Use case · Finance close
Workflows pull transactions into tables, rules flag what does not match, a Relay drafts the variance commentary, and the close dashboard shows what is left.
An illustrative finance close use case for Agent Serve, the AI workspace where teams build, deploy, and manage AI agents: Tables, Rules, Workflows, a Relay and Dashboards combine to reconcile transactions and explain variances at month end.
At a glance
The problem it removes
At month end the team matches bank lines to the ledger in spreadsheets, chases owners for explanations and writes variance notes late at night. Exceptions are found in the final days, when there is least time to fix them.
Step by step
A workflow column suggests the matching ledger entry for each bank line.
Rules flag amounts that differ, missing references and late postings, with the reason.
A Relay drafts variance notes; each owner approves before they are filed.
How it fits together
A scheduled workflow loads bank and ledger lines into a reconciliation table.
A workflow column suggests the matching entry and a reason on every row.
Rules flag amounts that differ, missing references and late postings.
A Relay drafts the variance commentary for each account owner to approve.
The close dashboard shows open items by entity and owner.
How it runs
Agent Serve runs the matching overnight, so the team starts the day on exceptions instead of on raw lines.
A workflow on a Daily schedule reads bank and ledger exports and writes them into one reconciliation table.
A workflow column runs on each unmatched row and fills in the likely ledger entry and why.
A Classification rule labels each open line as amount differs, missing reference or late posting.
A Relay set to Assist drafts variance notes and asks the account owner before saving them.
What to measure
Example figures for a finance team closing three entities.
8→5
Days to close
Business days from period end to sign-off.
92%
Lines matched automatically
Before anyone opens the table.
−60%
Time on commentary
Owners edit a draft instead of writing one.
1
Place to see status
Open items by entity and owner.
Illustrative example. Figures show how the workflow is measured; they are not customer results.
In depth
Agent Serve keeps the matching logic, the approvals and the status view in one workspace the controller can inspect.
The Agent Serve workflow builder shows each step of the load and match, from the Schedule trigger to the Table block.
An Agent Serve Relay set to Assist reads and drafts freely, but needs approval before it saves commentary to the Library.
Each account owner reviews the variance note the Agent Serve Relay wants to save, then approves or declines it.
Once the books close, Agent Serve dashboards show margin and EBITDA from governed metrics, with row-level security by entity.
Examples
Three month-end jobs financial services finance teams run on Agent Serve, each one a spreadsheet nobody misses.
An Agent Serve workflow column proposes where each suspense item belongs, and a rule flags items older than the policy allows.
An Agent Serve Relay compares booked accruals with the claims and premium tables and drafts the gap commentary for review.
An Agent Serve workflow matches scheme settlement files to the ledger every day, and a monitor alerts when unmatched items pile up.
Examples show what teams can build in Agent Serve. They describe patterns, not named customer deployments.
Before and after
| Work | Before | With Agent Serve |
|---|---|---|
| Matching | BeforeVLOOKUPs across exports, rebuilt every month. | With Agent ServeA workflow column suggests the match on every row of one table. |
| Exceptions | BeforeHighlighted cells and comments only the preparer understands. | With Agent ServeA rule labels each exception with a reason anyone can read. |
| Variance commentary | BeforeWritten from scratch by each owner on the last day. | With Agent ServeDrafted by a Relay and approved by the owner. |
| Close status | BeforeA tracker spreadsheet updated by email. | With Agent ServeA dashboard of open items by entity and owner. |
Governance
Agent Serve records how each number was matched, who approved each note and who can see each entity.
Security and governanceAn Agent Serve rule defines an exception the same way in the table, the dashboard and the monitor.
Agent Serve row limits show each entity’s finance team only their own reconciliation rows.
Every workflow run records each block’s inputs, outputs and errors, so a reviewer can trace how a result was reached.
Proposals, approvals, declines and executed actions are written to the workspace audit log, which admins can export.
Relays never send email or book meetings without an approval, under any autonomy setting. An approval covers the exact payload the reviewer saw and expires if nobody decides.
Row-level policies narrow what each person sees in tables, dashboards, exports and scheduled reports.
Workflows run from a deployed version, so a change is reviewed before it reaches production runs.
Further reading
Disputes that fail reconciliation tend to sit in a side queue until the chargeback window has closed. Here is an end-to-end setup that finds them early, ranks them by deadline and drafts the pack, with an analyst signing every filing.
Cards and payments operations · 12 min readPeriodic reviews pile up because every file starts from scratch, even when nothing has changed. Here is a setup that refreshes the data, drafts the file and routes it by risk, so analysts spend their time on the reviews that need judgement.
Retail and business banking · 12 min readSampled call reviews find a missing risk warning weeks after the client acted on the advice. Here is a setup that checks every recorded advice call within a day and puts the quote in front of a compliance reviewer.
Wealth and advice · 12 min readQuestions
Agent Serve Tables, Rules, Workflows, a Relay and Dashboards. Bank and ledger data reach the table through a workflow, an import or the Tables API.
Not in this use case. Agent Serve suggests matches and drafts commentary; people decide. A Relay set to Assist needs approval before it saves anything to the Library.
Yes. Every Agent Serve workflow run keeps a trace with each block’s inputs and outputs, so a reviewer can follow how a row was matched.
No. The figures are illustrative examples of what a finance team might measure with Agent Serve, not customer results.
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