Course Outline

Fundamentals of Agentic AI for Government

  • Definition and taxonomy of autonomous agents for government use
  • The agent loop: perceive, decide, act, observe cycle in public sector applications
  • Design patterns for defining agent responsibilities and scope in government operations

Python Tooling and Agent SDKs for Government

  • Utilizing LangChain and similar SDKs to develop agents for government projects
  • Async programming, task queues, and subprocess management in a governmental context
  • Packaging, virtual environments, and reproducible development workflows for government systems

Integrating External Tools and APIs for Government

  • Designing tool interfaces and implementing safe invocation patterns for government applications
  • Connecting to web APIs, databases, and internal services within a governmental framework
  • Managing credentials, secrets, and ensuring least-privilege access in government systems

Memory, State, and Context Management for Government

  • Short-term context windows and prompt engineering techniques for government agents
  • Long-term memory architectures: Redis, vector stores, and retrieval augmentation for public sector use
  • Consistency, caching strategies, and memory hygiene in government applications

Orchestration, Planning, and Multi-Step Workflows for Government

  • Chaining actions, subagents, and task decomposition for efficient governmental operations
  • Planning algorithms versus heuristic orchestration for government workflows
  • Handling failures, retries, and compensating actions in government systems

Safety, Testing, and Observability for Government Agents

  • Threat models, red-teaming, and input/output sanitization for secure government agents
  • Unit, integration, and end-to-end testing of agents for government use
  • Logging, metrics, tracing, and alerting for monitoring agent behavior in government systems

Deployment, Scaling, and MLOps for Government Agents

  • Containerization, CI/CD pipelines, and rollout strategies for government agents
  • Cost control, rate limiting, and resource optimization in government operations
  • Monitoring, governance, and operational playbooks for managing government agents

Summary and Next Steps for Government

Requirements

  • An understanding of Python programming for government applications
  • Experience with REST APIs and asynchronous I/O operations
  • Familiarity with machine learning concepts and pretrained language models

Audience

  • Machine Learning Engineers
  • Artificial Intelligence Developers
  • Software Engineers
 21 Hours

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

Upcoming Courses

Agentic AI Engineering with Python — Build Autonomous Agents

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2026-04-16 09:30
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