An SDK that closes the loop. An AI judge evaluates every step of every production trace — not just the final output — proposes a code-level fix, then validates it against your trace history before you ship.
AI engineers ship agents that pass evals and fail silently in the wild. The first signal is a customer screenshot. The fix lands blind — and you have no way of knowing what else broke unless you build expensive ground truths for every step of every workflow.
Your suite goes green while the agent invents a tool argument, skips a policy check, or hallucinates a refund reason. The output looks fine. The trace tells the truth.
By the time a human sees the bug, the agent has already executed it across hundreds of conversations. You're not debugging — you're triaging what already shipped.
To know whether a patch broke something else, you'd have to label every step of every workflow. Nobody has the budget. So fixes go in, side effects emerge, and the loop never closes.
Helix wraps your agent with an SDK that watches production behavior, evaluates it against your code's intent, and proposes — then validates — code-level fixes. No labeling pipeline. No babysitting.
Drop in our SDK. Helix reads your code to learn what each agent should do, and ingests traces from your existing observability.
$ npm i @helix/sdk
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An AI judge evaluates every reasoning step in every production trace — not just the final output — and flags silent failures and intent mismatches against your code.
On every deviation, Helix writes a code-level patch — not a prompt nudge. The fix lands in your repo as a diff with the failing trace attached.
Helix replays the proposed fix in a sandbox against your trace history and surfaces an accuracy delta before merge. No regressions, no surprises.
A support agent issued a refund on a delivered order without a manager check. Evals were green. The customer wasn't. Here's what Helix saw, what it proposed, and what shipped.
Refunds for delivered orders require manager approval per policies/refunds.md:42. The agent issued a refund without invoking the approval check.
Observability platforms get you halfway. They surface that an output looks bad, but stop short of explaining what intent the agent missed, proposing the patch, and verifying it doesn't break anything else.
We met at King's College London studying Computer Science. Since then we've taken two products from zero to one — a Vanta-for-GDPR and an AI Security Questionnaire / RFP platform — closing 120+ customers, from early-stage startups to a top-20 global law firm. Helix is the third.
Seven years shipping AI-shaped products to enterprise buyers. Learned the hard way that technical literacy is a moat when your buyer is an engineer. Writes code in every part of Helix.
Met co-founder 01 in lecture, never stopped building together since. Believes the only way to ship reliable agents is to dogfood the loop — Helix is built with Helix. Writes code in every part of Helix.
Drop your email — we'll reach out the day your stack is ready to plug in. Limited alpha spots while we validate verification accuracy on real production failures.