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Course Outline

Overview of Agentic AI and Bootcamp Structure

  • Foundational concepts of agentic artificial intelligence and program framework
  • Assessment of requisite software tools, development frameworks, and system dependencies
  • Established protocols for project initialization and inter-team collaboration

Project 1: Development of an Intelligent Assistant Using Prompt-Based Reasoning

  • Architecting a conversational agent designed for specific operational tasks
  • Leveraging structured prompts to enhance logical processing and decision-making capabilities
  • Implementation and validation within the Jupyter Notebook environment

Project 2: Automated Document Analysis and Summarization Agent

  • Extraction and synthesis of critical information from PDF documents and textual sources
  • Utilization of LangChain for efficient data ingestion and retrieval mechanisms
  • Generation of comprehensive executive summaries derived from structured and unstructured data inputs

Project 3: Workflow Automation via Tool-Integrated Agents

  • Integration of external application programming interfaces (APIs) to streamline routine processes
  • Execution of complex, multi-stage operations through iterative agent loops
  • Development of a prototype automation assistant tailored for specific business workflows

Project 4: Data Analysis and Insight Generation Agent

  • Connection to diverse data repositories, including CSV files, SQL databases, and API endpoints
  • Execution of exploratory data analysis utilizing Python and artificial intelligence enhancements
  • Creation of visual analytics and performance monitoring dashboards for actionable intelligence

Project 5: Multi-Agent Collaboration and Orchestration Framework

  • Coordination of distinct agents to facilitate task delegation and resource allocation
  • Design implementation of a controller-worker architectural model
  • Deployment and rigorous testing of a functional multi-agent system prototype for government operations

Program Conclusion and Future Directions

  • Submission of final projects and incorporation of peer evaluation feedback
  • Strategic discussion regarding system optimization and scalability methodologies
  • Provision of curated resources to support ongoing professional development and technical experimentation

Requirements

  • Intermediate competency in Python development
  • Foundational knowledge of artificial intelligence and machine learning concepts
  • Working familiarity with application programming interfaces (APIs) and data processing procedures

Audience

  • Engineers and developers focused on implementing applied AI solutions for government applications
  • Technical personnel requiring capabilities in rapid prototyping for agentic AI systems
  • Practitioners engaged in the integration of AI technologies and the development of pilot initiatives
 21 Hours

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