Galileo 🔭

𝘞𝘦'𝘷𝘦 𝘨𝘰𝘵 45 𝘮𝘪𝘭𝘭𝘪𝘰𝘯 𝘳𝘦𝘢𝘴𝘰𝘯𝘴 𝘵𝘰 𝘤𝘦𝘭𝘦𝘣𝘳𝘢𝘵𝘦, 𝘢𝘯𝘥 𝘸𝘦 𝘥𝘪𝘥 𝘪𝘵 𝘪𝘯 𝘴𝘵𝘺𝘭𝘦!  🎉🌴 Ahead of our Series B funding announcement, we whisked our incredible Galileo team away to Cabo for an unforgettable offsite. From beachside brainstorming to sunset team-building, we've returned refreshed, inspired, and more united than ever—we're ready to bring Evaluation Intelligence to AI engineers worldwide! 🌎 A massive thank you to our team - your brilliance and dedication are the real reasons we're celebrating. Here's to the exciting journey ahead! 𝘗𝘚 - 𝘞𝘦'𝘳𝘦 𝘩𝘪𝘳𝘪𝘯𝘨 👀🎯 #TeamGalileo #StartupLife #AIEngineering #WorkHardPlayHard #Hiring

Happy New Year, Beautiful Humans! Celebrating Innovation in Tech – 25 Humans and Companies to Watch in 2025! The new year is here, and so are the innovators shaping the future of technology. 🤩 Check out 25 individuals and companies making waves in AI, cybersecurity, web development, and beyond. These leaders and disruptors drive real change—and their work is worth watching! 👉 Read the complete list here: https://lnkd.in/gFX5mDb2 Who do you think will redefine tech in 2025? Drop your favorites in the comments! ⬇️ #TechLeaders #AI #Innovation #WebDevelopment #Cybersecurity #TeachJennTech

The dirty secret of AI agents? Most companies can't measure if they're actually working. 😲 The promise is compelling: AI agents that process insurance claims, analyze market data, and accelerate development. But as these digital workers proliferate across industries, a critical gap is emerging between deployment and value. The root cause isn't the technology - it's our approach to measurement. Effective agent evaluation requires understanding performance at three critical levels: • 𝗦𝘁𝗲𝗽-𝗟𝗲𝘃𝗲𝗹: Was the right tool chosen and used correctly at each decision point?  • 𝗧𝘂𝗿𝗻-𝗟𝗲𝘃𝗲𝗹: Were the steps performed in the correct order to reach a conclusion?  • 𝗦𝗲𝘀𝘀𝗶𝗼𝗻-𝗟𝗲𝘃𝗲𝗹: Is the final result accurate and valuable? This complexity demands a sophisticated measurement framework across four key dimensions: 1. 𝗦𝘆𝘀𝘁𝗲𝗺 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Beyond basic resource monitoring, we need to understand how agents handle complex state management and error recovery 2. 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻: It's not just about speed - we need to measure the quality and completeness of multi-step processes 3. 𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: How effectively are agents choosing and using the tools at their disposal? 4. 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: Are outputs consistently meeting regulatory and business requirements? The organizations succeeding with AI agents aren't just deploying technology - they're building comprehensive measurement frameworks that drive continuous improvement. 2025 will separate companies that effectively measure and optimize their AI investments from those still struggling with basic deployment. Read more in Pratik Bhavsar's blog in the comments 👇 #AIStrategy #DigitalTransformation #Enterprise #Innovation #ArtificialIntelligence

Which is the best framework to build an agent? 🌟 3 Frameworks Leading the Agent Landscape We ran a survey and found Langgraph, Autogen, and Crew AI to be favorites amongst developers: 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 LangGraph is an open-source framework designed by Langchain to build stateful, multi-actor applications using LLMs. Inspired by the long history of representing data processing pipelines as directed acyclic graphs (DAGs), LangGraph treats workflows as graphs where each node represents a specific task or function. This graph-based approach allows for fine-grained control over the flow and state of applications, making it particularly suitable for complex workflows that require advanced memory features, error recovery, and human-in-the-loop interactions. LangGraph integrates seamlessly with LangChain, providing access to a wide range of tools and models, and supports various multi-agent interaction patterns. 𝗔𝘂𝘁𝗼𝗴𝗲𝗻 Autogen is a versatile framework developed by Microsoft for building conversational agents. It treats workflows as conversations between agents, making it intuitive for users who prefer interactive ChatGPT-like interfaces. Autogen supports various tools, including code executors and function callers, allowing agents to perform complex tasks autonomously. The framework is highly customizable, enabling users to extend agents with additional components and define custom workflows. Autogen is designed to be modular and easy to maintain, making it suitable for both simple and complex multi-agent scenarios. 𝗖𝗿𝗲𝘄 𝗔𝗜 Crew AI is a framework designed to facilitate the collaboration of role-based AI agents. Each agent in Crew AI is assigned specific roles and goals, allowing them to operate as a cohesive unit. This framework is ideal for building sophisticated multi-agent systems such as multi-agent research teams. Crew AI supports flexible task management, autonomous inter-agent delegation, and customizable tools. Read the full analysis: https://lnkd.in/gfk57T5T

📢 Join 100+ product leaders at the AI Product Summit this Friday 12/6 in Palo Alto 📢 Founders Creative, a community of 10,000 product leaders, founders, investors and executives in Silicon Valley, is hosting a half-day summit on #AIProductManagement on Dec 6th in Palo Alto for senior product leaders. Who's leading it? #ProductCafe: We are a tight group of 100 product leaders who have come together through a series of coffee shop and lunch conversations hosted over the course of 2024. What's the plan? We’ll have tactical talks and hosted panels from world-class product leaders, organized roundtables with peers, a curated group of attendees, and a few fun surprises. 🕰️ Agenda CPO Fireside Chat Abhijit Kakhandiki, former CPO & AI Officer @ Redwood Software Mala Ramakrishnan, Chief Product & Technology Officer @ Cuvo & Managing Partner @ Progressive Ventures Executive Panel: Emerging AI Trends: Real-World Use Cases Driving Customer Value Rekha Venkatakrishnan, Head of Products, Gen AI @ Chime Michael T., Head of Product @ Zluri Karishma Jadeja, SVP of Products @ HealthTap Balaji P., Vice President & GTM @ Forcepoint Prakash A., Product Leader @ Lipi, Meta Executive Panel: Scaling Responsible AI While Driving Product Innovation Anand Madhavan, Director, AI Products @ Pure Storage, Chair, Product Council @ Founders Creative Vineet Mathur, Sr. Dir, Product Mgmt, Conversational AI @ Twilio Bhaskar Krishnan, Product Leader, Ads, Commerce & AI @ Meta Kiran Karadi, VP, Head of Product Mgmt, Service Cloud @ SAP Subha Shetty, Chief AI Officer, Fractional CPO Wayfound Sponsor Talks and Tables Marily Nika, Ph.D Nika, Founder @ AI Product Academy, Product @ Google Tatyana Mamut 🇺🇸🇮🇱🇺🇦, Cofounder & CEO @ Wayfound Soumya Mohan, Product Leader @ Galileo 🔭 Venkat Rapaka, Chief Product Officer @ Arta Finance rajni sharma, Founder @ World Data Store You’ll come away with: - A deeper understanding of AI's impact in product management A ton of fun and a chance at friendships - Best practices and actionable tips to ace your product role Who should attend? This summit is for senior product and growth people, and typically someone’s title will be one of: • Product Lead or Growth Lead • Principal PM • Group PM • Director of Product or Director of Growth • Head of Product or Head of Growth • VP of Product or VP or Growth • Chief Product Officer What’s the start and end time? Will any meals be served? Doors open at 12pm PST for roundtables, arrive on time. Lunch will be served. Content starts at 1pm and ends at 6pm PST, with opening and closing networking sessions 📈 We have a few spots available. Purchase your ticket today: https://lnkd.in/gmp2aDKq 🍽️🍷💡 Join us post-event for our exclusive executive dinner. Sign up here: https://lnkd.in/gWhTaEPw

AI agents come in many shapes and sizes. But what are the tradeoffs? Here is a detailed yet concise guide to understanding agents of different complexity!

Enjoyed chatting with Gené Teare at Crunchbase about how Citi Ventures invests in leading AI startups: Datavolo (Acq by Snowflake), Galileo 🔭, Glean, Lexion (Acq by Docusign), Norm Ai, poolside and Writer to accelerate the transformation of Citi and financial services and large enterprises companies in general. Arvind Purushotham Vibhor Rastogi Blaze O'Byrne Çağla Kaymaz Jelena Zec Olivia Zhang Arti Kuthiala Dana Wohlfarth-Piper, MSPR

Reduce complexity. Deploy faster. Drive results that matter. Building GenAI applications shouldn't mean navigating an endless maze of technology integration choices. The Elastic AI Ecosystem brings together Elasticsearch vector database integrations with industry-leading AI technology providers to build production ready GenAI applications. Meet the ecosystem: Alibaba Cloud, Anthropic, Amazon Web Services (AWS), Cohere, Confluent, Dataiku, DataRobot, Galileo 🔭, Google Cloud, Hugging Face, LlamaIndex, LangChain, Mistral AI, Microsoft Azure, NVIDIA, OpenAI, Protect AI, Red Hat, Vectorize, unstructured.io Learn more about how our AI Ecosystem enables your organization to accelerate AI innovation: https://go.es.io/3CmQ8w4 #Elastic #Genai #VectorDB #Ecosystem

Build a REAL finance research agent with LangGraph 🗯️ Imagine a research assistant that doesn't just answer questions but comes up with questions like a skilled analyst. It starts by understanding the problem and then searches for right information. Once it gets the information it distills it like a human. But it doesn't stop there... The magic happens in how it thinks. When you ask "Should I invest in Tesla?" it breaks this down into smaller questions: How's the EV market doing? What's Tesla's market share? What are their latest financials? Just like a pro would do. We can actually see how it makes decisions. Every claim it makes about Tesla needs proof. If it says "Tesla's sales grew 20%" it must show where it found this data. No random guessing allowed. Our top agents hit 84% accuracy while processing research in just 84 seconds - 3x faster than other versions. At just $0.0025 per request, we can keep testing until it's perfect. Every number tells us exactly when our agent is hitting the mark or needs improvement. Think of it like having a super-smart research intern who shows their work. When they're wrong we can see exactly where they messed up and fix it. When they're right we know precisely why. Want to build your own research agent? Full code and tutorial in the link 👇

Lost in the jargon behind AI Agents? Check out Erin Mikail 🏄🏼♀️ Staples's first blog for Galileo, covering the landscape of AI Agents and the workflow behind them. Give it a read 👇