Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

• Defining autonomous agents: concepts and classification
• The agent loop: the perceive, decide, act, and observe cycle
• Establishing design patterns for agent responsibilities and boundaries

Python Tools and Agent SDKs

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

Connecting External Tools and APIs

• Creating tool interfaces and implementing safe invocation methods
• Linking to web APIs, databases, and internal services
• Administering credentials, secrets, and least-privilege access controls

Managing Memory, State, and Context

• Optimizing short-term context windows and prompt engineering strategies
• Building long-term memory systems using Redis, vector stores, and retrieval augmentation
• Ensuring consistency, optimizing caching, and maintaining memory hygiene

Orchestration, Planning, and Multi-Step Processes

• Chaining actions, utilizing subagents, and decomposing tasks
• Comparing planning algorithms with heuristic orchestration
• Managing failures, retries, and compensatory actions

Safety, Testing, and Observability

• Developing threat models, red-teaming, and sanitizing inputs/outputs
• Conducting unit, integration, and end-to-end testing for agents
• Implementing logging, metrics, tracing, and alerts for agent actions

Deployment, Scaling, and MLOps for Agents

• Containerization, CI/CD pipelines, and deployment strategies
• Controlling costs, applying rate limits, and optimizing resources
• Monitoring, governance, and operational playbooks

Recap and Future Directions