Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Core Principles of Agentic AI

• Defining autonomous agents: key concepts and classification
• The agent cycle: the perceive, decide, act, and observe loop
• Establishing design patterns for agent scope and responsibilities

Python Ecosystem and Agent SDKs

• Leveraging LangChain and comparable SDKs to initialize agents
• Async programming, task queues, and managing subprocesses
• Package management, virtual environments, and reproducible workflows

External Tool and API Integration

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

Memory, State, and Context Handling

• Managing short-term context windows and prompt engineering tactics
• Long-term memory solutions: Redis, vector stores, and retrieval-augmented generation
• Ensuring consistency, caching strategies, and memory maintenance

Orchestration, Planning, and Multi-Step Processes

• Action chaining, subagent management, and task breakdown
• Comparing planning algorithms with heuristic orchestration
• Managing errors, retries, and compensation logic

Safety, Validation, and Observability

• Threat modeling, red-teaming, and input/output filtering
• Unit, integration, and end-to-end testing for agent systems
• Implementing 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 efficiency
• Monitoring, governance, and operational runbooks

Recap and Future Directions