Every treasury team has a version of the same morning. Log into five bank portals, export the balances, paste them into the spreadsheet, chase two subsidiaries, and produce a cash position that describes yesterday. By the time the forecast is assembled, the thing it forecasts has already moved.
This is not a fringe workflow. Strategic Treasurer found 77% of organisations still run cash positioning primarily on Excel, and in AFP's 2025 Treasury Benchmarking Survey, 73% rank cash forecasting as their top priority while more than 60% call it their most challenging task. The most important job in the function runs on the weakest tooling.
AI agents fit treasury unusually well, for a structural reason. Treasury is continuous monitoring of many accounts, entities and currencies, punctuated by decisions that need judgement. Software is good at the first part. People are good at the second. Most treasury stacks force people to do both, and the monitoring eats up the week.
Because the spreadsheet works right up until the moment it matters. It handles the routine week fine. It cannot see across five banks in real time, catch a covenant drifting toward its limit on a Friday, or explain why the forecast missed. The cost hides in three places:
Rising expectations, worsening inputs, flat headcount. That squeeze is why 46% of treasury teams are actively evaluating AI.
Three jobs. An AI agent in treasury continuously reads your bank, ERP and market data, maintains a live picture, and either acts within tight limits or brings a recommendation to a human with the reasoning attached. It is not a chatbot over your TMS, and it does not move money on its own judgement.
A forecasting agent connects to bank APIs and the ERP directly, so the position assembles itself. Then it does what spreadsheets structurally cannot: it learns payment behaviour at invoice level, which customer pays on day 34 regardless of terms, which subsidiary submits late, what seasonality does to collections, and assigns each open receivable a payment probability instead of assuming terms are destiny.
McKinsey research puts the machine learning gain at 30% to 50% better short-term accuracy over manual methods, and a tighter forecast means smaller idle buffers, so cash held as insurance can work instead. The production detail that matters: every forecast line carries its confidence and drivers, because a forecast that cannot explain its own variance will not survive its third bad week.
Liquidity problems are rarely surprises in the data. They are surprises in the noticing. The balance was drifting for nine days; nobody was looking at that account on those days. A liquidity agent watches every account, entity and facility continuously and flags:
Each alert carries the reason and a recommended action, which is what separates a monitoring agent from a fourth dashboard. The value is the watching, done on every account, by something that does not go home at six.
FX is where the money is largest and where autonomy should be smallest. MillTech's Q1 2026 survey found 96% of firms with unhedged exposures took losses, averaging £908,000 per quarter, with 14% losing between £1 million and £4.9 million. The pain underneath those numbers is visibility: you cannot hedge what you have not measured, and exposure data lives scattered across ERP entries, intercompany positions and forecasts in a dozen entities.
That aggregation is what an agent does well. It maintains net exposure by currency and entity as invoices post, flags every pair below your policy hedge ratio, runs scenario maths on live positions, and packages a hedge recommendation with reasoning for a human to approve or reject. The trade stays with the treasurer. A model can be wrong about direction; it should never be wrong about what your exposure is, and that half of FX risk is pure data work.
Treasury is the wrong place to learn AI governance by accident. Three problems account for most failures. The data was not ready and the agent amplified it: EuroFinance found 37% of treasurers name poor underlying data as their top barrier to trusting AI forecasts, and an agent on fragmented inputs produces confident numbers built on them. Plausible-but-wrong outputs burned trust early: one wrong forecast with no explanation trail, and the team quietly returns to Excel. And nobody could answer the auditor: who approved this, on what data, under which policy version?
The engineering answers are unglamorous and they are the actual product. Every number carries its sources and confidence. Every recommendation logs the policy applied and the human who dispositioned it. Autonomy is tiered: aggregate and alert freely, recommend within policy, execute nothing that moves money without a named approver. In treasury, governance is not the compliance tax on the project. It is the project.
And the blunt caution. One entity, two bank accounts, a single currency: you do not need an agent layer, and a disciplined spreadsheet with bank feeds will serve you at a fraction of the cost. Agents earn their infrastructure when the accounts, entities and currencies exceed what a team can watch.
Build versus buy: Kyriba, HighRadius and the TMS vendors sell strong general-purpose modules. Teams with unusual flows, marketplace float, client money segregation, crypto treasury, need agents built against their own ledger and policy logic, which is where an AI agent development partner beats a thirteenth SaaS subscription.
The morning routine is the tell. If your treasury team starts the day logging into portals and pasting balances, the constraint is not analytical talent. It is that the most watching-intensive function in finance runs on tools that cannot watch.
The three workflows share one shape: continuous attention interrupted by judgement calls. Agents take the attention, people keep the calls. Get that split right and the numbers follow, forecast accuracy up 30% to 50%, drift caught days earlier, hedges decided on complete exposure for the first time. Get the governance wrong and none of it survives the first audit.
Codiste builds AI agent systems for fintech treasury operations where every forecast, alert and hedge recommendation carries its data sources, its confidence and an audit trail your examiners can replay.
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