Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

• Defining autonomous agents: core concepts and classification
• The agent cycle: perceiving, deciding, acting, and observing
• Architectural patterns for defining agent responsibilities and scope

Python Ecosystem and Agent SDKs

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

Connecting External Tools and APIs

• Creating tool interfaces and implementing secure invocation patterns
• Interfacing with web services, databases, and internal platforms
• Managing credentials, secrets, and enforcing least-privilege access

Memory, State, and Context Handling

• Optimizing short-term context windows and prompt engineering strategies
• Long-term memory solutions: Redis, vector databases, and retrieval-augmented generation
• Ensuring data consistency, implementing caching, and maintaining memory hygiene

Orchestration, Planning, and Complex Workflows

• Sequencing actions, managing subagents, and decomposing tasks
• Comparing planning algorithms with heuristic-based orchestration
• Managing failures, implementing retries, and executing compensating actions

Safety, Validation, and Monitoring

• Developing threat models, red-teaming, and sanitizing inputs and outputs
• Conducting unit, integration, and end-to-end testing for agent behavior
• Implementing logging, metrics, tracing, and alerting systems

Deployment, Scalability, and MLOps for Agents

• Containerization, CI/CD pipelines, and deployment strategies
• Managing costs, rate limiting, and resource optimization
• Establishing monitoring, governance, and operational runbooks

Conclusion and Future Directions