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Today Hone launches Engines: AI that owns business outcomes.
Chatbots give answers. Agents do tasks. Engines deliver outcomes, and we hold them responsible for creating real value, measured in actual dollars instead of tokens or vibes.
You point it at a business metric, and the Engine figures out the rest. It onboards itself, builds the necessary agents, skills, and memories, collaborates with the right people, and gets better with experience.
From AI leaders to global enterprises, our customers have already used Engines to:
• Uncover millions in procurement savings
• Optimize product funnels
• Manage credit risk
• Increase qualified pipeline in new territories
• Accelerate implementations and improve account management
To scale this impact across the economy, we’ve raised a $60M seed led by Benchmark and Index with participation from Elad Gil, Hanabi, Definition, Diffusion, Lux, SV Angel, and Align.
AI’s raw capabilities are far ahead of the value it creates in the real world, and Engines will help close the gap.
Here’s how they work…
Hone retweeted
Pushing the frontier of persistent multiplayer agents at @hone carries a ton of interesting challenges:
• How can we safely and securely generate organizational learnings from engine sessions that had access to personal connectors?
• How can we separate access (reads) from authority (writes)? I may want an engine to draft emails for 20 different users without having access to their personal connectors.
• When should a learning be considered a user preference versus an organizational best practice?
historically, most of our internal agents (openswe for coding, gtm agent for outreach) have acted "on behalf of" whoever is using them
but as they start to do more proactive, background work... I wonder if that should change. eg i really like the idea Hone put forward of agents that are striving towards a goal (xitter.cf/hone/status/2108218574…)... these kind of seem like coworkers, and i wonder if they should be treated as such
“You can give the Engine a goal or an ambiguous workstream, and it creates the loop to understand, measure, iterate, and self-improve.”
Hear how @ChrisPrinz at @modal is rethinking GTM with Hone Engines that go beyond executing tasks to own entire workstreams, continuously measure results, and improve performance.
See what Engines can do for your organization at Hone.com.
Deploying AI that owns real outcomes at scale requires AI that can deploy itself.
That means a harness that adapts to each new use case from user and environment feedback, without an engineer in the loop.
Self-improvement only compounds if each change keeps what the last one fixed. Engines make sure of it by turning production failures into curated eval sets that simulate the enterprise environment with high fidelity, so every past failure becomes a test the next change has to pass.
More detail here:
hone.com/blog/closing-the-ga…
We’re researchers, operators, ex-founder, and engineers who believe AI should be held to a higher standard.
We’re building systems robust enough to close the gap between what frontier AI can do and the economic value it creates. We measure our work by results in the real world.
Visit us at hone.com to start your Engines - or help us build them.
Announcing Hone
Intelligence has become abundant. Yet the world looks remarkably similar to how it did five years ago. With every model release, the gap between what frontier AI can do and the economic value derived from it widens.
Closing the gap requires re-organizing work around organizational outcomes, not individual tasks. Hone builds AI that creates, orchestrates, and improves agents and software continuously to own organizational outcomes over weeks and months.
We are ex-founders and early core contributors to Cognition, Mercor, Ramp, and OpenAI. We obsess over real-world value, not theoretical benchmarks.
Our core beliefs on closing the gap in the thread below.