Google Cloud's multi-agent announcement at its London Summit marks a real inflection point: enterprise-grade autonomous systems are moving from proof-of-concept to production. For UK legal, financial services, insurance and accountancy firms, this is not academic. The FCA's Consumer Duty (PS22/9), the SRA's Technology Rules, and PRA SS1/23 all now assume firms will deploy AI at scale. But here is what Google's announcement does not address: regulated firms cannot simply hand work to autonomous agents and hope for the best. Multi-agent systems that 'reason and execute' sound elegant. They are also black boxes to auditors, compliance teams, and regulators unless you build governance into them from the start.

The industry pattern is clear and accelerating. Harvey, Luminance, and Legora have all demonstrated that AI can handle complex legal and professional workflows. But each of these vendors solved the wrong problem first: they optimised for accuracy and speed, then bolted on governance. Microsoft Copilot for Enterprise and similar approaches still struggle with the same issue—audit trails are afterthoughts, not architecture. What we are seeing now is a generation of firms discovering that autonomous multi-agent systems create a new problem: you cannot explain what the system did, why it did it, or whether it complied with your obligations under GDPR, FCA rules, or ISO 42001. The EU AI Act and Lloyd's Blueprint Two both demand explainability and human oversight at points of material risk. Multi-agent systems designed for speed first will fail those demands.

Trovix's view is direct: agentic AI for regulated firms must start with governance as the load-bearing wall, not decoration. This means building compliance checkpoints into agent workflows before deployment, not inspecting audit logs after decisions are made. The practical difference is significant. When you deploy a system like Trovix Audit (/solutions/trovix-audit), you are designing agents that document their reasoning, log their decisions, and flag compliance risks in real time. This is harder than what Google or other cloud platforms are promoting. It is also the only approach that actually works in regulated industries. A law firm using multi-agent workflow automation cannot tell the SRA 'the system decided autonomously'—the firm is accountable. That accountability must be embedded in how the system reasons, not recovered from logs afterwards.

If you manage a mid-market practice right now, do three things immediately. First, audit your current AI deployments for explainability gaps—if you cannot produce a documented trail of how an AI system reached a conclusion, you have a governance hole. Second, do not treat multi-agent systems as labour-saving devices alone. Treat them as tools that must comply with your regulatory obligations before they touch client work. Third, when you evaluate platforms that support agentic workflows—whether Google Cloud, Microsoft, or specialist vendors—ask for governance architecture first, performance metrics second. The firms that will succeed with multi-agent AI are those that slow down enough to do compliance properly. Speed without accountability is not efficiency; it is risk transferred to your regulators' next enforcement action.

Source: Computer Weekly