The gap between these two words is a regulatory liability.
If you are a Head of Compliance, Risk, CCO or CRO at a PRA-regulated firm with AI agents in production, you already carry personal accountability for their decisions under SM&CR. Right now, you cannot independently evidence why those decisions were made. whattowhy is the only platform that produces that evidence continuously, traced to source data, structured for regulators, from a party with no stake in the outcome.
of the world's largest banks are already piloting AI agents in live production workflows.
IIF-EY, 2025enterprises has a mature governance model for the autonomous AI agents they are deploying.
Deloitte, 2025vendors today offer truly independent evidence of why an AI agent made a consequential decision.
Market analysis, 2026A credit agent declines an application. Without whattowhy, a log entry. With whattowhy, a complete evidential record — in under a second.
Tools give you telemetry. Advisors give you frameworks. Neither produces the independent, regulator-legible evidence that proves the right decision was made, under the right policy, at the right moment.
Five steps. Each valuable alone. Each making the previous step more powerful over time.
Integrated at the layer between your agents and the systems they access, not inside the agent itself. No vendor partnership. No internal access. Structurally independent from the system being assessed. The same vendor cannot provide the agent and the assurance of it, ever.
What information the agent had. What policy it applied. How it interpreted that policy in context. Why it acted as it did. Reasoning traced all the way back to source data. Captured live, not reconstructed after the fact.
Each decision is scored by risk materiality, exactly as SS1/23 requires. High-risk decisions get full trace and human escalation. The output is tamper-evident, queryable, and structured for FCA, PRA and Consumer Duty — legible to a compliance officer, not just an engineer. Every decision added to the corpus makes the next phase more powerful.
As the corpus grows across your agent fleet, whattowhy identifies which agents are drifting toward bad outcomes before they appear in a log. Which decision patterns are accumulating tail risk across hundreds of low-materiality calls that each look fine individually. Which use cases pose a greater risk or have a lower level of control. Not reactive. Predictive.
The corpus identifies where policy is ambiguous, where agents consistently misinterpret intent, and where outcomes are diverging from what the policy was designed to produce. These surface as structured policy recommendations — turning operational evidence into continuous governance improvement, not a static rulebook reviewed once a year.
Firms must assess, test, understand and evidence the outcomes their AI systems deliver to customers."FCA Consumer Duty, active enforcement, 2024-2026
Every phase is valuable on its own. Together they compound into something no point-in-time audit or telemetry dashboard can replicate.
Every other tool captures telemetry — what your agent did. whattowhy captures the full decision chain: what information the agent had, what policy it applied, how it interpreted that policy in context, and why it acted as it did, traced all the way back to source data. Captured live at the moment of decision. We also run challenger models against each decision, scoring the alternative paths the agent could have taken — giving you a measurable view of not just whether agents followed policy, but whether they made the best available decision. This is the only form of evidence a regulator will accept. And it is the corpus that powers everything that comes next.
As the decision corpus grows, whattowhy identifies which agents are drifting toward bad outcomes before they appear in a log. Which decision patterns are accumulating tail risk across hundreds of calls that each look fine individually. Which use cases have a lower level of control than your risk appetite allows. Not reactive monitoring. Predictive risk intelligence, answering the question regulators are starting to ask: do you know where your risk is concentrating before something goes wrong?
whattowhy sits outside the agent stack, with no vendor partnerships and no internal access. The same entity cannot produce the AI agent and the independent assurance of it. Architecturally identical to external audit. Any firm that also provides the agents, the infrastructure, or the advisory wrapper has a conflict of interest that no policy can resolve.
Over time the decision corpus identifies where policy is ambiguous, where agents consistently misinterpret intent, and where outcomes are diverging from what the policy was designed to produce. These surface as structured recommendations, turning operational evidence into continuous governance improvement rather than a static rulebook reviewed once a year.
If you lead compliance, risk or AI governance at a PRA-regulated firm and have agents in production, we want to speak with you. No pitch. We will show you a single decision, fully traced, and ask whether you can get that from anything you have today.