2 Salary Administration AI Agents, Live in Production.

2 live agents automate salary administration within human resources. 1 runs on a schedule and 1 runs on demand. Each one publishes the inputs it needs, the steps it works through and what it hands back.

  • Live

    Payroll Input Validation

    Check what HR is sending to payroll before the cut-off — starters, leavers and changes — because an error caught here is a correction and an error caught after is somebody's rent.

  • Live

    Salary Data Validation

    Check salary records before a pay review lands — that every rate sits in its band, that changes carry authorisation, and that the gaps between comparable people can be explained by something other than who they are.

Catching the payroll error while it is still a correction

Salary administration has an asymmetric error cost that most process design ignores. An error found before the payroll cut-off is a data correction taking two minutes. The same error found afterwards is someone’s rent arriving short, a difficult conversation, an off-cycle payment and a loss of trust that outlasts the fix. Yet the checking is usually done after submission, because before the cut-off is exactly when everyone is busiest. Pay review introduces a second, quieter problem: rates drift out of band through successive individual decisions, changes get applied without recorded authorisation, and differences accumulate between people doing identical work. Nobody intends any of it, and nobody audits for it, so it surfaces years later as a pay equity finding.

These two agents put the checks in front of the deadline. Payroll Input Validation checks what HR is sending to payroll before the cut-off — starters, leavers and changes — for exactly the reason above: the same error costs almost nothing here and a great deal afterwards. Salary Data Validation checks salary records before a pay review lands: that every rate sits in its band, that changes carry authorisation, and that the gaps between comparable people can be explained by something other than who they are. That last test is framed as a question rather than a verdict, because a gap can have a legitimate explanation — different tenure, a retention decision, a market premium — and the point is to make sure someone has actually stated it rather than to score the organisation.

What this moves

Payroll errors that reach an employee
Starters, leavers and changes are checked before the cut-off, because an error caught here is a correction and an error caught after is somebody's rent.
Pay changes without recorded authorisation
Every rate is tested against its band and every change against its authorisation before a review lands.
Unexplainable gaps between comparable people
Differences between people doing the same work are surfaced so they can be explained by something other than who they are.

Human Resources

How AI agents handle salary administration

Drawn from the 2 agents above — what they require, how they run, and what comes back.

What they need

  • This month's payroll changes
  • Cut-off settings
  • Salary records
  • Review settings

What comes back

  • Ready for cut-off Score
  • Before cut-off Written summary
  • Someone will be paid wrongly Flagged exceptions
  • Change by change Table of results
  • Checks applied Verification checklist
  • Records fit to process Score
  • Review note Written summary
  • Do not process Flagged exceptions

How they run

Runs on a schedule
1
Run on demand
1
Steps per run
3
Credits per run
8

Where salary administration fits in human resources

Leave, expenses, notice periods and benefits questions arrive daily and the answers are already in the handbook. Employees ask a person because searching a PDF is worse than asking — an agent fixes that side of it.

All 20 human resources agents

Next Step

Deploying salary administration agents

These run as-is against the inputs listed above. Most deployments adapt one — a different source system, a different tolerance, a different approval path. The first call establishes which.

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