Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

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

Python Tools and Agent SDKs

• Leveraging LangChain and comparable SDKs to initialize agents
• Asynchronous programming, task queuing, and subprocess control
• Software packaging, virtual environments, and reproducible development practices

Connecting External Tools and APIs

• Creating tool interfaces and secure invocation methods
• Linking with web APIs, databases, and internal services
• Managing credentials, secrets, and enforcing least-privilege access

Memory, State, and Context Handling

• Short-term context windows and prompt engineering strategies
• Long-term memory frameworks: Redis, vector stores, and retrieval augmentation
• Ensuring consistency, caching tactics, and memory maintenance

Orchestration, Planning, and Complex Workflows

• Sequencing actions, managing subagents, and task breakdown
• Comparing planning algorithms with heuristic orchestration
• Managing errors, implementing retries, and executing compensatory actions

Safety, Testing, and Monitoring

• Threat modeling, red-teaming, and input/output sanitization
• Conducting unit, integration, and end-to-end tests for agents
• Implementing logging, metrics, tracing, and alerting for agent performance

Deployment, Scaling, and Agent MLOps

• Containerization, CI/CD pipelines, and release strategies
• Managing costs, rate limiting, and optimizing resources
• Monitoring, governance, and establishing operational playbooks

Conclusion and Future Directions