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
• Design patterns for defining agent responsibilities and scope

Python Tooling and Agent SDKs

• Leveraging LangChain and comparable SDKs to initialize agents
• Asynchronous programming, task queuing, and subprocess management
• Packaging, virtual environments, and reproducible development workflows

External Tool and API Integration

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

Memory, State, and Context Management

• Short-term context windows and prompt engineering strategies
• 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
• Managing failures, implementing retries, and executing compensating actions

Safety, Testing, and Observability

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

Deployment, Scaling, and MLOps for Agents

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

Conclusion and Future Directions