Graft AI

Graft AI connects at the interface, using vision, accessibility signals, and UI state to understand what's actually on screen at any given moment. That means a system with no API can still become agent-callable. For teams staring at a green-screen mainframe or a stubborn desktop tool, this is the part that makes the rest possible. No API? Not a dealbreaker.

Each workflow compiles into a versioned tool with a clear schema, typed inputs, and defined outputs. An agent calls create_invoice with a customer ID, an amount, and a currency, and knows what it gets back. Versioning matters here. When the UI assumption changes, Graft isolates the affected action and flags it for a retest instead of letting the whole thing drift.

Roles, boundaries, and approvals are part of the tool, not an afterthought. A write action can require approval before it runs, so an agent doesn't quietly change production data. The effect is spelled out up front, for example that a create action makes exactly one record, which keeps the blast radius small. Small blast radius. That's the whole game.

Every action returns evidence tied to the source system, such as the source record plus the ID of what was created. That's what makes agent actions auditable. You can confirm the effect happened in the real system rather than trusting a model's summary of what it thinks it did. Proof beats trust.

Governance here means roles, boundaries, approvals, and an audit trail all describe the same action. That's the point of AI agent governance baked into a tool contract: an approved action has a defined effect, a required approver, and a record of what changed. Auditors get specifics, not vibes.

Graft AI logs each step with full context and respects the roles and constraints of the environment it runs in. You get a running view of what agents created, updated, or cancelled, and when. Full context, every time. For anyone who has to answer to an auditor, this is the difference between a demo and something you'd actually deploy.

Security controls are evaluated for the workflow and the environment, not applied as a blanket policy that treats every action the same way regardless of risk. Why does that matter? Because a payroll workflow and a support lookup don't deserve the same guardrails. Data handling is scoped too, keeping inputs, evidence, and logs limited to the workflow being evaluated. If your environment has strict data rules, that per-workflow approach is easier to justify than a generic cloud connector.