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]

Company Overview & Strategy - BlackRock manages over $11 trillion in assets, aiming to enhance financial well-being through its Aladdin platform [3] - Aladdin is a proprietary technology platform unifying the investment management process for both public and private markets, serving institutional and retail investors [3] - BlackRock aims to increase productivity, drive alpha generation, and personalize user experience through AI, particularly with the Aladdin Copilot initiative [5][6] Aladdin Copilot & Architecture - Aladdin Copilot is embedded across 100 front-end applications, proactively surfacing relevant content and enhancing productivity [6][7] - The architecture supporting Aladdin Copilot uses a plug-in registry, allowing 50-60 Aladdin engineering teams to integrate their functionalities [9][10] - The system uses Langchain for orchestration, incorporating input and output guardrails for responsible AI moderation, including off-topic, toxic content, and PII handling [16][24] - GPT4 function calling is used for planning and action within the orchestration node, iterating until a final answer is obtained [19] Evaluation & Testing - BlackRock emphasizes evaluation-driven development, similar to test-driven development in traditional coding, to ensure the system's reliability [25] - The company tests every system prompt and intended behavior by generating synthetic data and building evaluation pipelines [26][27] - End-to-end testing capabilities are provided to developers, allowing them to configure testing scenarios, including application context, user settings, and multi-turn scenarios [30][31] - Ground truth data is collected for every plug-in to guarantee Aladdin Copilot's performance and routing in various scenarios [33]

[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 ...

Agentic Platform Engineering Implementation - Outshift by Cisco is redefining platform engineering with agentic platform engineering, aiming to automate workflows and allow platform engineers to focus on high-value work [1][2] - The company built Jarvis, a genai-powered multi-agentic system, to automate tasks and improve efficiency [2] - Langraph, combined with Langsmith's observability tools, enables debugging agentic applications and improving reasoning capabilities at scale [6] Efficiency Improvement - Implementing Jarvis allows developers to self-service through genai-powered automation, eliminating manual toil [4] - CI/CD pipeline setup time reduced from a week to less than an hour [7] - Resource provisioning time (e.g., S3 buckets, EC2 instances, access keys) reduced from half a day to nearly instantaneous [7] - The company has eliminated unnecessary back and forth between developers and SREs [7] Workflow Transformation - The company shifted from traditional automation to agentic reasoning-based workflows by adopting Langraph [5] - Developers interact with Jarvis for platform-related questions and configurations, retrieving information autonomously [8] - The new system allows the company to handle a higher volume of requests with the same team while reducing burnout [8] Technology Evaluation - Langraph's tight integration with Langsmith, especially for debugging and evaluations, is a significant advantage [9] - The company found Langraph to be superior compared to other agentic solutions or custom-built alternatives [9]

I'm Ben Kuss and I'm here to talk about the lessons that we learned at Box building um agent architectures. Um if you don't know Box, uh we are a B2B company. Um many people know us uh from our content sharing, but we think of ourselves as an unstructured data platform.Uh we have uh we typically deal with large enterprises. So like um uh big companies across Fortune 500. We have over 115,000 companies, tens of millions of users and our customers have given us uh entrusted us with over an exabyte of their da ...

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 ...