18 — Semantic Condition Checking

Concept overview

Semantic condition checking decides whether unstructured evidence satisfies a workflow condition, such as whether a customer has supplied a complete explanation or whether a document describes an exception. It is useful when exact string matching cannot capture meaning. The result should be a structured decision with evidence spans, an uncertainty state, and a versioned rubric.

Keep semantic classification separate from deterministic facts. Whether an amount exceeds a limit or a user has approval authority should be checked in code or policy. A semantic judge can establish that text appears to express consent, but it cannot independently establish that the speaker is authenticated or legally authorized.

Why it matters in production

Proof of concept

A prototype can label representative examples and compare rules, embeddings, and model-based classification. Include ambiguous and contradictory examples rather than only obvious matches.

Production

Production requires an abstention path, evidence references, stable rubrics, and thresholds calibrated to the cost of false acceptance versus false rejection.

Enterprise scale

At scale, evaluate performance by language, document type, and business segment. Monitor distribution changes and reviewer reversals because a classifier can remain confident while the meaning of operational language shifts.

Architecture components

Condition registry

Defines the semantic question, accepted labels, required evidence, and uncertainty behavior.

Evidence selector

Supplies relevant authorized text with provenance and preserves enough surrounding context.

Semantic evaluator

Produces a structured label and supporting spans under the versioned rubric.

Deterministic gate

Combines semantic output with verified facts, permissions, and hard business rules.

Review queue

Resolves ambiguous or high-impact decisions and records adjudicated outcomes.

The architecture, mapped

flowchart LR
 D["Customer explanation"] --> E["Evidence selector"]
 E --> S["Semantic condition evaluator"]
 S --> V["Evidence span validation"]
 V --> G["Deterministic business gate"]
 P["Verified policy facts"] --> G
 G --> A["Continue workflow"]
 G --> H["Ambiguity review"]
 G --> R["Condition not met"]

Runtime flow

1. 1

   Define condition

   Express the intended meaning and enumerate positive, negative, ambiguous, and contradictory examples.

2. 2

   Select evidence

   Retrieve relevant text with source identifiers and sufficient context to avoid misleading fragments.

3. 3

   Classify semantics

   Request a constrained label with evidence spans and an explicit unknown option.

4. 4

   Verify support

   Check that cited spans exist and that the classifier did not substitute unrelated evidence.

5. 5

   Apply hard rules

   Combine the semantic result with deterministic identity, amount, date, and policy checks.

6. 6

   Route uncertainty

   Continue, reject, or request review according to calibrated decision costs and task risk.

Checking whether a claim explanation addresses missing facts

Insurance operations · Illustrative enterprise scenario, not a reported customer outcome.

Business situation

A claims team asks customers to explain the circumstances of an incident before assigning a specialist reviewer.

The problem

Simple keyword checks accept irrelevant narratives and reject valid explanations phrased in unfamiliar language.

Agent requirement

The assistant must determine whether the response addresses required factual questions without deciding coverage or credibility.

Architecture

A semantic evaluator checks each required question, cites supporting spans, and passes labels to a deterministic completeness gate.

Runtime

• A customer response is segmented by incident time, location, and sequence; each condition receives supported, missing, or ambiguous status.
• Only verified completeness advances the administrative workflow; unresolved meaning generates a targeted follow-up question.

Failure scenario

If the text contains contradictory dates, the evaluator returns ambiguity and preserves both spans rather than selecting one silently.

Operational impact

Measure false-complete decisions, unnecessary follow-ups, reviewer disagreement, and performance by language. Completeness is not proof that the customer's account is true.

Architecture tradeoffs

Failure scenarios & recovery

Common architecture mistakes

Using a semantic model for numeric thresholds

Evaluate exact values in deterministic code.

Forcing every case into yes or no

Provide unknown and contradiction outcomes.

Treating evidence presence as factual truth

Keep completeness, credibility, and authorization separate.

When to choose this pattern

Choose it when

• Workflow conditions depend on meaning in unstructured text.
• Representative labeled examples can calibrate decisions.
• Ambiguous cases have a practical review path.

Avoid it when

• An exact business predicate already determines the answer.
• False acceptance is intolerable and no review exists.
• The evidence cannot be attributed to a trustworthy source.

Architecture checklist

• Version conditions and adjudication rubrics.
• Include ambiguous and contradictory test cases.
• Require verifiable evidence spans.
• Keep hard policy checks deterministic.
• Calibrate by decision cost and segment.
• Monitor reviewer reversals over time.

Related concepts

• 3 — Agentic State Machines
• 5 — Dynamic Routing
• 23 — Attribute-Based Access Control (ABAC)
• 31 — LLM Evaluation Frameworks (TruLens/Ragas)
• 44 — Human-in-the-Loop (HITL)

Primary references

• TruLens: the RAG triad
• NIST AI Risk Management Framework