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Agentic AI bootcamp

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

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Technologies and Tools

Open AI data science dojo bootcamp
llama index data science dojo bootcamp
cohere data science dojo bootcamp (1)
redis data science dojo bootcamp
LangChain data science dojo bootcamp
runpod data science dojo bootcamp

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12,000+

Alumni

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1M+

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Who is this bootcamp for?

The Agentic AI Bootcamp is designed for professionals who already understand the basics of LLMs and are ready to take the next leap, building intelligent, autonomous AI agents using real-world tools and techniques. 

Data and AI professionals

You’ve worked with LLMs—now learn to build systems that reason, plan, and act. This bootcamp teaches you how to integrate tools like LangChain, vector databases, and RAG to build truly agentic workflows.

Engineers and developers

Take your technical skills further by deploying LLM-powered agents in production environments. Learn how to connect APIs, fine-tune performance, and handle edge cases in real-time applications.

Product leaders and builders

Go beyond prompts. Understand the architecture behind AI agents and how to design agentic workflows that solve complex problems, automate internal processes, or power new customer-facing products.

Instructors and guest speakers

Learn from thought leaders at the forefront of building Agentic AI applications.

Thierry Damiba data science dojo bootcamp

Thierry Damiba

Developer Advocate, Qdrant
zain hasan data science dojo bootcamp

Zain Hasan

Senior DevRel Engineer, Together AI
sage elliot data science dojo bootcamp

Sage Elliot

AI Engineer, Union AI
Luis Serrano data science dojo bootcamp

Luis Serrano

Founder, Serrano Academy
Raja Iqbal data science dojo bootcamp

Raja Iqbal

Founder, Ejento AI
Kartik Talamadupula data science dojo bootcamp

Kartik Talamadupula

Head of AI, Wand AI
Hamza Farooq data science dojo bootcamp

Hamza Farooq

Founder, Travesaal AI
A2A Protocol Workshop: Build Interoperable Multi-Agent Systems

Zaid Ahmed

Senior Data Scientist, Data Science Dojo
Shub Argha data science dojo bootcamp

Shub Argha

Head of Solutions Engineering Arcade.dev

curriculum

Explore the bootcamp curriculum

Overview of the topics and practical exercises.

  • Lecture | Discussion | Practical Exercise

Key Topics:

  • Introduction to LLMs: Strengths and weaknesses of large language models
  • Discriminative versus Generative AI: Predictive models contrasted with generative models
  • Transformer Architecture: Tokenization, embeddings, positional encoding, and attention
  • Embeddings and Similarity: Representing words as vectors and measuring closeness
  • Attention Mechanism: Keys, queries, and values in self-attention
  • Softmax and Probabilities: Converting scores to probabilities for next-word prediction
  • Training and Fine-Tuning: Adapting models with curated data for new tasks
  • Search and Retrieval: Building a semantic search engine with embeddings
  • Retrieval Augmented Generation (RAG): Combining retrieval with generation for grounded answers
  • Hands-On Exercises: Sentence Transformers, semantic search, attention scoring, and attention mechanisms
  • Lecture | Discussion | Practical Exercise

Key Topics:

  • Foundations of Agentic AI: From next-token prediction to reasoning models; limitations of classic LLMs vs reasoning LLMs; the core pillars of agentic systems—reasoning, context, and autonomy.
  • Understanding LLMs: Context windows, session memory, and long-term memory (vector databases, knowledge graphs, summaries); data sources including pre-training, fine-tuning, and in-context learning.
  • Retrieval-Augmented Generation (RAG): Naïve RAG workflows and common challenges; RAG as a context enhancement strategy; preparing and structuring data for effective RAG pipelines.
  • Agentic AI Components: Cognition (reasoning, planning, self-reflection), knowledge representation, and autonomy through tool use, action execution, and monitoring.
  • Agentic Design Patterns: Planning, tool use, and reflection loops; Agentic RAG, routers, and iterative loops; sequential, parallel, and hierarchical workflows.
  • Architectures for Agents: Single-agent vs multi-agent systems; human-in-the-loop strategies; hybrid reasoning pipelines and decision graphs.
  • Advanced Context Techniques: Session summaries, hybrid memory systems, Model Context Protocol (MCP), and scalable context management.
  • Observability, Safety & Governance: Guardrails, explainability, monitoring and evaluation, ethical alignment, and compliance strategies.
  • Hands-On Exercises: Practical implementations of reasoning workflows, memory systems, RAG pipelines, and safe agent design.
  • Lecture | Discussion | Practical Exercise

Key Topics:

  • Introduction to LangChain: Purpose and scope of LangChain; building LLM-powered applications; common challenges in implementing Retrieval-Augmented Generation (RAG).
  • Core Components: LLMs and chat models; prompt templates and example selectors; document loaders and transformers for preprocessing data.
  • Output Parsers: Extracting structured data; enforcing consistent output formats; handling parsing errors and validation failures.
  • Retrieval: Embedding and vectorization strategies; retrievers and metadata filtering; parent document retrieval for contextual completeness.
  • Vector Stores: Storing embeddings efficiently; performing scalable similarity search; optimizing retrieval for large datasets.
  • Chains: Sequential prompt logic; pre- and post-LLM processing steps; integrating retrieval and tool use into end-to-end chains.
  • Tool Use: Connecting APIs and external systems; feeding tool outputs back into workflows; managing retries and error handling.
  • LangChain Expression Language (LCEL): Building modular workflows using runnable components; piping operations; parallel branches and composable pipelines.
  • Hands-On Exercises: Constructing retrieval chains; parsing structured outputs; combining LangChain modules into coherent, production-ready workflows.
  • Lecture | Discussion | Practical Exercise

Key Topics:

  • Vector Database Fundamentals: Embeddings and vector storage; approximate nearest neighbor (ANN) vs k-nearest neighbor (kNN) search; modern vector database architectures and data models.
  • Hybrid Retrieval Design: Combining dense and sparse vectors; applying metadata filters and payload indexing; full-text tokenization for mixed semantic and keyword queries.
  • Advanced Techniques: Maximal Marginal Relevance (MMR) for improving result diversity; Discovery APIs for broader coverage; monitoring and maintaining HNSW index health.
  • Agentic RAG Concepts: Using AI-native vector databases as long-term memory for agents; multi-step retrieval with reasoning loops; context selection strategies and hallucination mitigation.
  • Semantic Caching: Caching semantically similar queries using vector similarity; time-to-live (TTL) and invalidation policies; optimizing cost and latency.
  • Hands-On Exercises: Exploring AI-native vector database fundamentals; implementing hybrid search; applying re-ranking techniques such as MMR; monitoring HNSW index health; building a RAG pipeline with agentic orchestration.
  • Lecture | Discussion | Practical Exercise

Key Topics:

  • Complex Agentic Workflows: Designing workflows with system and user prompts; integrating retrieval, memory layers, web search, and vector databases; implementing critique and refinement loops.
  • Deterministic Chains and Control Flows: Building sequential pipelines with pre- and post-LLM steps; enforcing structured task execution and predictable control logic.
  • Agent Reliability and Dynamic Decisions: Using router agents and conditional flows; balancing autonomy with control for robust task execution.
  • LangGraph Fundamentals: Understanding nodes, edges, and state management; condition-based execution for reliable and auditable workflows.
  • Tool Integration: Connecting APIs, databases, and external systems through node-based tool calls; updating shared state after execution.
  • Agentic Design Patterns: Reflection for self-critique; tool use for external actions; planning for task decomposition and structured reasoning.
  • Multi-Agent Collaboration: Implementing parallel, sequential, loop, and router flows; incorporating error handling and human supervision strategies.
  • Multi-Agent Architectures: Designing hierarchical delegation systems; approval nodes; shared memory and resource coordination.
  • Hands-On Experience: Practical implementation of advanced context engineering concepts across agentic workflows and multi-agent systems.
  • Lecture | Discussion | Practical Exercise

Key Topics:

  • Why Agentic Patterns Matter: Transforming single-pass prompting into iterative, goal-oriented reasoning loops for more adaptive and reliable systems.
  • Reflection Pattern: Enabling agents to evaluate, critique, and refine their own outputs through structured feedback and revision cycles.
  • Planning Pattern: Designing stepwise reasoning flows that decompose complex goals, manage task dependencies, and adapt dynamically to new information.
  • Tool Use Pattern: Connecting models with external systems to retrieve data, execute actions, and extend problem-solving capabilities beyond the model’s internal knowledge.
  • Multi-Agent Collaboration Pattern: Coordinating specialized agents with defined roles; enabling structured communication and collaborative problem-solving.
  • Pattern Trade-Offs: Balancing autonomy with control, flexibility with stability, and creativity with reliability in agent design.
  • Pattern Composition: Integrating reflection, planning, and tool use into hybrid workflows for building more capable and adaptive agents.
  • Hands-On Labs: Implementing individual patterns and combining them into complete, production-ready agent workflows for real-world tasks.
  • Lecture | Discussion | Practical Exercise

Key Topics:

  • Multi-Agent Coordination: Collaboration challenges in multi-agent systems; message routing and task orchestration; scaling cooperation while maintaining stability and control.
  • Need for Agentic Protocols: Establishing discovery and negotiation mechanisms; structured task and state management; enabling secure and reliable agent cooperation.
  • Model Context Protocol (MCP): Client–server architecture for LLM tool integration; standardized access to data and prompts; exposing tools, resources, and templates in a structured way.
  • MCP Architecture: Roles of hosts, clients, and servers; message exchange formats and artifacts; connecting applications, IDEs, and assistants within unified workflows.
  • Agent-to-Agent Protocol (A2A): Task-oriented communication flows; capability discovery using Agent Cards; structured message parts and artifact formats for coordination.
  • Agent Communication Protocol (ACP): Open ecosystem for cross-agent interaction; routing, discovery, and dynamic updates; interoperability across frameworks and platforms.
  • MCP vs ACP vs A2A: Comparing scope, architectural complexity, and message types; selecting appropriate protocols for different workflow requirements.
  • Hands-On Exercise – MCP Client with Streamlit: Setting up the development environment; installing dependencies and configuring API access; connecting an MCP client to servers; discovering tools; automating workflows through tool invocation, data retrieval, and output validation.
  • Lecture | Discussion | Practical Exercise

Key Topics:

  • Origins & Motivation: Addressing fragmented integrations and brittle bespoke adapters; introducing a unified, interoperable interface — the “USB-C for AI.”
  • Protocol Structure: Client–server handshake model; defining resources, tools, and prompts; JSON-RPC transport with structured, schema-driven messages.
  • Context Exposure: How MCP surfaces tools, data, and metadata through a consistent schema to enable discoverability, governance, and controlled access.
  • Agentic Integration: Connecting MCP endpoints to reflection, planning, tool-use, and multi-agent coordination patterns for modular and scalable systems.
  • Hands-On Labs: Setting up an MCP client in Streamlit; discovering and registering tools; automating workflows through data retrieval and validation; logging traces for monitoring and review.
  • Lecture | Discussion | Practical Exercise

Key Topics:

  • Need for Evaluation: Reliability, accuracy, and safety; business and ethical alignment; transparency and user trust.
  • Challenges in Evaluation: Hallucinations and prompt sensitivity; weak context grounding; subjectivity and trade-offs between accuracy, fluency, and creativity.
  • Benchmarking Approaches: MMLU for multitask accuracy; HELM for robustness and fairness; BBH and HotpotQA for reasoning and multi-hop QA.
  • Text Quality Metrics: BLEU for precision; ROUGE for recall; BERTScore for semantic similarity.
  • RAG Evaluation (RAGAs): RAGAS for faithfulness and answer relevance; context precision and recall; joint retrieval-generation scoring.
  • G-Eval: G-Eval for fluency, faithfulness, relevance; claim-level scoring of open-ended outputs.
  • Additional Benchmarks: GLUE for NLU; TriviaQA for QA; RealToxicityPrompts for safety; Blended Skill Talk for dialogue quality.
  • Other Metrics: Perplexity for confidence; METEOR for alignment; MRR and MAP for ranking; ROSCOE for reasoning quality.
  • Hands-On Exercises: Apply RAGAS to RAG pipelines; compare BLEU, ROUGE, BERTScore; evaluate agent outputs using G-Eval.

 

 

  • Lecture | Discussion | Guided Lab | Independent Project

Key Topics:

Project Tracks:

  • Conversational Workflow Orchestration: Design a multi-turn assistant coordinating tasks across specialized agents.
  • Knowledge-Enhanced Agent: Integrate search and APIs for grounding, fact-checking, and real-time data access.
  • Document-Aware Action Agent: Retrieve and reason over documents; trigger external tools or services based on insights.
  • Orchestrated Collaboration (MCP): Build coordinated multi-agent systems using the Model Context Protocol for seamless tool and enterprise integration.

Attendees Will Receive:

  • Comprehensive Datasets: Industry-spanning document collections for robust development and testing.
  • Step-by-Step Implementation Guides: Clear instructions from environment setup to deployment.
  • Ready-to-Use Code Templates: Prebuilt templates within Data Science Dojo’s sandbox for accelerated development.

Learners Can Choose to Implement:

  • Virtual Assistant
  • Content Generation (Marketing Co-pilot)
  • Conversational Agent (Legal & Compliance Assistant)
  • Content Personalizer
  • MCP Chatbot – AI agent with calendar, CRM, and API integrations

Outcome:

A production-ready multi-agent application demonstrating mastery of reasoning, retrieval, tool use, and protocol-driven interoperability.

Earn a verified certificate

Earn a verified certificate from The University of New Mexico Continuing Education:

  • 3 Continuing Education Credit (CEU)
  • Acceptable by employers for reimbursements
  • Valid for professional licensing renewal
  • Verifiable by The University of New Mexico Registrar’s office
  • Add to LinkedIn and share with your network 
UNM Agentic AI Bootcamp

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. Contact us ->

Recognition

agentic ai bootcamp | Data Science Dojo
agentic ai bootcamp | Data Science Dojo
agentic ai bootcamp | Data Science Dojo

Reserve your spot

Learn to build Agentic AI applications from leading experts in industry. 

Agentic AI Bootcamp

Use AGENTIC500 for USD 500 discount

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A word from our alumni

"Partnering with Data Science Dojo aligns with our mission to make data science accessible. Their bootcamps contribute to safe AI deployment education."
Alessya VisnjicCEO & Co-founder, WhyLabs
Alessya Visnjic | WhyLabs | Data Science Dojo
"LLM bootcamp provides exceptional, hands-on initiation into LLMs and practical applications. A deep dive into the subject with theoretical knowledge."
Jeff FongPrincipal Product Manager, HOVER Inc
Jeff Fong | Hover | Data Science Dojo
"Six months of course material access post-training is valuable. Instructors ensure success, making it a top-notch learning experience in AI."
Abrar BhuiyanSupervisor, Software Application Engineer, ICF
Abrar | IFC | LLM Bootcamp | Data Science Dojo
"Instructors simplified complex topics effectively. Hands-on learning enhanced my LLM understanding in 5 days. Intensive but immeasurable rewards."
Fatemeh Shah-MuhammadiResearch Assistant Professor, University of Utah School of Medicine
Fatemeh Shah Mohammadi | Health University of Utah | Data Science Dojo
"A rewarding opportunity to help students of all backgrounds learn new techniques. Being part of a passionate data science community was a real privilege."
Sage ElliotAI Engineer | MLOps & Robotics, WhyLabs
Sage Elliott | WhyLabs | Data Science Dojo
"LLM Bootcamp provides hands-on experience with expert instructors, a comprehensive framework, and extensive resources for substantial upskilling."
Bernard TumanjongChief, Operations Analysis, USA Air Force
Bernard Tumanjong | United States Space Force | Data Science Dojo
"The effort in providing resources was commendable. Teachers and assistants were helpful, making it one of the best courses. I learned a lot for future use."
Fahad AlswainaSr. Data and Model Solutions Associate, KAPSARC
Fahad Alswaina | KAPSARC | Data Science Dojo
"Top-notch speakers, hands-on workshops, and networking make it a wonderful tech experience. Raja's in-depth teaching focuses on learning concepts."
Kevin SongSoftware Engineer, ICF
Kevin Song | ICF | Data Science Dojo
"Comprehensive curriculum in generative AI, prompt engineering, and data retrieval. Excited about practical training opportunities."
Taimur RashidAdvisor, Weaviate
Taimur Rashid | Redis | Data Science Dojo
"Collaborating with Data Science Dojo nurtures the next generation of LLM developers, commendable for fostering a dedicated creator community."
Harrison ChaseCo-Founder and CEO, LangChain
Instructor Harrison Chase from LangChain, guiding participants through the LLM Bootcamp.
"This boot camp provided great content, equipping me with skills and confidence for efficient job execution. Enjoyable class and projects, a rewarding decision."
Luis Armando MSales and Services Manager, RAISA
Luis Armando M | Raisa | Data Science Dojo
"The LLM Bootcamp offered an enlightening, well-structured learning experience, invaluable networking, and insights into real-world AI challenges. "
Amity FoxData Scientist, 343 Industries
Amity Fox | Strategic Insights L.L.C | Data Science Dojo
"Seamless navigation, invaluable hands-on exercises, and well-structured technical aspects for optimal, cost-effective results. Highly recommended for practical LLM knowledge."
Maryam Bagheri Designation-Senior Data Scientist, Deloitte Business
Maryam Bagheri | Deloitte Business | Data Science Dojo
"Comprehensive curriculum for in-depth understanding, seamless hands-on learning with cloud-based tools, and insightful talks by industry experts. Highly recommended."
Jared MillerSoftware Development Manager, Gursey
Jared Miller | Gursey | Data Science Dojo
"Outstanding boot camp, engaging discussions on coding and data security. A valuable and refreshing experience, recommended for advancing data science skills."
Kshitij SinghMachine Learning Lead, Trade Union
Kshitij Singh | Trade Union | Data Science Dojo
"Explored Data Science Dojo's LLM Bootcamp, gaining confidence with a comprehensive understanding. In-person experience, insightful instructors, and diverse insights."
Victor GreenDev-Ops Engineer, Victor Green Foundation
Victor Green | Victor Green Foundation | Data Science Dojo
"LLM Bootcamp surpassed expectations, bridging theory with practical examples. Engaging with cohorts, invaluable insights, and balanced intensity elevated understanding. Highly recommended for language models."
Ken ButlerSolutions Architect, ICF
Ken Butler | ICF | Data Science Dojo
"LLM Bootcamp enriched my understanding of the language-tech intersection. Collaborating with peers was wonderful. Sophie DA's insightful presentation on practical applications is recommended for foundational knowledge."
Erika DavisSenior Business Reporting Analyst, Starbucks
Erika Davis | Starbucks | Data Science Dojo
"LLM Bootcamp shifted my problem-solving approach, sparking creativity with limitless applications. Highly recommended for anyone building their toolkit, fostering creativity in problem solvers, and providing invaluable insights for diverse applications."
Aishwariya RamanSenior Software Engineer, Microsoft
Aishwariya Raman | Microsoft | Data Science Dojo
"Dedicated a week to a profound learning experience with Data Science Dojo, sparking innovative ideas. The practical approach revolutionized our models. Highly recommended."
David Martins GonclavesCo-founder CTO, Notify AI
David Martins Gonçalves - Notify Brand technology
"Engaging with diverse participants and the Dojo staff provided valuable insights. Emphasizes the importance of understanding model performance. Highly recommended for all roles."
Ed WileyAI Executive and Course Instructor, SteelHusk Innovations
Ed Wiley | SteelHusk Innovations | Data Science Dojo
"The bootcamp's blend of theory and hands-on experience suited learners of all backgrounds. In just five days, I gained invaluable knowledge and tools for navigating the AI landscape. "
Dave HortonVP Solutions Engineering, Odaseva
Dave Horton | LLM Bootcamp | Data Science Dojo
"The boot camp surpassed my expectations with insightful talks, practical insights, and comprehensive resources, essential for accelerating knowledge in LLMs."
Francisco MoralesSoftware Engineer, ICF
Francisco Morales | LLM Bootcamp | Data Science Dojo
"The Data Science Dojo Bootcamp exceeded expectations, offering comprehensive learning, hands-on experience, and valuable insights, transforming my confidence in AI."
Sahar NesaeiData Scientist & ML Enthusiast, Lightcast
Sahar Nesaei | LLM Bootcamp | Data Science Dojo
"The Bootcamp accelerated my text analysis skills by engaging in in-person experience, industry insights, and valuable hands-on learning. Highly recommended!"
Florian KlonekSenior Lecturer, Deakin University
Florian Klonek | LLM Bootcamp | Data Science Dojo
"The Bootcamp helped me bridge the gap between academia and industry, offering insightful talks and practical problem-solving exercises. "
Yashwanth ReddyAI Leader, Fidelity Investments
Yashwanth Reddy | LLM Bootcamp | Data Science Dojo
"The Bootcamp exceeded my expectations, offering comprehensive training for both beginners and experienced data scientists. Highly recommended for all!"
Shakeeb SyedData Scientist, Nationwide
Shakeeb Syed | LLM Bootcamp | Data Science Dojo
agentic ai bootcamp | Data Science Dojo
agentic ai bootcamp | Data Science Dojo
agentic ai bootcamp | Data Science Dojo
agentic ai bootcamp | Data Science Dojo
agentic ai bootcamp | Data Science Dojo

Frequently asked questions, answered.

Who does the Agentic AI Curriculum target?

The Agentic AI bootcamp is designed for both technical and non-technical professionals, including engineers, product managers, and business leaders alike. While it includes high-level modules on AI fundamentals, prompt engineering, and strategic deployment, it also dives deep into technical components for developers.

The bootcamp is a 10-week, 30-hour program.

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

Yes, participants who complete the bootcamp will receive a certificate of completion in association with the University of Mexico. This certificate can be a valuable addition to your professional portfolio and demonstrate your expertise in building large language model applications.

The LLM bootcamp covers the fundamentals of large language models and takes you through a complete learning track—from the basics to deployment.

In contrast, the Agentic AI bootcamp focuses specifically on building and deploying AI agents, so we dive straight into hands-on development. 

Yes, these sessions are live and are designed to be highly interactive.

When you join the Agentic AI bootcamp, you will receive:

  • Live sessions with industry experts
  • 1-year access to dedicated learner sandboxes
  • Exclusive access to Agentic AI coding labs
  • Access to all session recordings for review at your convenience
  • A verified certificate upon completion

While we do not provide exact recordings of the live sessions, all key topics and content covered during the class are available in our companion courses. We’ve created structured lesson clips that reflect the material discussed, allowing you to review everything at your convenience even if you miss a live session.

  • Basic understanding of LLM fundamentals and Python
  • LLM architectures: foundation models, prompts, embeddings, fine-tuning
  • Challenges & risks: prompt brittleness, context limits, security, cost
  • Transformers & attention: self-attention, multi-head attention, tokenization

You need a very basic level of Python programming for our Agentic AI bootcamp.

The preparatory material will be shared about two weeks before the bootcamp starts. You’ll receive an email with access details and instructions closer to the start date.

No, cloud subscriptions are not included. Participants will need to use their own accounts.

Transfers are allowed once with no penalty. Transfers requested more than once will incur a $200 processing fee.

If, for any reason, you decide to cancel, we will gladly refund your registration fee in full if you notify us at least five business days before the start of the training. We can also transfer your registration to another cohort if preferred.

However, refunds cannot be processed if you have transferred to a different cohort after registration. Additionally, once you have been added to the learning platform and have accessed the course materials, we are unable to issue a refund, as digital content access is considered program participation.

While we do not specifically focus on job placement, we actively promote networking with our partners, attendees, and an extensive network of alumni. Once you register for the bootcamp, we are happy to assist with introductions if you’re looking to connect with professionals in your desired field.

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