Graft AI

Graft AI is designed to bridge the gap between modern agent tools and the reality of how companies operate. It caters to businesses that rely on legacy applications, internal tools, and complex workflows that are not easily accessible via clean APIs. The primary purpose of Graft AI is to create a stable and reliable operational map that agents can use to perform tasks efficiently and securely. The problem Graft AI addresses is the inherent fragility of agent tools when interacting with systems that lack modern APIs. Many existing agent solutions assume a clean, well-documented interface, which is rarely the case for established enterprise software. This disconnect leads to unreliable agent performance, broken workflows, and significant maintenance overhead as underlying applications change. Graft AI aims to solve this by providing a robust layer that abstracts away the complexities of these legacy systems. One of Graft AI's core features is its ability to learn and map existing workflows. It observes how tasks are performed, including decisions, exceptions, and success conditions, to build a dynamic operational map. This map serves as the foundation for creating stable tools for agents, ensuring that even when the underlying user interface changes, the agent's interaction remains consistent. This feature is crucial for maintaining operational continuity and reducing the impact of software updates on agent productivity. Another key capability is the provision of stable tools with built-in governance. Graft AI equips agents with functionalities such as permissions, approvals, audit trails, and verification. This ensures that agent actions are controlled, traceable, and compliant with business policies. By integrating these controls directly into the agent's workflow, Graft AI enhances security and accountability, preventing unauthorized actions and providing a clear record of all operations. Graft AI also excels at handling UI drift and changes. Unlike traditional RPA tools that often break when an interface element moves or changes, Graft AI detects these discrepancies. It can often repair the workflow automatically or flag it for human review, ensuring that the agent interface remains stable and functional. This self-healing capability significantly reduces downtime and the need for constant manual intervention. The product's approach involves creating a living operational map that continuously updates. Graft AI monitors real workflow executions, comparing them against the current map. When changes are detected, it proposes diffs, tests them in shadow mode, and seeks human review only for risky or uncertain modifications. This ensures the map evolves with the company's operations, with normal work keeping it current and human oversight reserved for significant exceptions. The benefits for users include increased agent reliability and efficiency, reduced maintenance costs associated with broken workflows, and enhanced security and compliance through built-in governance features. By abstracting the complexities of legacy systems, Graft AI allows agents to perform tasks more consistently and effectively. Concrete use cases for Graft AI include automating tasks within ERP systems, interacting with internal portals, processing data from spreadsheets, and managing workflows trapped behind older application interfaces. For example, an agent could be tasked with processing customer refunds, and Graft AI would ensure it navigates the necessary legacy systems correctly, handles exceptions, and logs the transaction, even if the underlying software is updated. Graft AI is positioned as an infrastructure for agents, connecting to the tools companies use and extracting domain knowledge to create a usable operational map. It is designed for businesses struggling with agent integration into complex, non-API-driven environments. The product is currently in early access, inviting users to provide feedback and shape its development. In summary, Graft AI provides a robust solution for deploying agents in complex operational environments by creating a dynamic, self-updating map of company workflows, ensuring stability and governance even when underlying systems change.