Course Outline

Fundamentals of Agentic AI for Government

  • Definition and taxonomy of autonomous agents in the context of government operations
  • The agent loop: perceive, decide, act, observe cycle tailored for public sector workflows
  • Design patterns for defining agent responsibilities and scope to align with governmental governance

Python Tooling and Agent SDKs for Government

  • Utilizing LangChain and similar SDKs to develop agents for government applications
  • Asynchronous programming, task queues, and subprocess management in a public sector environment
  • Packaging, virtual environments, and reproducible development workflows for government projects

Integrating External Tools and APIs for Government

  • Designing secure tool interfaces and safe invocation patterns for government systems
  • Connecting to web APIs, databases, and internal services in a government context
  • Managing credentials, secrets, and implementing least-privilege access controls for government data

Memory, State, and Context Management for Government

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

Orchestration, Planning, and Multi-Step Workflows for Government

  • Chaining actions, subagents, and task decomposition to support complex government processes
  • Comparing planning algorithms with heuristic orchestration methods for government tasks
  • Handling failures, retries, and compensating actions in a government environment

Safety, Testing, and Observability for Government

  • Threat models, red-teaming exercises, and input/output sanitization for government agents
  • Unit, integration, and end-to-end testing methodologies for government agents
  • Logging, metrics, tracing, and alerting mechanisms to monitor agent behavior in government systems

Deployment, Scaling, and MLOps for Agents in Government

  • Containerization, CI/CD pipelines, and rollout strategies tailored for government deployment
  • Cost control, rate limiting, and resource optimization techniques for government applications
  • Monitoring, governance, and operational playbooks to ensure compliance and efficiency in government operations

Summary and Next Steps for Government

Requirements

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

Audience

  • Machine Learning Engineers
  • Artificial Intelligence Developers
  • Software Engineers in the public sector
 21 Hours

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