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Course Outline

Fundamentals of Agentic AI Architecture

  • Defining autonomous agents: core concepts and classification frameworks for government systems.
  • The agent operational cycle: perception, decision-making, execution, and observation protocols.
  • Architectural design patterns defining agent scope and functional responsibilities.

Python Infrastructure and Agent Software Development Kits (SDKs)

  • Leveraging LangChain and compatible SDKs to establish foundational agent capabilities.
  • Implementing asynchronous programming, task queue management, and subprocess control.
  • Establishing reproducible development environments through virtualization and standardized packaging for government use.

Integration of External Tools and Application Programming Interfaces (APIs)

  • Developing secure tool interfaces and establishing protocols for safe tool invocation.
  • Facilitating connectivity with public web APIs, relational databases, and internal federal services.
  • Managing authentication credentials and enforcing least-privilege access controls.

Memory, State, and Context Management Strategies

  • Optimizing short-term context windows and applying prompt engineering techniques for government applications.
  • Implementing long-term memory architectures utilizing Redis, vector stores, and retrieval-augmented generation methods.
  • Ensuring data consistency, caching efficiency, and rigorous memory hygiene standards.

Orchestration, Strategic Planning, and Multi-Step Workflow Execution

  • Coordinating action chains, subagent interactions, and task decomposition techniques.
  • Evaluating the application of formal planning algorithms versus heuristic orchestration methods.
  • Managing operational resilience through failure handling, automated retries, and compensatory actions.

Safety Protocols, Testing Standards, and Observability Frameworks

  • Conducting threat modeling, red-teaming exercises, and implementing input/output sanitization measures.
  • Executing unit, integration, and end-to-end testing protocols for agent reliability.
  • Establishing comprehensive logging, metric tracking, distributed tracing, and alerting systems for behavioral monitoring.

Deployment, Scalability, and Machine Learning Operations (MLOps) for Agents

  • Implementing containerization strategies, continuous integration/continuous deployment (CI/CD) pipelines, and controlled rollout procedures.
  • Optimizing resource allocation, managing costs, and enforcing rate limiting constraints.
  • Maintaining operational governance through rigorous monitoring and standardized incident response playbooks.

Conclusion and Strategic Recommendations

Requirements

  • Proficiency in Python programming
  • Practical experience utilizing REST APIs and asynchronous I/O frameworks
  • Knowledge of machine learning principles and pretrained large language models (LLMs)

Intended Audience

  • Machine learning engineers
  • Artificial intelligence developers
  • Software engineering professionals
 21 Hours

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