Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Core Concepts of Agentic AI

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

Python Ecosystem and Agent SDKs

• Leveraging LangChain and comparable SDKs to initialize agents
• Implementing async programming, task queues, and subprocess management
• Managing packaging, virtual environments, and reproducible development workflows

Connecting External Tools and APIs

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

Managing Memory, State, and Context

• Utilizing short-term context windows and advanced prompt engineering
• Building long-term memory systems using Redis, vector stores, and retrieval augmentation
• Ensuring consistency, optimizing caching strategies, and maintaining memory hygiene

Orchestrating Planning and Multi-Step Workflows

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

Ensuring Safety, Testing, and Observability

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

Deployment, Scaling, and Agent MLOps

• Utilizing containerization, CI/CD pipelines, and effective rollout strategies
• Controlling costs, applying rate limiting, and optimizing resources
• Establishing monitoring, governance, and operational playbooks

Wrap-Up and Future Directions