Know when your AI agent becomes a liability

Ollive connects to the production logs and traces your agents already generate and looks for behavior that can create legal, regulatory, compliance or contractual exposure. That includes things like unsupported claims, missed escalations, unauthorized commitments, data disclosures, missing required disclosures, and actions outside an agent's authority.

The point is not simply to tell you that an agent behaved strangely. It is to show when what the agent did can become a liability for your company.

Observability tells you what happened. Evals tell you how the agent performed. Security tools look for threats and abuse. Ollive uses that underlying telemetry to ask a different question: did what the agent said or did create legal, regulatory or contractual exposure?

Ollive is designed to sit on top of the stack you already have, not replace it.

If your agents already produce logs or traces, you’re most of the way there. Ollive connects to your existing observability stack, whether that’s Langfuse, Braintrust, LangSmith, Arize, Galileo, Helicone, or an in-house system, and uses the logs and traces you already generate to understand what your agents saw, said, and did in production.

Not when Ollive is used only for monitoring and detecting risks. Ollive can start by reading the traces your agents already emit, so you do not need to rebuild your agent or move your engineering workflow into another platform.

Inline protection is a separate layer: when enabled, it can sit in the execution path to pass, block, redact or escalate an output or action. That deployment is handled separately from production monitoring.

Enterprise buyers increasingly want more than a policy or a point-in-time assessment. They want to know what your agents are doing in production, what happens when something goes wrong, and who ultimately carries the financial risk.

Ollive gives you production evidence you can take into those conversations: what is being monitored, which material events were detected, and how they were handled. With AI liability coverage, Ollive supports an Assurance Certificate showing that defined contractual liability to an enterprise customer is financially backed.

Those policies remain important, but AI-agent failures do not always fit neatly into their traditional triggers. A customer-support agent can promise a refund it was never authorized to offer. A healthcare agent can miss a required escalation. An agent can expose information in an output without a conventional security breach.

Depending on the claim and the wording, existing policies may respond to some of these events, but the fit can be uncertain. Ollive's insurance fits around the liability created by the agent's behavior itself.

Ollive needs access to the production telemetry required to understand agent behavior, which can include sensitive or regulated information depending on the workflow.

Data handling is therefore part of the deployment architecture, not an afterthought. For regulated environments such as healthcare, Ollive's operating model includes appropriate agreements, redaction at the ingestion boundary, and defined retention requirements.

Ollive is built for AI companies with customer-facing agents already running in production, including voice, chat and workflow agents where an incorrect output or action can create real third-party liability.

We work across healthcare, customer support, legal, financial services, insurance and other regulated or high-consequence workflows. Ollive is most valuable when AI risk starts showing up in enterprise sales, legal or procurement conversations through questions about controls, indemnities, liability limits, and who ultimately pays when an agent causes a loss.