The researchers present aCLAr (Demonstrate, Execute, Validate framework), a system that uses multimodal foundation models to automate enterprise workflows, particularly in healthcare settings. The system addresses limitations of traditional RPA by enabling passive learning from demonstrations, human-like UI navigation, and self-monitoring capabilities. They successfully demonstrated the system automating a real healthcare workflow in Epic EHR, showing how foundation models can be leveraged for complex enterprise automation without requiring API integration.
Stanford researchers developed aCLAr, a novel approach to enterprise workflow automation using foundation models. The system specifically targets healthcare workflows but has broader enterprise applications. Their work demonstrates how to effectively deploy LLMs in production environments while handling sensitive data and complex UI interactions.
Snorkel developed a specialized benchmark dataset for evaluating AI agents in insurance underwriting, leveraging their expert network of Chartered Property and Casualty Underwriters (CPCUs). The benchmark simulates an AI copilot that assists junior underwriters by reasoning over proprietary knowledge, using multiple tools including databases and underwriting guidelines, and engaging in multi-turn conversations. The evaluation revealed significant performance variations across frontier models (single digits to ~80% accuracy), with notable error modes including tool use failures (36% of conversations) and hallucinations from pretrained domain knowledge, particularly from OpenAI models which hallucinated non-existent insurance products 15-45% of the time.
OpenAI's Forward Deployed Engineering (FDE) team, led by Colin Jarvis, embeds with enterprise customers to solve high-value problems using LLMs and deliver production-grade AI applications. The team focuses on problems worth tens of millions to billions in value, working with companies across industries including finance (Morgan Stanley), manufacturing (semiconductors, automotive), telecommunications (T-Mobile, Klarna), and others. By deeply understanding customer domains, building evaluation frameworks, implementing guardrails, and iterating with users over months, the FDE team achieves 20-50% efficiency improvements and high adoption rates (98% at Morgan Stanley). The approach emphasizes solving hard, novel problems from zero-to-one, extracting learnings into reusable products and frameworks (like Swarm and Agent Kit), then scaling solutions across the market while maintaining strategic focus on product development over services revenue.
Medable developed Agent Studio, a comprehensive platform for deploying AI agents in clinical trial operations to address the lengthy drug approval process that currently takes over 10 years. The platform enables both internal teams and customers to build configurable multi-agent systems that tackle problems like document classification in electronic trial master files and clinical research monitoring across multiple data systems. By taking a platform-first approach with support for model-agnostic agents, RAG knowledge integration, MCP connectors, workflow functionality, and robust evaluation frameworks, Medable has deployed multiple agentic applications that help clinical research associates process over 80,000 documents per year and monitor data across 13+ disparate systems, with the ambitious goal of reducing clinical trial timelines from 10 years to one year.
