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Building Agentic AI applications with LLMs

  • Kursuskode GK847002
  • Varighed 1 Dag

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Gain hands-on experience in designing agents that efficiently retrieve and refine information, intelligently route queries, and execute tasks concurrently using orchestration tools like LangGraph and sound software engineering practices.

The bar for what AI-powered agents can do has been steadily rising over the past few years, and new innovations allow them to not only engage in conversations but also utilize tools, conduct research, and execute on complex objectives at scale. This course empowers you to develop sophisticated agent systems that can execute on deep thought, research, software calling, and distributed operation. Throughout the course, you'll gain hands-on experience in designing agents that efficiently retrieve and refine information, intelligently route queries, and execute tasks concurrently using orchestration tools like LangGraph and sound software engineering practices. By the end of the course, you will have a solid foundation in agent architectures and will be able to construct interesting agent-like integrations to complement your existing workflows and software stacks.

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These events can be delivered exclusively for your company at our locations or yours, specifically for your delegates and your needs. The Company Events can be tailored or standard course deliveries.

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Kursets formål

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  • Understand the strengths and limitations of LLMs, and why agent-based paradigms help us to empower them in our modern software landscape.
  • Learn to produce structured outputs to enable machine-parseable function calls or API integrations.
  • Explore retrieval mechanisms and knowledge graphs for domain knowledge.
  • Experiment with multi-agent orchestration using frameworks like LangGraph.
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Kursusindhold

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Module 1: Fundamentals of Agent Abstraction and LLMs

  • Discuss LLM capabilities & pitfalls
  • Introduce agents as a task decomposition abstraction.
  • Demonstrate minimal agent with free-text LLM calls

Module 2: Structured Output & Basic Fulfillment Mechanisms

  • Bottlenecking LLMs with JSON/task-based outputs.
  • Ensure domain alignment & stable schema enforcement.
  • Introduction to cognitive architectures.

Module 3: Retrieval Mechanisms & Environmental Tooling

  • Formalize environment access strategies for agents to interface with other systems.
  • Develop tool interfaces for external data repositories (DBs, APIs)
  • Use vector-RAG-coded for semantic retrieval over document sets

Module 4: Multi-Agent Systems & Frameworks

  • Decompose tasks among specialized agents
  • Formalize communication buffers and process distribution schemes.
  • Differentiate between different frameworks and their unique approaches.

Module 5: Final Assessment

  • Deploy an agent that can schedule multiple retrieval operations to gather research and return to user.

Module 6:  [Optional] Real-Time Agents

  • Discuss multimodal considerations and agentic use-cases that interact with the physical world.
  • Explore recent advances in robotics, audio systems, and world models.
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