Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

• Defining autonomous agents: core concepts 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 comparable SDKs.
• Async programming, task queues, and subprocess management.
• Packaging, virtual environments, and reproducible development workflows.

Integration of External Tools and APIs

• Designing 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

• Managing short-term context windows and advanced prompt engineering.
• Long-term memory architectures: Redis, vector stores, and retrieval augmentation.
• Maintaining consistency, caching strategies, and memory hygiene.

Orchestration, Planning, and Multi-Step Workflows

• Chaining actions, managing subagents, and task decomposition.
• Comparing planning algorithms with heuristic orchestration.
• Handling failures, implementing retries, and compensating actions.

Safety, Testing, and Observability

• Threat modeling, red-teaming, and input/output sanitization.
• Unit, integration, and end-to-end testing strategies for agents.
• Logging, metrics, tracing, and alerting to monitor agent behavior.

Deployment, Scaling, and MLOps for Agents

• Containerization, CI/CD pipelines, and rollout strategies.
• Cost control, rate limiting, and resource optimization.
• Monitoring, governance, and establishing operational playbooks.

Summary and Next Steps