\"The model trains the next model\" — Lee Robinson, Cursor, SpaceXAI

This talk introduces adaptive engineering as a new philosophy for AI development, moving beyond fixed, pre-defined harnesses. It argues that as AI models become more powerful and interact with the dynamic, messy real world, static harnesses will become brittle. Adaptive engineering allows the harness to emerge and adapt during the engineering process, with the engineer's role shifting to designing constraints rather than dictating every step.

This talk argues that building agentic AI systems requires the same core engineering discipline as traditional software development, just with different primitives. Instead of focusing solely on using agents to write code, the emphasis shifts to architecting complex agentic systems. This approach allows engineers to leverage their existing skills in systems thinking, workflow design, and modularity, recapturing the thrill of building by operating at a higher level of abstraction.

This talk details the journey of transforming a large engineering organization into an autonomous one by leveraging AI agents. The core thesis is that true impact from AI enablement comes not just from individual engineers using tools, but from integrating agents deeply into the entire development workflow, enabling them to produce shippable results with minimal human oversight. This transformation involves moving engineers through maturity stages, from basic code generation to complex task delegation and multi-agent collaboration.

This talk introduces advancements in AI agents for computer use, moving beyond earlier, screen-dominating models to more integrated and efficient background operations. It highlights the development of tools like Quad Driver for seamless OS interaction across platforms and Kuabench for robust agent evaluation. The discussion also touches upon optimizing infrastructure for agent training to reduce costs and improve GPU utilization.

This talk proposes that AI harnesses are evolving beyond their initial function of managing AI agent workflows. They are increasingly integrating with collaboration tools and becoming more proactive, effectively transforming into "Claws." This evolution is driven by the capabilities of modern coding agents and the desire for AI assistants to be more present and available within team workflows.