Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

• Defining autonomous agents: key definitions and taxonomic classification
• The agent loop: the perceive, decide, act, and observe cycle
• Establishing design patterns for agent responsibilities and scope

Python Tooling and Agent SDKs

• Bootstrapping agents using LangChain and comparable SDKs
• Managing async programming, task queues, and subprocesses
• Ensuring reproducible development workflows via packaging and virtual environments

Integration of External Tools and APIs

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

Memory, State, and Context Management

• Optimizing short-term context windows and prompt engineering techniques
• Architecting long-term memory using Redis, vector stores, and retrieval augmentation
• Maintaining consistency, caching strategies, and memory hygiene

Orchestration, Planning, and Multi-Step Workflows

• Implementing action chaining, subagents, and task decomposition
• Comparing planning algorithms against heuristic orchestration
• Managing failures, implementing retries, and executing compensating actions

Safety, Testing, and Observability

• Developing threat models, conducting red-teaming, and sanitizing I/O
• Executing unit, integration, and end-to-end testing for agents
• Implementing logging, metrics, tracing, and alerting for agent behavior

Deployment, Scaling, and MLOps for Agents

• Implementing containerization, CI/CD pipelines, and rollout strategies
• Controlling costs, applying rate limiting, and optimizing resources
• Establishing monitoring, governance, and operational playbooks

Conclusion and Future Directions