Week 0 - Introductory Workshops

On-Demand on July 9 – 16

Theme: Build Your First Agent – Tools, Prompts, and Use Cases

Week 1 - Introductory Workshops | July 16–17

Live NOW | Wed - Thu

Theme: Build Your First Agent – Tools, Prompts, and Use Cases

Week 2 - Intermediate Workshops | July 23–24

Live on Wed - Thu

Theme: Agent Collaboration and Real-World Task Automation

Prashanth Rao, AI Engineer | Kùzu Inc.
Dennis Irorere, Data Engineer | Tripadvisor
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Abstract

‍In this hands-on session, we transition from theory to practice, showing how to build intelligent agent workflows powered by Graph RAG and production-ready knowledge graphs. Grounded in practical examples and real tools, this workshop will help you design, construct, and activate agentic systems that are robust, interpretable, and adaptable to complex enterprise environments.

‍Lesson 1: From Business Question to Graph SchemaWe begin by anchoring the session around a real-world business question and discussing how to translate it into a graph schema designed specifically for agentic workflows. You'll learn how to model information in a way that reflects the needs of downstream LLMs and agents, rather than relying on traditional entity-relationship approaches. We’ll also introduce an example data source, and explore how to think critically about the structure, depth, and navigability of the resulting knowledge graph.

‍Lesson 2: From Unstructured Data to Agent-Ready Graphs with BAMLNext, we focus on transforming high-quality unstructured content into structured graph data using BAML, a declarative functional language for building agent-compatible pipelines. You’ll learn how to define reusable extraction logic for entities and relationships, handle ambiguous or nested content, and prepare the data for ingestion. This section emphasizes reliability, modularity, and production-readiness, giving you the tools to automate what is traditionally a manual, high-effort process.
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Lesson 3: Agentic Graph RAG in ActionFinally, we’ll walk through a complete Graph RAG loop. You’ll see how an LLM converts a natural language query into Cypher, retrieves data from the Kuzu graph database, and synthesizes a natural language response. We’ll also address what happens when no direct match is found, and how agentic workflows can adapt, recover, and still return useful output.Whether you’re designing internal tools or building external-facing AI agents, this session equips you with the principles and practices needed to take Graph RAG from idea to implementation.

Week 3 - Advanced Builders Workshops | July 30–31

Live on Wed - Thu

Theme: Designing Scalable and Intelligent Agent Systems

David Hughes, Principal Data & AI Solution Architect | Enterprise Knowledge
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You've built your Graph RAG system with agentic workflows in the last session—now comes the critical question: How do you know it's actually working? Traditional LLM evaluation metrics fall short when measuring Graph RAG performance in production environments where agents make autonomous decisions based on retrieved knowledge.This advanced session tackles the unique challenges of evaluating agentic Graph RAG systems. Unlike simple question-answering scenarios, agentic workflows require evaluation frameworks that measure not just accuracy, but decision quality, reasoning consistency, and operational reliability under real-world conditions.

‍Lesson 1: What Makes Graph RAG Evaluation DifferentWe'll explore why standard retrieval metrics miss the mark for agentic systems. When agents use Graph RAG to make decisions, you need to evaluate retrieval quality, reasoning coherence, and downstream task performance simultaneously. We'll define evaluation frameworks that capture how well your Graph RAG system enables agents to achieve their goals, focusing on metrics that directly impact business outcomes rather than traditional accuracy measures.

‍Lesson 2: Practical Benchmarking with OPIKYou'll get hands-on experience setting up comprehensive evaluation pipelines using OPIK, an open-source platform designed for production LLM evaluation. We'll demonstrate how to create evaluation datasets that reflect real agentic scenarios, implement automated benchmarking workflows, and interpret results to identify system weaknesses. You'll learn to benchmark not just individual queries, but entire agentic decision sequences that reveal how well your Graph RAG supports agent reasoning.

‍Lesson 3: Production Monitoring and Continuous ImprovementWe'll conclude by looking forward and investigating strategies for ongoing system health monitoring in production environments. You'll learn techniques for detecting performance degradation, handling dynamic data environments, and implementing feedback loops that help your workflows improve over time. We'll cover essential aspects like monitoring data freshness, query performance, and agent decision quality.

We'll also explore multi-agent coordination strategies and how frameworks like Google ADK and A2Acan enhance your agentic workflows to ensure your Graph RAG system maintains reliability as it scales.By the end of this session, you'll have the tools and methodologies to confidently deploy and maintain Graph RAG systems that your agents can rely on for critical decision-making.

Secure Your Spot Now
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Learn from the Minds Shaping Agentic AI

Lance Martin

Software/ML Engineer | LangChain

João (Joe) Moura

CEO at CrewAI

Paige Bailey

DevRel Lead, GenAI at Google

Naman Garg

Co-Founder at AGI Inc.

Martin O'Hanlon

Technical Curriculum Developer at Neo4j

Jerry Liu

Co-Founder and CEO at LlamaIndex

Philipp Schmid

AI Developer Experience at Google DeepMind

Chi Wang

Founder of AutoGen (Now AG2) | Senior Staff Research Scientist at AG2 | Google DeepMind

Valentina Alto

Technical Architect, AI & App at Microsoft

Boris Bolliet

Head of Agentic AI at Infosys-Cambridge AI Centre

Greg Loughnane

Co-Founder & CEO at AI Makerspace

Tuana Çelik

Sr. Developer Relations Engineer at LlamaIndex

Shane Murray

Field CTO & Head of AI Strategy at Monte Carlo

Dennis Irorere

Data Engineer at Tripadvisor

Prashanth Rao

AI Engineer at Kùzu Inc.

Abhishek Choudhary

Co-founder and CTO at TrueFoundry

Ivan Nardini

Developer Relations Engineer at Google Cloud

John Dickerson, PhD

CEO | Co-founder and Chief Scientist at Mozilla.ai | Arthur

Amber Roberts

Staff Technical Marketing Manager at Databricks

Sheamus McGovern

CEO and Software Architect, Data Engineer, and AI expert at ODSC

Micheal Lanham

Principle AI Engineer at Brilliant Harvest

Rick Chakra

Founder & CEO at Armada IQ

David Hughes

Principal Data & AI Solution Architect at Enterprise Knowledge

Amy Hodler

Founder | Consultant | Graph Evangelist at GraphGeeks.org

Chris Alexiuk

Co-Founder & CTO | DL at AI Makerspace | NVIDIA

Master Agentic AI in Just Three Weeks

Week 1: Foundations of Agentic AI – Learn the building blocks of autonomous agents, including core architectures, planning methods, memory systems, and leading development frameworks.

‍Week 2: Advanced Techniques and Collaboration – Dive into advanced reasoning, multi-agent coordination, tool chaining, self-healing workflows, and emerging security challenges.

‍Week 3: Evaluation, Improvement, and Deployment – Focus on real-world applications with sessions on agent evaluation, reliability, deployment strategies, and cumulative demo showcases.

Learn step-by-step, guided by experts.

Build Practical Skills

Interactive Workshops: Participate in live, expert-led sessions where you’ll build real agentic systems.

Cutting-Edge Tools:
Experiment with the latest agent frameworks, memory systems, and orchestration platforms.

Exclusive Builder Credits: Receive free credits from partners to immediately start building and scaling your own autonomous AI agents.
Dive into live, expert-led sessions where you’ll work on real-world AI challenges and learn by doing

Guided by Industry Experts

Expert-Led Sessions
Learn directly from pioneers in autonomous agents, multi-agent coordination, and agentic workflows.

Real-World Insights:
Engage in live Q&A, hands-on demos, and hear from innovators behind today’s leading agent technologies.

From Research to Deployment: Understand both cutting-edge research and practical deployment strategies for agentic AI.

Immediate Impact: Build, Deploy & Scale AI Agents with Credits

Attendees get exclusive builder credits from leading agentic AI platforms — including tools for LLMs, multi-agent orchestration, memory systems, and more. Use your credits to:

✔ Deploy real autonomous agents with no upfront cost
✔ Access premium APIs, frameworks, and cloud tools
✔ Experiment, build, and test in real-world environments
✔ Compete in build challenges and mini-hackathons
✔ Start building with industry-grade platforms — immediately and for free

UNLOCK CREDITS

The Mission: Build an Agentic AI App
July 25

Using any of Google's powerful platforms and tools (Gemini, Vertex AI, Firebase, and more)

JOIN HACKATHON

Featured Topics

Agent Architecture and Design
Foundations for building autonomous, capable AI agents.

‍Agent Types and Patterns
From ReAct and ToT to planning and reflex agents.

‍Agent Design Paradigms
Explore emerging design philosophies behind effective agentic systems.

‍Agentic Frameworks
Compare CrewAI, AutoGen, LangGraph, LangChain, and more.

Advanced Reasoning and Learning for Agents
Explore self-critique and other cutting edge techniques that allow agents to irmpove via reasoning
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‍Agent Planning Techniques
Dive into CoT, LATS, and multi-step reasoning strategies.

‍Agent Memory Systems
From RAG to persistent long-term context.

Multi-Agent Collaboration
Strategies for building agent teams that coordinate and communicate.Featured Topics at the Agentic AI Summit

Error Handling in Agent WorkflowsFeatured Topics at the Agentic AI Summit

‍Agent Evaluation and Benchmarking
Methods and tools to measure performance, reliability, and safety.
Techniques to reduce hallucinations, recover from failure, and ensure robustness.

‍Autonomous Web Agents
Explore agents that navigate and interact with live websites.

‍Self-Healing Agents
Build agents that can detect, adapt, and recover from failures.

‍Agent Security
Address prompt injection, tool exploits, and inter-agent vulnerabilities.

‍Open vs. Closed Agent Ecosystems
Understand the trade-offs between proprietary and open agent platforms.

‍Tool Chaining and Composition
Equip agents to reliably sequence and execute complex tool workflows.Featured Topics at the Agentic AI Summit

Master Agentic AI

Build, Deploy & Scale Autonomous AI Agents in a 3-Week Hands-on Virtual Summit

VIRTUAL I JULY 16-31