Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Foundations of Agentic AI

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

Python Tools and Agent SDKs

• Initializing agents using LangChain and comparable SDKs
• Mastering asynchronous programming, task queues, and subprocess control
• Managing packaging, virtual environments, and reproducible workflows

Connecting External Tools and APIs

• Crafting tool interfaces and secure invocation patterns
• Linking to web APIs, databases, and internal service endpoints
• Oversight of credentials, secrets, and principle-of-least-privilege access

Managing Memory, State, and Context

• Leveraging short-term context windows and prompt engineering strategies
• Structuring long-term memory with Redis, vector stores, and retrieval augmentation
• Maintaining consistency, caching tactics, and memory hygiene

Orchestration, Planning, and Multi-Step Processes

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

Safety, Testing, and Observability

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

Deployment, Scaling, and Agent MLOps

• Containerizing applications, CI/CD pipelines, and rollout strategies
• Optimizing costs, rate limiting, and resource efficiency
• Establishing monitoring, governance, and operational playbooks

Recap and Path Forward