Most agent tools assume clean APIs. Graft starts where companies actually work: legacy apps, internal tools, and workflows trapped behind screens. It learns how the work gets done, turns it into a living operational map, and gives agents stable tools with permissions, approvals, audit trails, and verification built in. When the underlying UI changes, Graft detects the drift and repairs the workflow without breaking the agent interface.
Hey PH 👋
I started Graft after realizing that the hardest part of deploying agents at work is not the model. It is the software around it.
Real companies run on ERPs, desktop apps, internal portals, spreadsheets, and years of operational knowledge that agents cannot reliably understand or use.
Graft began as a way to turn those interfaces into stable tools for agents. While building it, the idea grew into something bigger: a living map of how a company actually works. Graft learns the workflow, decisions, permissions, exceptions, and success conditions, then gives agents a safe way to do the work with approvals, audit trails, and verification built in.
When the underlying software changes, the agent-facing tool stays stable.
We are launching early because we want to learn from the people actually building and operating agents.
What is one system or workflow your agent still cannot reliably use today?
docs go stale in a week so i like the living map idea. how does it stay updated without me babysitting it?
Graft will be helping f500's transform their legacy applications into operational knowledge basis for agents to use. Agents can interact with their legacy software and help them transition into agent native software, this is a shift we're seeing but companies fail because the data is in people's head as domain knowledge so we're building the infra to make it operational by agents.
Building a receipt-scanning feature right now and even at my tiny scale, the "verification" part is the hard bit — I ended up schema-validating every LLM response and failing closed on anything unparseable, because silently-wrong output is worse than an error. Curious how you handle the case where the workflow succeeds mechanically but the result is semantically wrong — is verification rule-based per workflow, or learned?
Operations as a map agents can walk is close to what I do per-client, structured facts an AI can't step outside of. Mine is one brand, yours is a whole company, and I suspect the hard part scales badly. How do you keep the map current when the operations change weekly?
One thing that would help us adopt this faster is a visual timeline view inside the operational map, so we can scrub through past workflow executions and see exactly where drift or failures happened. Right now troubleshooting at scale feels like guesswork.
the framing that the model was never the hard part is honestly accurate for most companies, the real question is whether the "living map" stays correct as workflows quietly drift over months, not just when the UI visibly changes.
spend months trying to get an agent to reliably click though our old internal ticketing system before giving up, this is the exact problem.
nothing in the pitch about workflows that need a human judgement call partway through, is that out of scope for now?
feels adjacent to Browser Use in spirit, just aimed at ERPs and desktop apps instead of the open web.
building stable tools instead of chasing every API makes sense, but that shifts a lot of ongoing maintenance onto graft's side to keep those maps accurate as companies change.
if the underlying business logic changes and not just the screen, does the agent, facing tool silently start doing the wrong thing instead of breaking?
approvals + audit trails baked in from day one is the part that'll actually get this past an IT review, not the AI framing.
Graft turns real company operations into a shared, continuously updated layer for agents. We've seen companies transform to the AI native era but fail in operations because of institutional knowledge, and graft solves that.
About Graft AI on Product Hunt
“Turn company operations into a living map for agents”
Graft AI launched on Product Hunt on July 16th, 2026 and earned 111 upvotes and 27 comments, placing #11 on the daily leaderboard. Most agent tools assume clean APIs. Graft starts where companies actually work: legacy apps, internal tools, and workflows trapped behind screens. It learns how the work gets done, turns it into a living operational map, and gives agents stable tools with permissions, approvals, audit trails, and verification built in. When the underlying UI changes, Graft detects the drift and repairs the workflow without breaking the agent interface.
Graft AI was featured in SaaS (43.1k followers), Artificial Intelligence (473.7k followers) and Vercel Day (26 followers) on Product Hunt. Together, these topics include over 158.2k products, making this a competitive space to launch in.
Who hunted Graft AI?
Graft AI was hunted by Yashas Gunderia. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Yashas Gunderia