Agentic AI Bootcamp

Agentic AI Applications

Vector Retrieval

Context-Aware

Model Orchestration

Multi-Agent

LangGraph Workflows

Agent Interoperability

Agentic Patterns

Observability

Learn to build enterprise-grade agentic ai applications.

Learn to build systems that reason

AI agents is the future of knowledge work

Learn to build agents, not just apps. Automate reasoning, planning, context retrieval and execution.

Context-Aware LLM application development
Introduction to vector databases
Multi-Agent system design with LangGraph
Agentic workflows and node-based execution
Agent communication and interoperability
Agentic design patterns: ReAct, Reflection, CodeAct
Observability and monitoring of agentic workflows

Instructors and guest speakers

Learn from though leaders at the forefront of building agentic AI applications

Luis Serrano

Founder, Serrano Academy

Raja Iqbal

Founder, Ejento AI

Sebastian Witalec

Director of Developer Relations, Weaviate

John Gilhuly

Head of Developer Relations, Arize AI

Kartik Talamadupula

Head of AI, Wand AI

Zain Hasan

Senior DevRel Engineer, Together AI

Hamza Farooq

Founder, Travesaal AI

Sage Elliot

AI Engineer, Union AI

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Explore the bootcamp curriculum

Overview of the topics and practical exercises.

Module I

Building Context-Aware LLM Applications and MCP

Use components like Model I/O, loaders, memory, and retrieval chains to develop applications that retain and use context effectively.

Lecture | In-class discussion | Practical Exercise

Learn how to create intelligent applications that maintain context over time using LLM-specific tooling and architecture.

Key Topics:

Understanding Model I/O: prompts, responses, parsers
Retrieval chains using loaders and retrievers
Implementing memory: buffer memory, summarization memory, vector-backed memory
Combining modules into coherent, state-aware workflows
An Introduction to model context protocol
Hands-on exercises on module topics

Module II

Vector Databases

A comprehensive introduction to vector databases

Lecture | In-class discussion | Practical Exercise

Learn about efficient vector storage and retrieval with vector database, indexing techniques, retrieval methods, and hands-on exercises.

Rationale for vector databases
Vector search, text search, hybrid search
Product Quantization (PQ), Locality Sensitive Hashing (LSH) and Hierarchical Navigable Small World (HNSW)
Retrieval: Cosine Similarity, Nearest Neighbor Search
Relevance scoring in hybrid search using Reciprocal Rank Fusion (RRF)
Using auto-cut feature to remove irrelevant results dynamically
Improving search relevance by using language understanding to re-rank search results
Challenges: Scaling optimization. Reliability optimization. Cost optimization
Hands-on exercise on similarity search, hybrid search, vector compression, generative search and semantic caching.

Module III

Multi-Agent Applications

Build collaborative agents using tools and LangGraph to handle complex, multi-step tasks dynamically.

Lecture | In-class discussion | Practical Exercise

Build distributed, multi-tasking agents that collaborate to perform complex actions using tools, task routing and modular workflows.

Key Topics:

Introduction to tools, agents and autonomous behavior
Tools for building multi-agent systems (LangChain agents, toolkits)
Designing task-specific agents (e.g., planner, executor, summarizer)
Communication protocols between agents
Hands-on: Create a multi-agent system for a business use case

Module IV

Agentic Workflow Fundamentals

Explore LangGraph’s node-based workflows, async execution, and memory-aware agent routing.

Lecture | In-class discussion | Practical Exercise

Dive into LangGraph’s orchestration engine to create structured workflows, decision trees, and looping behaviors.

Key Topics:

Graph-based orchestration models
A Practical Guide to Coordinated LLM Agents Using LangGraph: Nodes (functions or agents), Edges (data/control flow), Cycles (iteration, self-correction), State
Add memory or context passing between agents
Node-based task design
Async vs sync execution in agentic flows
Conditional routing and stateful transitions
Integrating memory into LangGraph workflows
Hands-on exercises using LangGraph

Module V

Agentic Design Patterns:

Implement reusable LLM behavior patterns like ReAct, Reflection, and CodeAct for dynamic reasoning and action.

Lecture | In-class discussion | Practical Exercise

Implement advanced reasoning and decision-making patterns that enable LLMs to plan, reflect, and act intelligently.

Key Topics:

ReAct (Reason + Act) framework
Reflection: self-checking and improvement
CodeAct: write + execute code dynamically
Combining patterns into flexible agents
Use cases: coding agents, research agents, evaluators
Hands-on exercises on building ReAct, Reflection and CodeAct agentic workflows

Module VI

Interoperability of Agents:

Understand how agents communicate and collaborate across platforms like Google A2A and others for seamless orchestration.

Lecture | In-class discussion | Practical Exercise

Learn how to make agents interoperable across platforms, tools, and APIs for broader AI orchestration.

Key Topics:

Google A2A (Agents-to-Agents) overview
Cross-agent communication architecture
Creating API-ready agents
Token hand-off strategies across multiple LLMs
Building language-agnostic agent endpoints
Hands-on: Deploy agents that call and respond to each other

Session VII

Observability and Monitoring

Track, debug, and evaluate agent behavior and LLM performance using robust observability tools.

Lecture | In-class discussion | Practical Exercise

Establish robust monitoring to understand agent behavior, debug workflows, and ensure safety and reliability in production.

Key Topics:

Logging and tracing agent decisions
Callback mechanisms in LangChain & LangGraph
Tracking token usage, latency, success rate
Visual debugging of agent flows

Hands-on Exercise:

Add observability to your agent workflow

Technologies and Tools

Attend the LLM Bootcamp for free

We Accept Tuition Benefits

All of our programs are backed by a certificate from The University of New Mexico, Continuing Education. This means that you may be eligible to attend the bootcamp for FREE.

Not sure? Fill out the form so we can help.

Get a certificate from The University of New Mexico Continuing Education with 5 CEUs

Choose an upcoming cohort

CLASS DATES

CLASS TIMINGS

REGISTER NOW

7 July to 26th August
8 Mondays

9am – 12pm PST
(Instructor-Led Online)

Frequently Asked Questions

Are there any prerequisites?

Yes, a very basic LLM fundamentals and python programming language.

What software do I need to install on my laptop?

Just bring your laptop. We will provide all software, subscriptions, and browser-based sandboxes.

Is the program accredited?

Yes. You will receive a certificate from The University of New Mexico with 3 CEUs.

Are cloud subscriptions included?

Yes. During the bootcamp, you will be given all resources needed for completing the labs and exercises.

What is the refund policy?

Registrations are 100% refundable for requests received 5 business days before the bootcamp.

What is the duration of the bootcamp

The bootcamp is a 8-week, 30-hour program.

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Consulting

Community

Agentic AI Bootcamp

AI agents is the future of knowledge work

Instructors and guest speakers

Luis Serrano

Raja Iqbal

Sebastian Witalec

John Gilhuly

Kartik Talamadupula

Zain Hasan

Hamza Farooq

Sage Elliot

Loved by customers and partners

Explore the bootcamp curriculum

Building Context-Aware LLM Applications and MCP

Vector Databases

Multi-Agent Applications

Agentic Workflow Fundamentals

Agentic Design Patterns:

Interoperability of Agents:

Observability and Monitoring

Technologies and Tools

We Accept Tuition Benefits

Choose an upcoming cohort

CLASS DATES

CLASS TIMINGS

REGISTER NOW

Frequently Asked Questions

Are there any prerequisites?

What software do I need to install on my laptop?

Is the program accredited?

Are cloud subscriptions included?

What is the refund policy?

What is the duration of the bootcamp

Training Programs

Enterprise

Community

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