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Domain 10

LangChain & AI Agent Development

Systems that plan, use tools and carry context across steps — and the engineering discipline that stops them going wrong quietly.

Emerging-tech trackPrior LLM or Python experience is expected1 program available
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Learning objectives

What you will be able to do.

  • Orchestrate multi-step LLM workflows
  • Give a model tools, and handle what it does with them
  • Add retrieval and memory as capabilities
  • Design agents that fail visibly rather than silently
  • Evaluate and constrain agent behaviour

Who this is for

  • Learners who have built with LLMs and want to go further
  • Developers building automation on top of AI

Prerequisites: Prior LLM or Python experience is expected

Tools and technologies

  • Python
  • LangChain
  • LLM APIs
  • Vector databases
  • Tracing tools
  • Git

Roles this prepares you for

  • AI Agent Developer
  • AI Engineer
  • Automation Engineer

Curriculum

5 modules, in the order they build on each other.

Each module ends in something you have made work, not just something you have watched.

  1. 01

    Orchestration

    • Chains and composition
    • Structured output and parsing
    • Branching and control flow
    • Streaming and partial results
  2. 02

    Tools

    • Defining tools a model can call
    • Argument validation
    • Handling tool failure
    • Side effects and confirmation steps
  3. 03

    Retrieval and memory

    • Retrieval as a tool
    • Short and long-term memory
    • Context window management
    • State across sessions
  4. 04

    Agent design

    • Planning and reasoning loops
    • Multi-agent patterns
    • Termination conditions and loop limits
    • Cost control
  5. 05

    Reliability

    • Guardrails and constraints
    • Observability and tracing
    • Evaluating agent runs
    • Common failure modes

Practical work

What you could build in this domain.

Project briefs are agreed with your mentor at the start, so the work suits the level you are actually at.

  • 01

    A multi-tool research agent that reports its reasoning trail

  • 02

    An agent that automates a real multi-step workflow

  • 03

    A traced agent with an evaluation suite over recorded runs

Programs

How to study LangChain & AI Agents.

The same domain, in the format that fits your situation.

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  • LangChain & AI AgentsAdvanced

    LangChain & AI Agent Development

    Orchestrating LLMs into systems that plan, call tools, remember context and complete multi-step work.

    • LLM orchestration and chaining
    • Tool and function calling
    • Retrieval as an agent capability
    • Memory across steps and sessions
    • Agent evaluation, failure modes and guardrails

    You finish with: A multi-tool agent that plans, executes and reports on a real task.

    Program details

Keep looking

Other domains.

Learn Smart. Build Practical. Grow with MindXcel.

Learn the technologies shaping tomorrow — AI, ML, Data Science, Generative AI, LLMs and AI Agents.

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