, and observing
• Structuring design patterns for agent responsibilities and boundaries

Python Ecosystem and Agent SDKs

• Leveraging frameworks like LangChain to initialize agents
• Mastering async programming, task queues, and subprocess handling
• Managing packaging, virtual environments, and reproducible workflows

Connecting External Tools and APIs

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

Memory, State, and Context Handling

• Optimizing short-term context windows and prompt engineering
• Designing long-term memory systems using Redis, vector stores, and RAG
• Ensuring consistency, implementing caching, and maintaining memory hygiene

Orchestration, Planning, and Complex Workflows

• Implementing action chains, subagents, and task breakdowns
• Comparing planning algorithms with heuristic-based orchestration
• Managing failures, retries, and compensatory actions

Safety, Testing, and Observability

• Developing threat models, red-teaming, and input/output sanitization
• Conducting unit, integration, and end-to-end testing for agents
• Implementing logging, metrics, tracing, and alerts for agent performance

Deployment, Scalability, and Agent MLOps

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

Conclusion and Future Pathways