Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Core Concepts of Agentic AI

• Defining autonomous agents: conceptual frameworks and classification
• The agent loop: understanding the perceive, decide, act, and observe cycle
• Establishing design patterns for agent responsibilities and operational scope

Python Tooling and Agent SDKs

• Leveraging LangChain and comparable SDKs to initiate agent development
• Implementing async programming, task queues, and subprocess handling
• Managing packaging, virtual environments, and reproducible development workflows

External Tool and API Integration

• Structuring tool interfaces and establishing secure invocation patterns
• Linking to web APIs, database systems, and internal services
• Oversight of credentials, secrets, and least-privilege access protocols

Memory, State, and Context Management

• Optimizing short-term context windows and refining prompt engineering techniques
• Designing long-term memory structures using Redis, vector stores, and retrieval augmentation
• Maintaining consistency, implementing caching strategies, and ensuring memory hygiene

Orchestration, Planning, and Multi-Step Workflows

• Executing action chains, managing subagents, and decomposing complex tasks
• Evaluating planning algorithms against heuristic orchestration methods
• Managing failure scenarios, retries, and compensatory actions

Safety, Testing, and Observability

• Developing threat models, conducting red-teaming, and sanitizing inputs/outputs
• Implementing unit, integration, and end-to-end testing strategies for agents
• Establishing logging, metrics, tracing, and alerting for agent behavior monitoring

Deployment, Scaling, and Agent MLOps

• Implementing containerization, CI/CD pipelines, and strategic rollout methods
• Controlling costs, applying rate limits, and optimizing resource usage
• Maintaining monitoring standards, governance, and operational playbooks

Wrap-Up and Future Directions