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, observe cycle
• Establishing design patterns for agent responsibilities and scope

Python Ecosystem and Agent SDKs

• Leveraging LangChain and comparable SDKs for agent initialization
• Asynchronous programming, task queuing, and subprocess management
• Package management, virtual environments, and reproducible development workflows

External Tool and API Integration

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

Memory, State, and Context Management

• Managing short-term context windows and prompt engineering strategies
• Long-term memory architectures: Redis, vector stores, and retrieval augmentation
• Ensuring consistency, caching strategies, and memory hygiene

Orchestration, Planning, and Multi-Step Workflows

• Chaining actions, utilizing subagents, and decomposing tasks
• Comparing planning algorithms with heuristic orchestration
• Managing failures, retries, and compensating actions

Safety, Testing, and Observability

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

Deployment, Scaling, and Agent MLOps

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

Summary and Future Directions