Companies like Intuit, Capital One, LinkedIn, Stanford University and Highmark Health are quietly putting AI agents into production, tackling concrete problems, and seeing tangible returns. Here are the four biggest takeaways: 1) AI Agents are moving into production, faster than anyone realized A KPMG survey released on June 26, a day after our event, shows that 33% of organizations are now deploying AI agents, a surprising threefold increase from just 11% in the previous two quarters. Intuit, for instance, has deployed invoice generation and reminder agents in its QuickBooks software. Businesses using the feature are getting paid five days faster and are 10% more likely to be paid in full. Even non-developers are feeling the shift, building production-ready software features with power of tools like Claude Code. 2) The hyperscaler race has no clear winner as multi-cloud, multi-model reigns Enterprises want the flexibility to choose the best tool for the job, whether it’s a powerful proprietary model or a fine-tuned open-source alternative. This trend is creating a powerful but constrained ecosystem, where GPUs and the power needed to generate tokens are in limited supply. 3) Enterprises are focused on solving real problems, not chasing AGI Highmark Health Chief Data Officer Richard Clarke said it is using LLMs for practical applications like multilingual communication to better serve their diverse customer base, and streamlining medical claims. Similarly, Capital One is building teams of agents that mirror the functions of the company, with specific agents for tasks like risk evaluation and auditing, including helping their car dealership clients connect customers with the right loans. 4) The future of AI teams is small, nimble, and empowered Small team structure allows for rapid testing of product hypotheses and avoids the slowdown that plagues larger groups. As GitHub and Atlassian noted, engineers are now learning to manage fleets of agents. The skills required are evolving, with a greater emphasis on clear communication and strategic thinking to guide these autonomous systems. This nimbleness is supported by a growing acceptance of sandboxed development. The idea is to foster rapid innovation within a controlled environment to prove value quickly.