The numbers from Salesforce paint a picture of urgency mixed with recklessness. Ninety per cent of UK executives are already using or exploring multi-agent systems, yet 81% explicitly say their AI projects will fail without process visibility. That is not a gap. That is a chasm. For mid-market legal, insurance, financial services and accountancy firms operating under FCA Consumer Duty PS22/9, SRA Code, PRA SS1/23, and ICO UK GDPR, the stakes are higher than for most. A multi-agent system that cannot be audited, cannot be explained, and cannot be reversed is not an asset. It is a regulatory breach waiting to happen. The fact that three-quarters of firms also report their existing processes are holding them back suggests they know what they need to do — they just do not know how to do it without throwing out the infrastructure that keeps them compliant.

This story is a symptom of a larger pattern we see across the sector: the industry has become obsessed with the headline capability of AI — autonomous agents, autonomous reasoning, autonomous decision-making — without first securing the boring, critical foundation that makes autonomous systems safe. The generative AI boom of 2023–24 created an expectation that bigger models and more autonomous behaviour automatically equals better outcomes. It does not. Tools like Harvey, Legora, and Luminance built impressive use cases around document intelligence and contract analysis, but they succeeded because they were solving specific, bounded problems with clear human oversight. Multi-agent systems are orders of magnitude more complex. They make decisions across workflows, invoke third-party tools, and can operate with less human intervention by design. That autonomy is the entire point — and it is also the entire problem if you cannot see what they are doing or why.

Here is Trovix's honest take: you cannot bolt governance onto an agentic AI system after it is live. You have to build it into the architecture from the start. This means designing for observability and auditability before you deploy, not after. It means ensuring that every decision, every data access, every workflow transition is logged, traceable, and explainable. It means integrating your AI implementation with your existing process controls, not replacing them. Most vendors will tell you they have a solution for this. Few actually do. They give you dashboards and compliance modules that feel adequate until you have to explain to the FCA or ICO why an autonomous agent made a decision you cannot defend. The difference between a responsible agentic AI deployment and a reckless one is not the intelligence of the model. It is the visibility of the system. Trovix Sift and Trovix Aria were designed around this principle: every interaction with the system must be logged, retrievable, and defensible. That is not a feature. That is a requirement.

If you are in a mid-market law firm, insurance broker, financial services outfit, or accountancy practice and you are considering agentic AI, here is what you should do right now. Do not ask your vendor whether they have governance features. Ask them whether their system is built to be governed. There is a difference. Insist on process visibility as a precondition for any pilot. Map your existing audit, compliance, and quality assurance workflows and make sure any AI implementation integrates with them, not around them. Start with bounded use cases where the stakes are medium and the workflow is clear—matter intake, regulatory change monitoring, initial document review. Do not deploy a multi-agent system across your core operations without understanding what it is doing at every step. And be explicit with your team about what you are trading off: autonomy for explainability, speed for auditability. Most firms think they want both. You cannot have both. Choose deliberately.

Source: Computer Weekly