PALs

AI Agents can generate impressive outputs - code, slide decks, documents – but they fail to follow the trust processes that humans rely on. Everyone has an established workflow that is honed after years of practice. If agents cannot understand or adapt to these workflows, people are forced to change how they work to use AI, which causes friction, mistrust, and low adoption. How can we get agents to not just have good outputs but follow trusted processes that are already known to a user? We propose building process-aware agents that can learn and adapt to a user’s workflow. Our approach is to gather version history across tools like Google Docs, Google Slides, Github commit histories, and chat logs. We will then use process mining techniques to extract workflows, like iteration cycles and checkpoints, to represent the process as a structured workflow graph. Agents will follow this mined workflow, ask clarifying questions, and adapt on-the-fly to the user’s working style in order to build a collaborative partnership over the course of a long-term project. We aim to show that process-aware agents are more trusted and produce outputs of code, slides, and documents that are personalized to that person’s working style, demonstrating that AI can adapt to people, rather than forcing people to adapt to AI.