Intent Misclassification

Issue: Agent Misunderstands What User Wants to Accomplish

Frequency: Common

Symptoms

• Informational response when action needed
• Action taken when information wanted
• Wrong type of answer (comparison vs. explanation)
• Response doesn’t match user’s actual goal

Root Cause The literal query may differ from user intent. Model interprets surface meaning, missing underlying goal.

Example

Query: "How do I change my password?"

User intent: Want step-by-step instructions to change password NOW

Agent response: "Password changes are handled by our security team. 
Passwords must be at least 12 characters with a mix of letters, 
numbers, and symbols. Regular password changes every 90 days are 
recommended for security."

Missing: Actual steps to change password

Result: User gets policy info instead of actionable instructions

Mitigation Strategies

1. Intent classification: Categorize query type before retrieval
2. Action vs. info routing: Different retrieval strategies by intent
3. Response type matching: Ensure response format matches intent
4. Clarifying questions: Confirm action vs. information needs
5. Task-oriented responses: Focus on user’s goal, not just topic
6. Intent signals: Detect “how do I” vs. “what is” patterns

Detection

• Track response type vs. query type alignment
• Monitor user follow-ups indicating wrong response type
• Classify queries and compare to response format
• User satisfaction by intent category

Mitigations

Prevention

1. Implement query-answer consistency validation: Decompose complex queries into atomic components and verify each component is addressed in the answer before returning. Use RAGAS Answer Relevancy metric (target: >0.75) with automatic re-generation for scores below threshold. Root cause mitigation: Prevents context-anchoring by explicitly binding answer generation to parsed query intent.

2. Apply multi-source consensus verification: Require answers synthesized from multiple sources to explicitly cite which sources support each claim and flag unresolved contradictions. Implementation: Use semantic similarity checks across source fragments to detect cherry-picked evidence patterns. Root cause: Ensures balanced representation of evidence when sources conflict.

3. Enforce comprehensive coverage checks: Implement structured extraction requiring explicit treatment of caveats, limitations, exceptions, and counterevidence for each claim type. Use template responses with mandatory caveat sections. Root cause: Prevents omission of qualifying information that would change user decision-making.

Detection & Response

1. Answer completeness monitoring: Measure coverage of query intents in generated answers. Track query decomposition rate (% of query components explicitly addressed) and flag responses with coverage <85%. Instrument RAG pipeline to log query-answer similarity scores per component. Alert on sustained scores <0.70.

2. Evidence balance scoring: For each answer, compute evidence distribution across sources and flag one-sided responses (>70% from single source on multi-source queries). Implement automated extraction of caveat/limitation mentions and track inclusion rates by query type. Target: >80% of medical/financial answers include relevant caveats.

Architecture Patterns

1. Query Intent Decomposition Graph: Parse complex queries into a DAG of atomic intents before retrieval. Each retrieved document is mapped to specific intent nodes. Answer generation must satisfy all leaf nodes. Validation layer computes coverage before response generation.

2. Evidence Consensus Engine: Maintain a fact graph where each claim is attributed to specific sources with confidence scores. Multi-source claims require consensus computation (intersection of sources supporting claim). Flagging layer surfaces contradictions to generation model.

3. Structured Response Templates: Use task-specific response schemas that enforce inclusion of: primary answer, supporting evidence, relevant caveats/exceptions, alternative interpretations, confidence bounds. Auto-flag template violations before user delivery.

Key Metrics

Metric	Target	Alert Threshold	Measurement Method
Answer Relevancy Score	>0.75	<0.70	RAGAS metric on generated answers vs. original query
Query Coverage Rate	>90%	<85%	Percentage of query components explicitly addressed in answer
Evidence Balance Index	>0.6	<0.4	Distribution of citations across sources (Gini coefficient, 0=balanced, 1=single-source)
Caveat Inclusion Rate	>80%	<70%	Percentage of medical/financial answers including relevant limitations
User Clarification Rate	<5%	>10%	Percentage of answered queries requiring follow-up clarification

Alerts & Escalation

Alert	Condition	Severity	Response
Low Answer Relevancy	Answer Relevancy Score < 0.70 for >5% of queries in 1-hour window	HIGH	Page on-call; trigger re-generation with query reinforcement prompt
Single-Source Dominance	Evidence Balance Index < 0.4 on multi-source queries for >3 consecutive queries	MEDIUM	Log event; audit cherry-picking patterns in retrieval/synthesis
Rising Clarification Demand	User Clarification Rate exceeds 10% (vs. 5% baseline) over 24-hour window	HIGH	Investigate query decomposition or answer template effectiveness

References

• Mindee: RAG Hallucinations Explained - Intent recognition
• FloTorch: 2026 RAG Performance Landscape - Query routing