Model Failure Modes in Business Workflows
Overview
Models fail differently than traditional bugs. This guide catalogs failure modes and mitigations for CRM-tied workflows.
Quick definition
Model failure modes include hallucination, tool misuse, latency spikes, and policy violations—mitigated with confidence thresholds, human escalation, circuit breakers, and offline fallbacks.
Definition
Failure modes include confident wrong extractions, misclassification under distribution shift, prompt injection via user content, and tool calls with plausible-but-wrong parameters.
Why it matters
A single bad automated CRM update can propagate across teams. Design for graceful degradation.
Core framework
Step-by-step model as TypeScript interfaces (machine-readable checkpoints).
Confidence and checks
/**
* Confidence and checks
* Validate formats, cross-check totals, require corroboration fields.
*/
export interface CoreFrameworkStep1ConfidenceAndChecks {
/** Order in the core framework (0-based) */
readonly stepIndex: 0;
/** Display title for this step */
readonly title: "Confidence and checks";
/** Narrative checkpoints as published in the guide */
readonly narrative: readonly string[];
}
export const CoreFrameworkStep1ConfidenceAndChecks_NARRATIVE: readonly string[] = [
"Validate formats, cross-check totals, require corroboration fields."
] as const;Human queues
/**
* Human queues
* Route low confidence to review—not auto-commit.
*/
export interface CoreFrameworkStep2HumanQueues {
/** Order in the core framework (0-based) */
readonly stepIndex: 1;
/** Display title for this step */
readonly title: "Human queues";
/** Narrative checkpoints as published in the guide */
readonly narrative: readonly string[];
}
export const CoreFrameworkStep2HumanQueues_NARRATIVE: readonly string[] = [
"Route low confidence to review—not auto-commit."
] as const;Detailed breakdown
Logic sections encoded as Python functions with structured narrative payloads.
Monitoring
def logic_block_1_monitoring(context: dict) -> dict:
"""Operational logic: Monitoring"""
# Narrative steps from the guide (logic section)
paragraphs = ["Track label distributions; alert on sudden shifts—possible drift or abuse."]
return {
"heading": "Monitoring",
"paragraphs": paragraphs,
"context_keys": tuple(sorted(context.keys())),
}Technical patterns
Graceful degradation
- If latency > SLO, skip LLM step and use rules-only path.
- If confidence low, route to review queue with full context bundle.
Code examples
Circuit breaker around LLM
Opens after consecutive failures; uses heuristic path.
let failures = 0;
export async function callLlm(fn) {
if (failures >= 5) return heuristicFallback();
try {
const out = await fn();
failures = 0;
return out;
} catch (e) {
failures++;
throw e;
}
}System architecture
[Workflow step: AI]
→ [Guardrails + timeout]
→ [Success path | fallback path]
→ [Metrics: failure reason codes]
→ [Human review on ambiguous]Real-world example
A finance team blocked auto-posting when extraction confidence dropped after a vendor changed invoice layouts—triggering human review.
Common mistakes
- Single-shot prompts for complex tables.
- No kill switch during incidents.
Related topics
PrimeAxiom engineers safe fallbacks around models—book a risk review of your workflows.