Course Overview of Building Agentic AI Applications with LLMs:

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

After completing Building Agentic AI Applications with LLMs, participants will be able to:

  • Understand the strengths and limitations of LLMs, and why agent-based paradigms help us empower them in modern software.
  • 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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Key Features of Building Agentic AI Applications with LLMs:

  • Hands-on labs on agent abstractions, structured outputs, and retrieval.

  • Knowledge graph and vector-based retrieval-augmented generation workflows.

  • Multi-agent orchestration and concurrent task execution using LangGraph.

  • Concepts for real-time and scalable agent operations (optional module).

  • Final assessment interfacing with a scalable multi-tenant agent API.

  • Practical, software‑engineering‑driven approach to robust agent design.

Who should Attend Building Agentic AI Applications with LLMs:

  • Developers building LLM-based applications and intelligent systems.
  • AI/ML engineers designing automation or retrieval pipelines.
  • Technical professionals exploring agent-based architectures for enterprise.
  • Teams seeking to integrate agentic workflows into existing software stacks.

Prerequisites of Building Agentic AI Applications with LLMs:

  • Working knowledge of Python.
  • Familiarity with LLMs and API integrations.
  • Recommended: Basics of RAG, embeddings, or vector databases.

    • Why choose CloudThat as your training partner?

    • Expert‑led sessions on agent architectures and LLM engineering.
    • Hands‑on labs with LangGraph, structured outputs, and retrieval pipelines
    • Customized learning paths for individuals and enterprise teams.
    • Interactive Q&A and mentorship throughout the program.
    • Career support and guidance for agentic AI projects.
    • Regularly updated curriculum reflecting the latest agentic innovations.
    • Trusted by enterprises for pragmatic, production‑grade AI training.

    Course Outline: Download Course Outline

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

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

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

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

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

    [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.

    Certification Details:

      Participants who complete the course and final assessment will receive a certificate recognizing competency in agent architectures and LLM driven workflows.

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    Course ID: 27010

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    FAQs:

    Designing and deploying intelligent agents with LLMs, structured outputs, retrieval, and multi agent orchestration using frameworks like LangGraph.

    No, familiarity with LLMs, APIs, and Python is sufficient; knowledge of RAG is helpful.

    Yes, task decomposition, communication buffers, and concurrency patterns are included.

    Yes, deploy a scalable agent integration that coordinates multiple retrievals.

    Yes, as an optional module on multimodal, robotics, and real time constraints.

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