The knowledge layer for specialized AI
Kinesthetic provides the infrastructure and tooling to ergonomically build agents that can learn how to improve themselves.
See exactly how your AI behaves
Auditing a domain agent usually means reading a giant prompt and guessing what the model attended to. Kinesthetic makes the specification first-class and queryable: ask in plain language how a scenario is handled and get back the specific ground-truth artifacts that govern it, each with its scope, owner, and full text. For any production trace, you can see exactly which artifacts were retrieved into the context, and at which version, so an audit is a handful of artifacts to read, not a 40k-token prompt to reverse-engineer.
Scale knowledge past the context window
A prompt has a ceiling; a knowledge base doesn't. Kinesthetic moves your specification out of the context window and into a KB the agent retrieves from: no input-token limit, no context rot as it grows. At inference time, our knowledge engine assembles a small, task-tailored context instead of stuffing in the corpus. The spec can grow arbitrarily as you continue to refine and expand the behavior your agent can perform.
Improve from information in any form
Most improvement tooling only ingests data shaped for optimization: reward signals, labeled pairs. Kinesthetic takes whatever form your insight already arrives in: a plain-English correction, a batch of annotated traces, a failure-investigation report, a new feature spec. Coding agents are great for code, but they can't safely make these edits across large instructions. Our agent built specifically for this purpose can directly resolve many tasks that currently end up as tickets.
The same "no bulk export" message is written three different ways — "not available," "not supported yet," and a sales hand-off — so searching for "bulk export" only catches one of the three. And your note is really two fixes, not one: correct the capability wherever it's stale, and loosen the blanket apology. Staged across 4 artifacts:
Give the agent optimal context
The Knowledge Engine provides fewer, better tokens tailored to the task instead of the whole corpus, which lifts answer quality while cutting wasted agentic search. Additionally, our engine learns from its actions. Using all feedback on incorrect actions and examples of correct actions, the engine captures learnings and provides them to your agent at inference.
It's delivered as a managed service: state-of-the-art retrieval and learning methods from frontier research, on tap. Your team doesn't have to keep solving the hard, general problems of running and improving a knowledge base; we handle those, so your people focus on the genuinely bespoke needs of your product. And because the agent gets context it can act on immediately, you can start capturing the gains of smaller and open-source models.
Version-control your specification
Every change is a diff on specific artifacts, staged on a branch and merged through a pull request, with a PR note the system drafts from your conversation. Each artifact carries its own history you can trace back to the PR, author, and date that introduced it, so you always know who changed what, when, and why.
Validate before you ship
Before a change ships, run it against real sampled traces in the Playground and diff your branch's behavior against production: see exactly which inputs change and read the agent's reasoning on each. Safety checks then run over the whole branch: a behavioral diff that quantifies what moved, and a consistency check that flags inconsistencies or unspecified behavior across artifacts.
A clear authority gradient
Kinesthetic enforces a strict authority gradient. Human-authored ground truth is the single source of truth; everything the system derives from it (indexes, structures, the assembled context, distilled playbooks) is disposable and regenerable. When someone corrects the truth, the change flows downward and rebuilds the derived machinery, so nothing downstream ever hardens into a competing source of truth.
Generates your post-training data
Improving the agent and generating training data turn out to be the same activity. Every expert correction, and every trajectory a teacher actually got right, is captured as labeled, domain-grounded data, the exact policy data you'd train on. The loop that makes the agent better today is quietly accumulating proprietary data no foundation model has seen and no competitor can replicate, so you can decide later what genuinely needs frontier inference and what you'd rather distill into cheaper models, or weights you own.
A durable asset that survives model releases
Most of what teams invest in is perishable: prompts tuned to a specific model, fine-tuned weights, a hand-built harness; all of it depreciates the moment a new frontier model ships or the stack changes. Your ground-truth specification doesn't. It's model- and harness-agnostic knowledge, kept in plain language the agent reads at inference, and the model-specific machinery beneath it regenerates from that truth. So every correction is a permanent deposit that compounds across release cycles instead of resetting with them. Not only is this a better place to put and use knowledge for your system today, that work is also more worthwhile when it looks like investment versus rent.
Multi-tenant, multi-agent by design
One specification can serve many agents and many tenants. Behavior is scoped: shared base rules that apply everywhere, per-agent rules, and per-tenant overrides that store only the difference from the base. The engine retrieves the base and the override together and reconciles them at inference, and you can view the resolved, effective behavior for any agent or tenant. So you share common ground truth across agents while customizing per customer, without forking the whole spec for each one.