unOpen.AI
In closed beta: Roxy, a private AI assistant with permanent memory running on infrastructure the user owns. Brand, site, product, backend and conversational design.
AI agent guardrails are the checks that run around an agent: on what it reads, the evidence it cites and the actions it proposes. With Jev, each check is a focused, fast judgement. This is for teams letting agents act in real systems. Your application still owns permissions, approval rules and what happens when a check is uncertain.
Proposed integration pattern · Evaluate on your own traffic
Introduction transcript. Qubi: An agent that can act needs a second look before it reads, answers or acts. Incoming text is screened for hidden instructions, and stays labelled as untrusted. Claims are checked against their source, and each proposed action against the request. Unsure cases go to a person; permissions stay in code, and every step is logged.
Before a model reads external content, assess whether it contains an instruction trying to redirect the workflow. Before an answer reaches a user, check whether the cited passage supports the claim. Before a tool runs, compare its proposed action with the user’s request.
These are separate questions with separate consequences. TypeSafe’s guardrail cookbook demonstrates screening messages and using probability thresholds to route the result. We would adapt the questions to your policies and measure both missed hazards and unnecessary blocks.
A model check complements access controls. It cannot authorize a refund, grant database access or make untrusted content safe by declaration. Those boundaries belong in code, with a record of who requested each action.
Imagine an agent that drafts replies and can change delivery preferences. A customer message asks for an address update but includes unrelated instructions to export the account database. The application exposes only the permitted delivery tools; a Jev check can flag the conflicting request for review.
For the reply, a separate check compares its delivery claim with the carrier record. For the address change, code verifies account ownership and asks for the required confirmation. None of these checks should silently substitute for another.
This is a proposed design, not a claim that Jev catches every attack. TypeSafe publishes related examples for classifying retrieved passages and checking citations.
request + evidence
intent · support · risk
allow · review · block
identity + scope
approved actions only
Build the evaluation set from representative normal traffic, known bad examples and difficult boundary cases. Report false positives and false negatives separately. An aggregate pass rate hides the difference between an overblocking system and a permissive one.
Test ambiguous phrasing, conflicting evidence, provider errors and unavailable checks. Decide which operations pause for review and which can safely continue through a deterministic fallback. Track the added response time as part of the user’s complete task.
TypeSafe explicitly lists adversarial inputs among the model’s known limitations. Treat a model change as a reason to re-evaluate the policy. Our prompt-injection guide covers the surrounding system controls.
No. A model can misclassify a request or miss an attack. Use its decisions alongside least-privilege tools, validated arguments, approvals and monitoring.
Yes, when they evaluate the same available state independently. A check that needs a tool result must wait until that result exists.
Use held-out examples and the consequences of mistakes. Different actions need different thresholds; record review load as well as detection quality.
What we built, what stage it is at, and a link to the live thing wherever there is one.
In closed beta: Roxy, a private AI assistant with permanent memory running on infrastructure the user owns. Brand, site, product, backend and conversational design.
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