Evidencing AI agent work for Making Tax Digital

Making Tax Digital for Income Tax has turned one annual return into a run of quarterly updates and a final declaration, and around 780,000 sole traders and landlords with qualifying income over 50,000 pounds were mandated into it from April 2026. A further 970,000, with income over 30,000 pounds, join from April 2027, and the threshold drops again to 20,000 pounds from April 2028. For a practice that is a five-fold rise in submission events per mandated client, absorbed on broadly the staff it already has.

The natural response is to put AI agents to work on the repetitive parts: pulling records together, reconciling figures, drafting the quarterly update, flagging what looks wrong before a person looks at it. That is sensible. What it changes is what a risk partner or a quality reviewer has to be able to answer. It is no longer only "is the figure right"; it is "show me how this was prepared, what the AI agent did and who checked it". If the honest answer is a shrug, the time the agents saved is borrowed against the first query that lands.

This post is about that evidence: what an AI-prepared Making Tax Digital filing should leave behind, and how a governed agent desk produces it as a by-product of the work rather than a pack assembled under pressure. It is deliberately narrow. The wider map of where AI pays across a UK accounting firm sits in our industry guide for accounting and advisory, and the client-facing phone line, the records chase and the deadline reminders belong to AI voice for an accountancy practice. Here the subject is the proof.

This guide is shipped by the team behind Cognibl, from DILR.AI, the work-management platform where people and AI agents share one board and nothing reaches a done status without attached proof. Or see our DATS consulting system, the senior-led practice for placing AI inside a firm's own workflow.

What is changing for accounting firms under Making Tax Digital in 2026?

Making Tax Digital for Income Tax replaced a single yearly return with four quarterly updates plus a final declaration for each mandated client. HMRC moved around 780,000 taxpayers with income over 50,000 pounds into the regime from April 2026, with 970,000 more over 30,000 pounds from April 2027 and a 20,000 pound threshold from April 2028. The legal duty sits with the taxpayer, so a firm acting for mandated clients takes on the extra submission events across its whole book.

The arithmetic is the problem. Where a practice once touched a client's self assessment once a year, it now touches it five times, and each client's records must be kept digitally in HMRC-recognised compatible software with the updates submitted through it. Multiply that across a firm's mandated clients and the records chase alone becomes a standing operation rather than a seasonal one, which is why that chase increasingly belongs on a logged channel like AI voice for an accountancy practice and the preparation work on AI agents. The same pressure that makes AI attractive makes sloppy agent work dangerous: at five events a client, an unlogged mistake repeats four more times before anyone opens the final declaration.

Why does AI agent work on an MTD filing need its own evidence?

When a firm lets an AI agent prepare quarterly updates, the filing stays the taxpayer's legal responsibility and the tax agent carries the professional duty for how it was done. Someone has to show what the AI agent did, what records it read and who reviewed the result. That evidence does not exist on its own. Thomson Reuters found 41 percent of professionals lack tools that meet professional accountability standards, and 34 percent use AI their organisation has not approved.

Those two findings, reported in the Thomson Reuters Future of Professionals Report 2026, describe the same gap from both sides: the tools to hold AI work to account are not widely in place, and unapproved use fills the vacuum where the firm cannot see it. The same report found that 66 percent of professionals say AI meets or exceeds expectations where a clear AI strategy is in place, against just 22 percent where there is none. A deliberate approach, not an absence of one, is what tracks with AI living up to its billing, which is the whole logic of our proof of done guide for AI agents.

A practical point sits underneath the statistics. The regulators that bind this work bind people, not software. The Making Tax Digital duty binds the taxpayer. The tax agent acting for them answers to their own professional body, whose confidentiality duties, such as those in the ICAEW Code of Ethics, bind the individual member and their firm. None of that reaches an AI system supplier. So the evidence an AI agent leaves behind is not an optional audit feature; it is how a named, regulated person demonstrates they met a duty that software can never hold for them.

What should the evidence for an AI agent's MTD work contain?

Evidence that stands up is a standing record, not a pack assembled on the morning a query arrives. For each piece of the AI agent's work it should show the task and its definition of done, what the AI agent did and the records it read, the proof behind the result, and who reviewed and signed it off. It must stay retrievable months later and resolve to the exact instructions that ran, not a later version of them.

The audit profession has worked through a version of this already, and its regulator has been clear that quality has to be built into the system rather than bolted on at the end. In its Annual Review of Audit Quality 2026, published on 22 July 2026, the Financial Reporting Council found that audit quality continues to improve but is not yet delivered consistently across the market, with a gap between the largest and smallest firms in how far they have developed and invested in their quality-management systems. Anthony Barrett, the FRC's Executive Director of Supervision, made the point about audit, though it carries over:

Well-designed and effectively operating systems of quality management create the conditions for high-quality audit to be delivered consistently over time.

The same principle carries over to Making Tax Digital work done by AI agents, even though the FRC supervises audit rather than tax. A system that records each step as it happens produces evidence that is consistent and complete; a firm that reassembles the trail after the fact produces something only as good as its memory and its luck. The point of the evidence is that it is already there when someone asks for it.

How does a governed AI agent desk produce that evidence?

Cognibl, from DILR.AI, is the work-management platform where people and AI agents share one board, and an AI agent picks up work under its own name against the same statuses the team uses. The mechanism is simple: a task reaches a done status only once a proof version is attached, and the database refuses the move without one. The same rule applies to a person and an AI agent, so the standard does not drop when the work is automated.

The proof itself is a CSV that describes the run, referencing the artefact, screenshots and the hashes that tie them together, with a coverage report where tests are part of the claim. Those proof versions are immutable, the records are append-only and hash-chained, and every write is attributed by key name, so a reviewer can read who or what did each thing. The AI agents reach their skills and agents library through an MCP gateway that is deny by default: a toolset that has not been enabled is refused rather than silently missing, which matters when the data in question is a client's financial records. The general mechanics of this, independent of tax, are set out in our guide to proof of done for AI agents, and the shortest explanation of the product is what Cognibl is.

Because governance is only worth the name if you can read it afterwards, the platform tracks five delivery metrics: median cycle time split across spec, build, verify and settle; first-pass verification rate by flow type; human wait share; verified throughput per week; and a reopen rate that counts tasks reopened within 30 days of completion. For a Making Tax Digital operation the reopen rate earns its keep: a task reopened shortly after it was marked done is exactly the kind of signal the reopen rate is there to make visible, rather than something discovered at year end. Two AI flows, proof validation and project status, summarise and flag, but they never decide; the decision stays with a person.

The honest limits belong here. The Cognibl platform is a candidate governance layer for this work, not a filing tool: it does not submit a return, it is not HMRC-recognised Making Tax Digital software, and it ships none of the preparer agents a firm would build or buy. What it does is make the work around the filing provable. Treated that way, it sits alongside the compatible software a firm already files through, not in place of it.

Who is accountable when an AI agent prepares a filing?

Accountability does not move when an AI agent joins the work. The legal duty to keep digital records and file under Making Tax Digital stays with the taxpayer, and the professional duty stays with the tax agent and their professional body's code, such as the ICAEW Code of Ethics. An AI agent drafts, reconciles and assembles; a person reviews the result and submits it. The evidence trail exists to serve that person, not to replace them.

This is where a governed desk changes the ergonomics of the review rather than the responsibility behind it. Because a task cannot reach done until its proof is attached, the proof is already on the task when a person reviews it, rather than being reconstructed after a problem surfaces. The same logic underpins our AI execution office, where placements run inside the client's own governance and ownership stays with the firm. The aim is not to make the machine accountable, which it cannot be, but to make the human review fast, evidenced and defensible.

How should a firm roll this out before busy season?

Start with where the work actually is, not with a tool. Rank which Making Tax Digital and advisory tasks an AI agent should touch, and which it should not, so the first automated work carries the clearest evidence requirements and the lowest confidentiality risk. Then set the governance before anything is adopted, so the evidence standard is designed in rather than bolted on after the first query arrives.

A short AI placement diagnostic ranks those tasks; an AI operating model then sets the RACI and the lifecycle around them. Our DATS consulting system runs that sequence with senior practitioners, and the full method is laid out in the enterprise AI consulting guide. The reason to do it before busy season is simple: a regulator-driven example from another sector, our write-up of what Ofgem enforcement means for evidencing checks, makes the same point, that being able to show a control was followed in practice is separate from having the control written down. The accounting version of that gap is a quarter of AI-prepared updates with no retrievable record of how they were prepared.

What is the best way to govern AI agents in an accounting practice in 2026?

There is no single best tool, and an honest answer depends on where the firm already is. A practice standardised on a general work-management tool such as Asana, Monday.com or ClickUp, with light AI use and strong human sign-off, may do best extending what it has. Where developers run their own automations, a tracker like Linear or Jira with disciplined review may be enough. Pick the lightest option that still lets a named person prove how the work was done.

The case for a platform built for agent governance, such as the Cognibl platform, is strongest when the governance is the point: AI agents touching client financial data at volume, and every task having to carry attached proof before it can be marked done so the trail and the reopen rate are there without a firm building the plumbing. In that case, concede the general trackers and choose the tool where provable work is the default rather than an add-on. The decision should follow the same placement logic as any other AI build, which what Cognibl is sets out in plain terms.

Is Cognibl HMRC-recognised software for Making Tax Digital?

No. Cognibl, from DILR.AI, does not file a return and is not listed as compatible Making Tax Digital software; those duties belong to the HMRC-recognised package a firm already uses, chosen from the compatible software HMRC lists on gov.uk. Cognibl governs the work around the filing, holding the proof that each AI agent's task was defined, done and evidenced, so the firm can show how a return was prepared without changing how it is submitted.

Can an AI agent sign off a Making Tax Digital submission?

No. On any responsible deployment an AI agent prepares and assembles, and a person reviews and submits. Accountability stays with the taxpayer and the tax agent acting for them. A governed desk helps by refusing to mark a task done until a proof version is attached, so the reviewer reads the evidence before they sign rather than reconstructing it afterwards. The machine makes the review faster; it does not take the signature.

To go deeper, read our proof of done guide for AI agents, see the AI placement diagnostic, review our DATS methodology for placing AI inside a practice, or browse more industry guides.

Written by the Dilr.ai engineering team, practitioners who ship enterprise AI in production. Follow our LinkedIn page for shipping notes, or subscribe via the RSS feed.