[Music] My name is Zach. I'm a Genai software engineer at Prosper Marketplace. We're a financial services company that connects people with the financial solutions they need.Everything from personal loans to credit cards, HELOC, and so on. The goal of the AI team is to free up time as much as possible by automating very manual and tedious work and hopefully cut down on costs at the same time, which involves building a very flexible and modular AI agents platform. One of the bigger problems we tackled recent ...

[Music] I'm Isis Julian and I'm a senior software engineer at Morning Star and I work on the intelligence engine. Morning Star is a global leader in providing investment research data and analysis and we pride ourselves in empowering investor success by serving transparent, accessible, and reliable investment information. So, with AI gaining increasing popularity nearing the end of 2022 and early 2023, with a scrappy team of just five engineers, we were able to launch our first ever AI research assistant na ...

Company Overview - 11X is building digital workers, including Alice, an AI SDR, and Julian, an AI voice agent [1] - The company relocated from London to San Francisco and rebuilt its core product, Alice, from the ground up [2] Alice Rebuild & Vision - The rebuild of Alice was driven by the belief that agents are the future [3] - The new vision for Alice centers on seven agentic capabilities, including chat-based interaction, knowledge base training via document uploads, AI-driven lead sourcing, deep lead research, personalized emails, automatic handling of inbound messages, and self-learning [11][12][13] Development Process & Tech Stack - The rebuild of Alice 2 took only 3 months from the first commit to migrating the last business customer [3][14] - The company chose a vanilla tech stack and leveraged vendors like Langchain to move quickly [15][16][17] - Langchain was chosen as a key partner due to its AI dev tools, agent framework, cloud hosting, observability, Typescript support, and customer support [18][19] Agent Architecture Evolution - The company experimented with three different architectures for campaign creation: React, workflow, and multi-agent systems [21] - The final architecture was a multi-agent system with a supervisor and specialized sub-agents for research, positioning, LinkedIn messaging, and email writing [44][45][46] Results & Future Plans - Alice 2 went live in January and has sourced close to 2 million leads and sent close to 3 million messages [52] - Alice 2 has generated about 21,000 replies, with a reply rate of around 2%, on par with a human SDR [52] - Future plans include integrating Alice and Julian, implementing self-learning, and exploring new technologies like computer use, memory, and reinforcement learning [53][54]

Agent Adoption & Scalability - LinkedIn aims to scale agentic adoption within the organization to enable broader idea generation [2] - LinkedIn built the Hiring Assistant, its first production agent, to automate recruiter tasks and free up time for candidate interaction [3] - The Hiring Assistant follows an ambient agent pattern, operating in the background and notifying recruiters upon completion [4][5] - LinkedIn adopted a supervisor multi-agent architecture, with a supervisor agent coordinating sub-agents that interact with LinkedIn services [6] Technology Stack & Framework - LinkedIn standardized on Python for GenAI development, moving away from its traditional Java-centric approach [7][8] - The company built a service framework using Python, gRPC, Langchain, and Langraph to streamline the creation of production-ready Python services [9][19] - Over 20 teams have used this framework to create over 30 services supporting Generative AI product experiences [9][10] - Langchain and Langraph were chosen for their ease of use and sensible interfaces, enabling rapid development and integration with internal infrastructure [22][23] Infrastructure & Architecture - LinkedIn invested in a distributed architecture to support agentic communication modes [10] - The company modeled long-running asynchronous flows as a messaging problem, leveraging its existing messaging service for agent-to-agent and user-to-agent communication [26][27] - LinkedIn developed agentic memory with scoped and layered memory types (working, long-term, collective) [29][30] - LinkedIn implemented a centralized skill registry, allowing agents to discover and access skills developed by different teams [34][35]

[Music] I'm Paul Rasgitis and I'm the tech lead for AI products at Modern Treasury. Modern Treasury is the payment operations platform built for the instant economy. At Modern Treasury, our goal is to transform how teams track and move money.Recently, we launched Modern Treasury AI, a horizontal platform that includes chat, task management, monitoring, and an AI agent designed specifically for financial operations. The agent is just one part of this broader experience, but it plays a key role in how users i ...

All right. Hello everyone. Uh, thanks for being here and joining us on this nice Wednesday afternoon.Uh, my name is Matasanis and this is my colleague. Hey folks, I'm Sorup Sherhhati. And today we're going to present how we built AI developer tools at Uber uh, using Langraph.So to start off, a little bit of context. Um Uber is a massive company serving 33 million trips a day across 15,000 cities. And this is enabled enabled by a massive code base with hundreds of millions of lines of code.And it is our job ...