SolutionsExecutionClaims & Service Operations

Service Exceptions

Service fell out of SLA — what's the right make-good?

The decision

A service commitment was missed. The customer is upset, several remedies are possible — a credit, an expedite, an escalation, a personal follow-up — each with a different cost and effect. What make-good fits this customer and this miss?

Why it's hard today

Over-compensate and you train customers to complain; under-compensate and you lose them. The right remedy depends on the customer's value, the severity, and what's worked before — across CRM, contract, and history.

Why your systems don't help

Your service system logs the breach; it doesn't choose a remedy proportional to the customer and the miss. It opens a ticket, not a resolution.

What AgentForgeOS does

It assembles the picture you can't see, and makes the call under your rules.

Offer a proportional service credit with an expedited fix and a personal follow-up — matched to the customer's value and the repeat miss — under your remedy policy.

Operational context assembled
  • SLA breach severity
  • Customer value and history
  • Available remedies and cost
  • Past remedy effectiveness
  • Contractual obligations
Governed by your policy
  • Remedy-budget limits
  • Proportionality rules
  • Contractual-remedy compliance
  • Repeat-miss escalation
decision-workspace · service · case #61190

⚠ SLA missed — case #61190, high-value customer

Evidence assembled

  • Second miss this quarter
  • Customer lifetime value high
  • Credit + expedite worked before
  • Contract has a remedy clause

Recommendation · service credit + expedited resolution, owner follow-up

Policy: within remedy budget · proportional to value · awaiting service lead

Decision Object #61190Evidence ×4Policy ✓ApproveAdjust

What improves

  • Retained high-value customers
  • Remedies proportional, not reflexive
  • Fewer repeat escalations
The knowledge it keeps

Each remedy and whether it retained the customer is kept, so the model learns which make-goods actually work, by customer value and miss type.

Under the hood

Underneath, this is the same operating model the rest of the platform runs: verified context is assembled, options are weighed and adversarially challenged, the decision is governed by your policy, and the outcome is learned. The decision changes from one of these to the next. The architecture does not.

See how it works

This is exactly how your team works today.

Only now the decision is assembled, governed, and remembered — instead of made from memory and lost.