AI Agents

Context

Built for banking within the Garnishment Processing stack and owned by Ops / Legal, this agent focuses on the non-standard cases: files with missing data, duplicates, or non-supported jurisdictions that don’t fit the straight-through garnishment flow. It is designed for Tier 1–2 banks where volumes are high and the cost of letting bad or incomplete cases bounce around the organisation is significant.

What it does

The agent monitors garnishment cases as they move through the standard pipeline and actively looks for conditions that mean “this should not continue as usual”: missing or inconsistent party details, conflicting account matches, duplicate or overlapping orders, jurisdiction codes the bank doesn’t support, or gaps in the documentation required by policy. When it detects such an exception, it stops the case from progressing blindly, tags it with a clear reason code, assembles the relevant context (order data, customer details, prior actions taken), and routes it to the right queue or specialist team according to your operating model. The case record is updated with what was flagged and why, so whoever picks it up can act immediately instead of spending time rediscovering the problem.

Core AI functions

At its core, this agent performs exception handling: it evaluates garnishment cases against a set of structural, policy, and jurisdiction checks, recognises patterns of missing data, duplicates, and non-supported jurisdictions, and classifies them into actionable exception types. It doesn’t replace legal judgement; it makes sure that only the outliers that truly require expert review reach Legal and senior Ops, and that they arrive with a concise explanation and the right evidence attached.

Problem solved

Without this agent, exceptions are usually discovered late and inconsistently—one team spots missing data and sends the case back, another pushes it through, a third escalates to Legal without the full picture. This creates rework cycles: cases ping-pong between Ops, branches, and Legal; the same gaps are explained multiple times; and customers or authorities experience delays while internal teams try to understand what went wrong. By systematically flagging exceptions early and making them explicit, the agent cuts down on that loop of rediscovery and back-and-forth.

Business impact

The main impact is a reduction in escalations and unnecessary noise. Legal and senior Ops see fewer, better-prepared escalations because the obvious structural issues are handled within the normal flow, and the genuine edge cases arrive with a clear “reason for exception” and supporting data. That shortens resolution time, frees specialist capacity, and reduces frustration for front-line staff who no longer have to guess whether a case should be sent back, pushed through, or escalated. Over time, this improves overall garnishment handling quality because systematic exception data can be used to tighten policies and upstream checks.

Integration and adjacent use cases

Integration complexity is low: the agent needs access to the garnishment case data, key reference tables for jurisdictions and supported products, and the work queues or routing rules used by Ops and Legal.

Common combinations in this stack:

• Garnishment Order Extractor to parse incoming court orders into structured data the exception logic can reliably test;

• Customer & Account Matcher to ensure the right debtor and accounts are linked before deciding whether a match is ambiguous or duplicated;

• Funds Availability & Freezing Agent to compute and apply enforceable balances on accounts when cases pass basic checks;

• Multi-Order Prioritization Agent to sequence valid orders by priority and timing once they are confirmed as in-scope;

• Notification & Communication Generator to turn decisions and status into consistent letters for customers, employers, and authorities; and

• Garnishment Exception Handler to intercept any case that fails these steps, label it correctly, and route it to the right experts with a minimum of rework.