The trust problem in agentic AI

Software used to be predictable. You called a function, and it returned a value - deterministic, auditable, explainable. Now, with AI agents, that is changing. Agents make decisions, retrieve context, weigh options, invoke tools, escalate to sub-agents, and act. The question that follows every deployment is the same: "Can we trust what this system is doing?"

Most current infrastructure is built around operational reliability: uptime, latency, and error rates. But AI agents introduce a different class of failures - an answer that is fluent but wrong, a tool call made with stale context, a policy silently ignored. These failures don't throw exceptions. They ship to users.

In high-stakes domains like banking, underwriting, insurance, and regulated operations, a single behavioral failure can cascade. Enterprises need more than observability. They need to know whether a specific production decision was justified - and be able to prove it.

Existing tools solve pieces of this. Traces capture what happened. Evals score outputs against a benchmark. LLM judges flag anomalies. But none of them answer the question that actually matters in production: why did the agent make this decision, and was it justified given the context it had?

Traces show execution, not reasoning. Evals are static - they tell you how your system performs on a dataset, not whether a specific live decision was sound. LLM judges are powerful, but uncalibrated ones drift, disagree with each other, and often lack alignment with what your business actually defines as "correct." The result is a trust bottleneck. Enterprises want to deploy agents into consequential workflows. The tools to govern that deployment don't exist yet.

A model risk management layer for AI agents

We are building the reliability layer that sits between your agents and production - a platform designed from the ground up for the moment when an agent's decision actually matters.

Calibrated evaluation that you can trust

Most teams that evaluate agents use LLM-as-judge. The problem is that an uncalibrated judge is just another model making decisions you can't verify. We take a different approach.

For each deployment, we ingest a system knowledge report - your agent's tools, prompts, and retrieval configuration - alongside a set of human-annotated examples. We calibrate our LLM judges against those annotations, measuring agreement metrics until the judges behave like reliable human annotators for your specific system. The result is an evaluation layer that is grounded in your domain, not a generic benchmark - and one that you can audit and improve over time.

Continuous behavioral monitoring

Live monitoring detects when agents deviate from expected decision patterns - not just whether they crash, but whether they are reasoning differently than they were last week. Drift in tool selection, changes in retrieval behavior, shifts in output distributions: these are the early signals of a system losing reliability. We surface them before they become user-facing failures.

Improvement feedback loop

When the monitoring layer surfaces a problem, we don't just alert - we diagnose. We identify whether the issue originates in the prompt, the retrieval layer, the tool configuration, or the model itself, and surface next best actions for the team to act on.

Why now

AI agents are moving from demos to decisions. They are handling customer queries, financial workflows, underwriting assessments, and internal operations at a scale no human team could match.

The bottleneck is no longer capability. Agents can perform. The bottleneck is trust. Today, enterprise deployments are being blocked or severely limited not because the agents can't do the work, but because teams cannot answer basic governance questions about what the agents are doing, why they are doing it, and whether they would be able to prove it to a regulator.

We have seen this firsthand. Building AI agents in finance and sales, the question that blocked every production deployment wasn't "can the agent do this?" - it was "how do we know it will keep doing it correctly?" The tools to answer that question don't exist at the depth the problem demands.

The companies that win the next decade will be the ones that deploy AI agents into the workflows that matter. Trust is what makes that deployment possible. We are building that trust layer.