Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Core Principles of Agentic AI

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

Python Ecosystem and Agent Frameworks

• Leveraging LangChain and comparable SDKs to scaffold agents
• Working with asynchronous programming, task queues, and subprocess control
• Managing packaging, virtual environments, and reproducible dev workflows

Connecting External Tools and APIs

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

Managing Memory, State, and Context

• Optimizing short-term context windows and prompt engineering methods
• Building long-term memory systems: Redis, vector databases, and RAG
• Maintaining consistency, implementing caching, and ensuring memory hygiene

Orchestration, Planning, and Complex Workflows

• Implementing action chaining, sub-agents, and task breakdown strategies
• Comparing planning algorithms with heuristic-based orchestration
• Managing failures, implementing retries, and defining compensating actions

Security, Validation, and Monitoring

• Establishing threat models, conducting red-teaming, and sanitizing I/O
• Performing unit, integration, and end-to-end testing on agent behavior
• Setting up logging, metrics, tracing, and alerting for agent operations

Deployment, Scalability, and Agent MLOps

• Implementing containerization, CI/CD pipelines, and deployment strategies
• Managing costs, applying rate limits, and optimizing resource usage
• Establishing monitoring, governance frameworks, and operational runbooks

Conclusions and Future Directions