Control what agents may do, verify every outcome, and keep the evidence behind every decision.
Same agent, same question — "where's my refund?" The only difference is whether anyone can see, and trust, what it just did.
Illustrative — example UI, not customer data. ▸ Tap Before and With AgentGuard — it also cycles on its own.
A confidently wrong answer. An agent that acted on the wrong document. A field of sensitive data in a response. Days later someone asks why — and the answer lives in four different tools, or nowhere. AgentGuard answers it from one record.
A permission model on the action, not a filter on the output. Every model call, tool call and action is checked against what that agent is permitted to do — server-side, in the request path, before it executes.
Hallucinations, wrong decisions and behaviour that drifts as models and prompts change — measured against your own criteria, so a regression blocks the release instead of reaching a customer.
Audits, compliance reviews and risk sign-off, answered with the record rather than someone's memory — the decision, the policy that applied, the verdict and the evidence, exportable.
Underneath, that is tracing, guardrails, evaluation and governance evidence — four subsystems, one record. You buy the three outcomes; the pillars are how they are built.
Declared per agent. Enforced server-side, in the request path, before the tool runs.
Approval required is human-in-the-loop as a guardrail — the reviewer sees the action, the arguments, and the policy that stopped it.
A trace tells you what the model said. It doesn't tell you whether you can trust it. AgentGuard runs across the whole request — and attaches everything to a single interaction, so one record shows what was allowed, what it cost, and whether the answer held up.
Illustrative single interaction — example values, not customer data. Every instrumented decision carries its own cost, latency, evaluation, security and compliance context; the Trust Score is gated on the weakest critical issue and shows insufficient data when the evidence isn't there.
Model-agnostic, framework-agnostic, cloud-agnostic. AgentGuard instruments the AI you've already built — no re-architecture. Tracing attaches via the SDK; guardrails run inline in your own request path.
Product and framework names indicate compatibility only — not partnership or endorsement. All trademarks belong to their respective owners.
Observe what the system did, secure it against attack and leakage, evaluate whether it was right, then govern it with evidence. The Trust Score is where that story lands — not where it starts.
Every instrumented interaction, traced end to end.
Guardrails enforced, every block recorded.
Correctness and quality, scored continuously.
Evidence mapped to the frameworks you answer to.
One honest number — gated, "insufficient data" when the evidence isn't there.
Illustrative interface — a representative layout, not live product data or a customer's numbers.
Most platforms produce dashboards. AgentGuard produces trust evidence.
The Trust Score is gated like a credit score — one unresolved critical issue caps it — and reports "insufficient data" rather than invent a number. Compliance shows coverage from live evidence, not a certification or legal assurance.
The same interaction means something different to each team that touches it. AgentGuard gives all three one record to work from — no reconciling three tools, three exports, and three versions of the truth.
SaaS or self-hosted, in your own cloud or ours. You scale on volume and retention — never on how much of your own evidence you are allowed to see.
SOC 2 Type II in progress.
Two decades building the platforms regulated, high-stakes enterprises depend on taught us to monitor everything — every server, every service, every millisecond. Then AI arrived, and the most important decision in the system was suddenly made by something nobody could fully explain. Infrastructure had monitoring. Applications had monitoring. Trust had nothing. AgentGuard is the layer we went looking for and couldn't find.
We built the trust layer we spent twenty years wishing we had.
Founder-led and bootstrapped. Customers in production.
In thirty minutes, we'll walk you through a live Trust Profile — the trace, the guardrails, the evidence, and the score behind them — on a system that looks like yours. No slideware. Your questions, straight answers.
Trust every AI decision.