Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Fundamentals of Agentic AI

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

Python Tooling and Agent SDKs

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

Integrating External Tools and APIs

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

Memory, State, and Context Management

• Managing 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

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

Safety, Testing, and Observability

• Developing threat models, red-teaming, and sanitizing inputs/outputs
• Conducting unit, integration, and end-to-end testing for agents
• Monitoring agent behavior through logging, metrics, tracing, and alerting

Deployment, Scaling, and MLOps for Agents

• Implementing containerization, CI/CD pipelines, and rollout strategies
• Optimizing costs, rate limiting, and resource usage
• Establishing monitoring, governance, and operational playbooks

Summary and Next Steps