[music] Thanks for joining me in one of the later day sessions. Looks like we we we kept a lot of people here. This is a nice full room.I'm great to see it. We're going to go through a lot of content in a short amount of time. So, I'm going to get right into it.If you want to get deeper into any of this stuff, we have published this uh AI strategy playbook for senior executives. And uh a lot of the content that I'm going to go through, I'm not going to have time to get quite as deep, but this is just a nice ...

Agent Experience Definition and Importance - Agent experience is defined as how easily agents can access, understand, and operate within digital environments to achieve user-defined goals [5] - The industry believes agent experience is the only experience that matters because agents will be the largest user base [33] - The industry suggests that if a tool requires human intervention, it hasn't fully addressed agent needs [33] The Shift in Development Tools - 37% of the latest YC batch are building agents as their products, indicating a shift from co-pilots and legacy SAS companies [1] - The industry argues that tools built for humans are for the past, and the focus should be on building tools for agents [3] - The industry emphasizes the need to build tools that enable agents to operate autonomously [12][13] Key Components of Agent Experience - Seamless authentication is crucial; agents should be able to authenticate without exposing passwords [6][7] - Agent-readable documentation is essential, with standards like appending ".md" to URLs and using llm's.txt [8][9] - API-first design is critical, providing agents with machine-native interfaces to access functionality efficiently [10] Daytona's Approach to Agent Native Runtime - Daytona aims to provide agents with a computing environment similar to a laptop for humans [19] - Daytona's initial focus was on speed, achieving a spin-up time of 27 milliseconds for agent tools [21] - Daytona preloads environments with headless tools like file explorers, Git clients, and LSP to help agents do things faster [22] Daytona's Features for Autonomous Agents - Daytona offers a declarative image builder, allowing agents to create and launch new sandboxes with custom dependencies [27] - Daytona provides Daytona volumes, enabling agents to efficiently share large datasets across multiple machines [29] - Daytona supports parallel execution, allowing agents to fork machines and explore multiple options simultaneously [31]