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Course Outline

Fundamentals of Agentic AI

  • Understanding autonomous agents: definitions and taxonomy.
  • The agent loop: the perceive, decide, act, and observe cycle.
  • Design patterns for agent responsibilities and scope.

Python Tooling and Agent SDKs

  • Leveraging 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, and retrieval augmentation.
  • Ensuring consistency, caching strategies, and memory hygiene.

Orchestration, Planning, and Multi-Step Workflows

  • Chaining actions, subagents, and task decomposition.
  • Comparing planning algorithms with heuristic orchestration.
  • Handling failures, retries, and compensating actions.

Safety, Testing, and Observability

  • Threat models, red-teaming, and input/output sanitization.
  • Conducting unit, integration, and end-to-end testing for agents.
  • Implementing 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

  • 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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