Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

• Defining autonomous agents: definitions and classification
• The agent loop: the perceive, decide, act, and observe cycle
• Architectural patterns for agent responsibilities and scope

Python Toolsets and Agent SDKs

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

Integration of External Tools and APIs

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

Memory, State, and Context Administration

• Short-term context windows and prompt engineering strategies
• Long-term memory structures: Redis, vector stores, and retrieval augmentation
• Data consistency, caching methods, and memory maintenance

Orchestration, Planning, and Multi-Step Processes

• Sequencing actions, subagents, and task breakdown
• Planning algorithms compared with heuristic orchestration
• Managing failures, retries, and corrective actions

Safety, Testing, and Observability

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

Deployment, Scaling, and MLOps for Agents

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

Recap and Future Directions