Production AI fails after deployment — costs spike, agents loop, prompts drift, data leaks, and no one can explain why. AgentGuard is the trust layer that observes, secures, evaluates, and governs every decision your AI makes in production.
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 trace, the evaluation, the security event and the compliance evidence live in four different tools, or nowhere. The AI worked; the trust failed. Every production AI system forces the same four questions.
Latency, errors, and runaway cost — traced to the exact request, prompt, and tool call that caused them.
Prompt injection, data leakage, and unsafe agent actions — attempts detected and blocked in your own request path.
Hallucinations, incorrect decisions, and behaviour that quietly drifts as models and prompts change.
Audits, compliance reviews, and risk sign-off — answered with evidence instead of someone's memory.
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 answers all four questions at once.
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.
Every plan includes all four pillars. You scale on volume and retention — not on how much trust you're allowed to see.
Public pricing is being finalized — talk to us for a quote scoped to your deployment. 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.
Bootstrapped. A team of about fifteen engineers. First 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.