Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

• Defining autonomous agents: concepts and classification.
• The agent loop: understanding the perceive, decide, act, and observe cycle.
• Designing patterns for agent responsibilities and scope.

Python Tooling and Agent SDKs

• Utilizing LangChain and comparable SDKs to initialize agents.
• Asynchronous programming, task queues, and subprocess control.
• Packaging, virtual environments, and reproducible development workflows.

Integrating External Tools and APIs

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

Memory, State, and Context Management

• Short-term context windows and prompt engineering strategies.
• Long-term memory architectures using Redis, vector stores, and retrieval augmentation.
• Maintaining consistency, caching strategies, and memory hygiene.

Orchestration, Planning, and Multi-Step Workflows

• Chaining actions, subagents, and task decomposition.
• Planning algorithms versus heuristic orchestration.
• Managing failures, retries, and compensating actions.

Safety, Testing, and Observability

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

Deployment, Scaling, and MLOps for Agents

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

Summary and Next Steps