Course Outline

Fundamentals of Agentic AI

  • What is an autonomous agent: definitions and taxonomy
  • Agent loop: perceive, decide, act, observe cycle
  • Design patterns for agent responsibilities and scope

Python Tooling and Agent SDKs

  • Using LangChain and similar SDKs to bootstrap agents
  • Async programming, task queues, and subprocess management
  • Packaging, virtual environments, and reproducible development workflows

Integrating External Tools and APIs

  • Designing tool interfaces and safe tool invocation patterns
  • Connecting to web APIs, databases, and internal services
  • Managing credentials, secrets, and least-privilege access

Memory, State, and Context Management

  • Short-term context windows and prompt engineering techniques
  • Long-term memory architectures: Redis, vector stores, retrieval augmentation
  • Consistency, caching strategies, and memory hygiene

Orchestration, Planning, and Multi-Step Workflows

  • Chaining actions, subagents, and task decomposition
  • Planning algorithms vs heuristic orchestration
  • Handling failures, retries, and compensating actions

Safety, Testing, and Observability

  • Threat models, red-teaming, and input/output sanitization
  • Unit, integration, and end-to-end testing for agents
  • Logging, metrics, tracing, and alerting for agent behavior

Deployment, Scaling, and MLOps for Agents

  • Containerization, CI/CD pipelines, and rollout strategies
  • Cost control, rate limiting, and resource optimization
  • Monitoring, governance, and operational playbooks

Summary and Next Steps

Requirements

  • An understanding of Python programming
  • Experience with REST APIs and asynchronous I/O
  • Familiarity with machine learning concepts and pretrained LLMs

Audience

  • ML engineers
  • AI developers
  • Software engineers
 21 Hours

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