Multi-agent AI
Multi-agent AI on Beyond Market Intelligence: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on multi-agent ai in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around multi-agent ai, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Why Capital One built its multi-agent AI platform around open-weight models
At VB Transform 2026, Capital One’s Kel Vanee detailed the bank’s strategic shift toward building AI, not just using it. Capital One constructed a scalable, multi-agent AI platform centered around deeply customized open-weight models, leveraging proprietary data for enhanced accuracy and extensibility. This approach, underpinned by prior investments in data transformation and cloud adoption, enables the bank to optimize workflows, from fraud detection to customer service, and even automate internal infrastructure tuning.
A Guide to Saving Token Usage with Multi-Agent AI
Scaling multi-agent AI can unlock incredible potential, but escalating costs are a common concern. This guide outlines four key strategies to optimize token usage and ensure efficient scaling. Learn how to streamline your architecture without sacrificing performance, enabling you to explore increasingly complex AI applications. We’ll equip you with practical techniques to maximize your investment and drive tangible results. For a deeper dive into agent architecture and real-world API performance, see our article, "Does MiniMax Agent Actually Make Work Easier?".
Enterprise AI agents can't talk to each other, can't be trusted with permissions, and can't be audited — 5 startups are already fixing that
Enterprise AI agents promise transformative work capabilities, but a crucial infrastructure gap remains: ensuring secure communication, reliable authorization, and comprehensive auditing. Five innovative startups are addressing this challenge, focusing on orchestration, observability, connectivity, and security. From BAND’s coordination layer to Arcade's secure runtime, these solutions are laying the groundwork for a future where AI agents collaborate seamlessly and securely. As Meta envisions billions of personal AI agents within five years, this foundational work is increasingly vital.