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Agent, workflow and domain-reasoning data

Exception handling records: the long tail agents fail on

Quick answer

Exception handling data for AI agents is the record of cases where standard processing stopped, such as a quantity mismatch, a payment return, a missing document or a stock count variance, together with what a person checked, in which systems, and how the case was resolved. Agents trained only on clean straight-through work have not seen these cases, and that is where automation tends to break. A usable dataset pairs each exception with its trigger rule, investigation steps, resolution code and outcome, plus matched clean cases for contrast.

By SourceX Editorial · Updated

Business exceptions, not runtime errors

This page covers business exceptions: cases that a control, match or rule stopped while every system worked, and that a person then investigated and resolved. Agent runtime errors are a separate problem. SHIELDA, a framework for exception handling in LLM agent workflows, catalogs failures such as hallucinated plan steps and tool timeouts that can propagate across workflow phases [1]; tool-call errors and recoveries covers that data.

Business exceptions call for judgment about what the business would accept. Researchers testing language models on policy decisions report that models apply policies rigidly even when that is impractical, and that supervised fine-tuning on human decisions with explanations beat ethical-framework and chain-of-thought prompting [2]. Resolved exception queues are where operating businesses keep those decisions. The agent and workflow data hub maps neighboring record types, including approval and rejection records.

Where exception queues live, and the reason codes they carry

Many back-office systems already route exceptions to a work queue and tag each case with a reason code, so a dataset should start from those queues and their code tables rather than from free-text tickets.

DomainQueue or recordTrigger and reason codesInvestigation evidenceResolution to capture
Accounts payableBlocked-invoice list, AP automation queuePrice or quantity variance; invoice posted before goods receipt and blocked until goods arrive [3]PO history, receipts, vendor emailsRelease, PO change, credit memo
FreightTMS exception board; EDI 214 shipment status messages [4]Delay, refusal, over, short and damaged (OS&D)Delivery receipt, photos, carrier tracingReschedule, reship, freight claim
InventoryWMS cycle-count variances, adjustment approvalsCounted quantity differs from system quantityRecounts, receiving and pick historyAdjustment with root-cause code
PaymentsACH return queueR01 insufficient funds, R02 account closed, R03 no account or unable to locate [5]Customer contact, bank-detail checkRe-present, new method, collections
Healthcare billingDenial work queue built from 835 remittancesClaim Adjustment Reason Code (CARC), with Remittance Advice Remark Codes (RARCs) for detail [6]Eligibility check, chart, payer portalCorrected claim, appeal, write-off
Drug manufacturingDeviation and CAPA (corrective and preventive action) recordsDeparture from written procedures, which 21 CFR 211.100 requires to be recorded and justified [7]Batch record, investigation reportDisposition, corrective action

Standard code sets such as CARCs and ACH return codes give a taxonomy that stays comparable across suppliers. The EDI 214 and ACH descriptions above come from vendor guides [4][5], so confirm codes against the trading partner's X12 implementation guide and the Nacha Operating Rules. Internal codes need the code table with effective dates, and the share of cases filed under a catch-all such as "Other" is a fast quality check. For single-domain depth, see P2P and O2C records, WMS task and exception logs, and SourceX's pages on invoice exception records, logistics exception records and inventory discrepancy resolution.

The fields that make an exception record trainable

A training-grade exception record links five layers: the trigger with the rule and threshold in force, the state of each system at that moment, the ordered investigation steps, the resolution with its reason code and approver role, and an outcome showing whether the fix held.

The investigation layer is often the gap. Many queues store only a close code, while the checks happened in a WMS screen, a carrier portal or an email thread; SourceX's logistics exception resolution page describes where those steps leave traces. Ask which steps are logged, which can be rebuilt from cross-system workflow records, and which are lost.

Illustrative example: invented to show structure; it does not describe an available dataset.

{
  "exception_id": "exc_2026_018734",
  "queue": "ap_invoice_blocks",
  "trigger": {"rule": "qty_invoiced > qty_received", "rule_version": "ap_tolerance_v7",
              "detected_at": "2026-05-04T08:12:00Z", "reason_code": "QTY_VAR"},
  "linked_objects": {"po_item": "po_88213-10", "asn": "asn_55102", "goods_receipt": "gr_70931",
                     "invoice": "inv_v4471_99120", "shipment": "shp_ltl_30418"},
  "state_at_trigger": {"po_qty": 40, "asn_qty": 40, "received_qty": 36, "invoiced_qty": 40},
  "investigation": [
    {"t": "2026-05-04T10:05Z", "actor": "role:ap_specialist", "system": "WMS",
     "action": "view_receiving_scans", "finding": "36 cartons scanned at dock 3"},
    {"t": "2026-05-04T10:20Z", "actor": "role:ap_specialist", "system": "TMS",
     "action": "open_delivery_receipt", "finding": "POD notes '4 short', signed by driver"},
    {"t": "2026-05-05T14:40Z", "actor": "role:ap_specialist", "system": "email",
     "action": "query_vendor", "finding": "[VENDOR_CONTACT] confirms 36 shipped, 4 backordered"}
  ],
  "resolution": {"t": "2026-05-06T09:00Z", "action": "request_credit_memo",
                 "reason_code": "SHORT_SHIP_ORIGIN", "approver_role": "ap_supervisor",
                 "rationale": "Shortage occurred at origin; no carrier claim"},
  "outcome": {"credit_memo_posted": "2026-05-12", "paid_qty": 36, "reopened_within_90d": false},
  "matched_control": "case_stp_2026_041177"
}

The rule version preserves the tolerance in force, the steps span three systems, and the matched control points to a straight-through case for the same vendor and month.

Sampling the tail without losing the baseline

Ask for the exception taxonomy and a frequency table before any records are sampled, then over-sample rare types for training and pair every exception with matched straight-through cases.

The frequency table should give counts per reason code and month, median time to resolve, reopen rate and the catch-all share. Natural mixes are often dominated by a few codes, so a random sample may contain few cases such as a payment returned for a closed account or a deviation in a cleaning step. Set a minimum count per type and accept a mix that differs from production.

Over-sampling is a deliberate trade. Work on data representativity separates coverage of the input space, which supports robustness to distribution shift, from mirroring the population, where good average error mostly reflects the majority [8]. Report evaluation per exception type and as a frequency-weighted total so neither view hides the other; long-tail and edge-case coverage covers slice metrics.

Straight-through controls fix a selection problem: a queue holds only what the rules flagged. Without clean cases an agent can learn that every invoice is suspect, and it never sees exceptions the rules missed. The openIMIS claims specification sends a share of claims its AI tier judges unsuspicious to manual review to estimate validity [9]; ask suppliers for a random, reviewed sample of clean cases matched on vendor, lane or payer and period.

Fine-tuning targets and replay evaluation

For fine-tuning, the target is the next investigation step or the resolution with its rationale; for evaluation, freeze each held-out case at the moment it entered the queue and score the agent against what the business did and what happened next.

  • Replay. Seed a sandbox with the system state at the trigger (seed data for agent sandboxes). τ-bench compares the final database state with an annotated goal state and reports pass^k, the chance of succeeding on all k repeated trials [10]; inconsistent handling of the same exception is itself a failure.
  • Splits. Hold out by time and by counterparty, because exceptions recur per vendor, carrier or payer. Process-mining researchers describe incompletely separated training and test sets as a leakage problem particular to predictive process monitoring [11].
  • Label hygiene. Flag cases later reversed or reopened, bulk month-end releases that reflect no per-case judgment, and blocks that cleared automatically when a missing receipt arrived. Verifying outcome labels and rework and reversal records cover the methods.

Personal data and third-party content in investigation notes

Exception records carry more personal and third-party detail than clean transactions, because investigations happen in free-text notes, emails, call logs and partner portals.

  • People. Notes name customers, drivers, patients and vendor contacts. Replace them with typed placeholders and keep role-tagged pseudonyms consistent across systems.
  • Bank details. Payment and vendor-master exceptions expose account and routing numbers. Exclude them but keep a flag recording that they changed.
  • Health data. Denial queues hold protected health information. HHS recognizes two HIPAA de-identification methods, Safe Harbor and Expert Determination [12], and SourceX requires one of them before health records are considered for a license.
  • Partner documents. Carrier, payer and vendor documents may carry their own confidentiality terms, so confirm the supplier may share them.

For datasets SourceX sources, rights review checks that the business owns or may share the records and that required consents are in place. Personal details are removed or replaced before delivery, the method is recorded and a sample is checked; no de-identification method is perfect.

Request template for exception handling records

State the queues, taxonomy, coverage floors, controls, evidence and uses, so a supplier can check its systems before anyone samples data.

FieldWhat to state
Queues and periodDomains, queues, source systems and date range
TaxonomyReason-code tables with effective dates; catch-all share
Frequency tableCounts per code and month, time to resolve, reopen rate, delivered before sampling
CoverageMinimum cases per exception type; rare types to over-sample
ControlsRatio of matched straight-through cases and the matching keys
Investigation evidenceLogged steps, notes, emails, call logs and documents, by system
ConfigurationTolerances, thresholds and approval limits in force, with change dates
Outcome windowHow long after resolution to observe reopens, reversals or payment
PrivacyPseudonym keys, excluded fields, placeholder scheme
FormatXES [13] or OCEL 2.0 [14] event logs, or Parquet tables with a data dictionary; see process mining event logs
UsesFine-tuning, evaluation, derived benchmarks; see license terms for agent data

SourceX sources operational datasets from US companies, including finance workflows and support histories, on request rather than from stock, so a request does not guarantee a matching dataset. To have SourceX look for businesses that hold the queues you need, describe your exception data requirements.

Need real exception queues for agent training?

Describe the exception types, systems, investigation evidence and coverage you need. SourceX looks for US businesses that hold matching records, checks the data and each supplier's licensing permissions, manages the license, and coordinates delivery and payment; nothing is contracted until a supplier agrees. Specify your exception handling dataset.

Sources

  1. arXiv preprint 2508.07935, "SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows" (2025). https://arxiv.org/pdf/2508.07935
  2. arXiv preprint 2503.02976 (v2), "Teaching AI to Handle Exceptions: Supervised Fine-tuning with Human-aligned Judgment" (2025). https://arxiv.org/html/2503.02976v2
  3. International Conference on Process Mining (ICPM 2019), "BPI Challenge 2019 (challenge and data description)" (2019). https://icpmconference.org/2019/?p=302
  4. DataInterchange (vendor page), "EDI 214 T-Set: Structure, Benefits & Use Cases". https://datainterchange.com/edi-214-t-set-optimising-shipment-status-communication/
  5. Plaid (vendor guide), "ACH return codes: A complete guide for businesses". https://plaid.com/resources/ach/ach-return/
  6. Centers for Medicare & Medicaid Services, "Medicare remittance advice job aid JA6229 (Contractor Learning Resources)". https://www.cms.gov/Medicare/Medicare-Contracting/ContractorLearningResources/downloads//JA6229.pdf
  7. Legal Information Institute, Cornell Law School, "21 CFR 211.100 - Written procedures; deviations". https://www.law.cornell.edu/cfr/text/21/211.100
  8. arXiv preprint 2203.04706, "Data Representativity for Machine Learning and AI Systems" (2022). https://arxiv.org/pdf/2203.04706
  9. openIMIS wiki, "Automated Claims Adjudication (openIMIS specification)". https://openimis.atlassian.net/wiki/x/BgDIN
  10. Yao et al., Sierra (arXiv:2406.12045), "τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains" (2024). https://export.arxiv.org/pdf/2406.12045
  11. Weytjens and De Weerdt (arXiv:2107.01905), "Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring" (2021). https://export.arxiv.org/abs/2107.01905
  12. U.S. Department of Health and Human Services, Office for Civil Rights, "Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the HIPAA Privacy Rule" (2012). https://www.hhs.gov/hipaa/for-professionals/special-topics/de-identification
  13. IEEE Standards Association, "IEEE 1849-2023 - IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams" (2023). https://standards.ieee.org/ieee/1849/10907
  14. OCEL standard authors (ocel-standard.org; arXiv:2403.01975), "OCEL (Object-Centric Event Log) 2.0 Specification" (2023). https://arxiv.org/pdf/2403.01975

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