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
• Establishing design patterns for agent scope and responsibilities

Python Ecosystem and Agent SDKs

• Leveraging LangChain and comparable SDKs to initialize agents
• Async programming, task queuing, and subprocess handling
• Package management, virtual environments, and reproducible development practices

Connecting External Tools and APIs

• Crafting tool interfaces and secure invocation mechanisms
• Linking to web APIs, databases, and proprietary internal services
• Handling credentials, secrets, and implementing least-privilege access

Managing Memory, State, and Context

• Optimizing short-term context windows and prompt engineering strategies
• Long-term memory structures: Redis, vector databases, and retrieval augmentation
• Maintaining consistency, implementing caching, and ensuring memory hygiene

Orchestration, Planning, and Multi-Step Processes

• Sequencing actions, subagent coordination, and task breakdown
• Comparing planning algorithms with heuristic orchestration
• Managing failures, implementing retries, and executing compensating actions

Ensuring Safety, Testing, and Observability

• 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 pipeline integration, and rollout methodologies
• Managing costs, enforcing rate limits, and optimizing resources
• Monitoring, governance frameworks, and operational procedures

Conclusion and Future Directions