AI Agents

Context

Built for banking within the Garnishment Processing stack and used mainly by Ops and Legal teams, this agent steps in once a garnishment order has been validated and the right customer has been identified. Its mandate is simple but high-stakes: calculate the enforceable balance on the customer’s accounts and apply freezes in a way that matches policy and law, not individual interpretation.

What it does

The agent takes the structured garnishment order and the customer’s ledger data from the core (balances, movements, existing holds) and calculates how much is actually enforceable on each relevant account. It applies the bank’s garnishment policies and jurisdictional rules to distinguish what can and cannot be frozen, then issues precise freeze instructions back into the core system. Alongside the decision, it writes a clear breakdown into the garnishment case—what portion of the balance was considered, what was excluded by rule, and what amount was ultimately frozen—so Ops and Legal can see and reuse the same logic.

Core AI functions

At its core, this agent performs policy and ledger checks: it reads the order and the applicable garnishment rules, reads the live ledger position for the customer, and reconciles the two into an enforceable balance. It focuses on correctly applying the bank’s own policies and documented legal guidance to real account data, and on making those checks repeatable instead of relying on manual, case-by-case judgement.

Problem solved

Without this agent, funds availability and freezes are often calculated by hand, using spreadsheets and local know-how. That’s where wrong allocations creep in: under-freezing, which exposes the bank to creditor and regulatory risk, or over-freezing, which harms customers and triggers disputes. Each correction means more work for Ops, Legal, and customer-facing teams. The agent removes that variability by turning policy plus ledger into a consistent, system-driven decision.

Business impact

The immediate gain is compliance and accuracy: freezes line up with internal policy and documented legal expectations, and the logic is applied the same way across teams, branches, and regions. That reduces disputes, rework, and audit findings, while cutting handling time per case because staff can rely on a system decision instead of recalculating every time. It is most valuable for Tier 1–2 banks, where volumes and scrutiny make manual errors particularly expensive.

Integration and stack context

Integration complexity is medium: the agent needs read/write access to core ledger data and to the garnishment or collections case record, plus access to the bank’s garnishment policy and rule repository.

Common combinations in this stack:

• Garnishment Order Extractor to ingest and structure inbound court orders into machine-readable form before availability checks,

• Customer & Account Matcher to resolve the correct debtor and in-scope accounts across cores and channels,

• Multi-Order Prioritization Agent to decide how to apply freezes when several garnishment orders compete for the same funds,

• Notification & Communication Generator to turn the resulting decisions and balances into creditor, employer, and customer communications using approved templates, and

• Garnishment Exception Handler to route disputed, incomplete, or complex multi-jurisdiction cases to specialists with all evidence and reasoning attached.