Karsten Wenzlaff, Advisor
August 26th, 2025
Karsten Wenzlaff is a Crowdfunding and Fintech Expert from Germany. He is Secretary-General of Digit[...]
Agentic AI At Home, At Work, Under Scrutiny
Apr 10, 2026 | NCFA Insight | Artificial Intelligence And Data, Regulation And Policy, Risk Compliance And Regtech
Agentic AI Moves Into Daily Life Workflows And Oversight
On March 31, 2026, the UK Digital Regulation Cooperation Forum published a foresight paper on the future of agentic AI. It offers one of the clearest regulatory reads yet on where agentic AI is heading as it moves into consumer tasks, enterprise workflows, and regulatory supervision.
The paper draws a clear line between systems that respond and systems that act. Agentic AI can plan, pull data, use tools, and carry out actions such as making payments on a user’s behalf. Once AI moves into real workflows, the focus shifts fast to consent, accountability, cybersecurity, consumer protection, and competition. That lines up with wider NCFA analysis of AI governance gaps and the cost of weak controls once software starts operating inside regulated systems.
At Home Agentic AI Becomes A Delegation Layer
Everyday life admin is one of the clearest use cases. Instead of bouncing across websites, forms, logins, and support queues, a user can ask an agent to sort bills, renew a policy, or book a trip. That turns software into a delegation layer between the customer and the market.
Three benefits stand out.
Lower search costs. Agents can turn simple requests into structured comparisons across providers and calculate total cost including fees.
Better deal discovery. They can monitor prices, surface better options, and apply savings automatically.
Easier switching and cancellation. They can pre-populate forms, track deadlines, and maintain records of consent.
For users facing access barriers, including disability or language constraints, these systems could also expand participation and accessibility. But that upside depends on trust. As NCFA noted in user backlash over AI data use, convenience can disappear quickly when people no longer understand how their data is being used or who controls it.
At Work The Business Case Starts To Get Real
Companies can use agentic AI across customer facing work and internal operations.
On the customer side, agents can handle support requests, guide onboarding, process refunds, and complete routine tasks before sending more complex cases to staff.
Inside the business, they can pull information from multiple systems, draft reports, create follow ups, route approvals, and take care of repetitive work that usually eats up time.
The report includes real data. A large study of AI in customer support found productivity gains of about 14% to 15% in issues resolved per hour, with the biggest improvements among less experienced staff. A UK Government Digital Service trial across 20,000 staff reported average time savings of 26 minutes per day.
These aren’t fully agentic systems, but they help explain why firms are moving in that direction.
There’s also a concrete example from Allianz. The company built a system using seven AI agents to automate food spoilage insurance claims. The agents pull evidence from multiple sources and work together on a single case.
That matters because it shows where agentic AI is likely to land first in regulated industries. Not in open ended autonomy, but in tightly defined workflows where speed, documentation, and consistency matter. It also supports NCFA’s earlier view on which fintech processes are most ready for agentic AI.
Regulators Won’t Just Regulate Agents They’ll Use Them
The DRCF isn’t only asking how to regulate agents. It’s also looking at how regulators can use them.
AI can help detect issues, review large volumes of documents, support analysis, and assist with drafting. The Competition and Markets Authority already uses AI tools to detect bid rigging linked to more than £300 billion in UK public procurement each year. The CMA has also deployed agentic AI to detect consumer harms like drip pricing at scale.
That matters for regulated finance. Once supervisors and watchdogs use AI to monitor conduct and review journeys, firms will need stronger controls, clearer evidence trails, and more reliable oversight. NCFA’s recent review of what regulated AI needs points in the same direction.
AI doesn’t just change the market. It changes how the market is watched.
The Risks Rise When Systems Start Acting
The same features that make agentic AI useful also create new pressure points.
Accountability gets harder. Multiple agents, tools, and providers can operate inside one workflow. When something breaks, responsibility can be difficult to trace.
Action bundling raises the stakes. A simple request can trigger multiple steps at once, including data access, consent, payment, and sharing. Users may not fully understand what they handed over or when control returns to them. That risk is especially relevant in finance, where NCFA recently looked at how AI payments challenge consent rules and liability.
As agents take over comparison and decision making, users may rely on rankings they can’t see. If those rankings reflect platform incentives or partnerships, outcomes can change without users realizing it.
That also changes competition. Firms may start competing to be selected by the agent, not by the customer.
Cyber Risk Scales Faster Than Most Firms Expect
Agentic AI can improve defense by helping teams triage threats and respond faster. At the same time, it expands the attack surface.
Prompt injection becomes more serious when agents act on untrusted input. Broad permissions increase exposure. If controls are weak, emails, browsing history, and customer records can be exposed.
A recent attack used agentic AI to carry out 80% to 90% of the attack lifecycle.
That lowers the cost of scale for attackers.
The report also points to emerging ideas like Know Your Agent identity and authorization frameworks as something to watch.
Competition Risk Goes Beyond Big Tech
Vendor lock in. As agents become embedded in workflows, firms may depend more heavily on one provider’s infrastructure, data, and orchestration layer. Without strong interoperability, markets could tilt toward a single vendor that captures most outcomes. That concern sits close to NCFA’s argument that smart data infrastructure redefines financial competition.
Algorithmic collusion. Research shows agents can converge on higher prices or coordinated behaviour in controlled settings without being told to do so.
These are experimental findings, not live markets. Still, the warning is clear. Without strong controls, hidden coordination becomes a conduct risk.
What Fintechs Should Take From This Now
The most useful part of this report isn’t prediction. It shows where regulators already expect pressure to build as agentic AI moves into real use. There’s no regulatory gap to wait for. Existing rules already apply. Firms are expected to manage these risks now, not later.
The upside is real. Agentic AI can reduce friction, compress workflows, and lower operating costs across the stack.
But the bigger change is structural. Once systems act, not just suggest, control becomes part of the product. Who authorizes actions, what gets logged, how decisions can be explained, and how users step in or push back all become core features.
That creates a new dividing line. Some firms will bolt agentic AI onto existing systems and hope their controls hold. Others will rebuild around visibility, control, and user trust. The second group will move faster, scale more cleanly, and face fewer problems as scrutiny rises. NCFA’s earlier piece on agentic AI in banking makes the same point from the deployment side.
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