Forward Deployed Engineering at Cursor — Pauline Brunet

This talk explores the concept of AI models training future AI models, focusing on how this iterative process can lead to significant advancements. It delves into the practical implications of this approach for developers and the potential for AI to accelerate its own development cycle. The core idea is that by leveraging AI to improve AI, we can unlock new levels of performance and capability.

This talk details a 30-day initiative at Automattic where employees were encouraged to pair up and build/ship projects, leveraging AI tools. The experiment aimed to accelerate product development and foster new skills, particularly for designers, by allowing them to own the entire product lifecycle from ideation to deployment. The speaker shares personal experiences building three distinct projects, highlighting the shift in their role from designer to design engineer and the broader implications for organizational agility.

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.