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Written by Max Zeshut
Founder at Agentmelt
The process of verifying that an AI agent's output meets quality, accuracy, safety, and format requirements before it's delivered to the user or passed to the next step in a workflow. Validation can be automated (schema checks, PII detection, confidence scoring, fact-checking against source documents) or human (reviewer approves before sending). Output validation is the last line of defense against hallucination, policy violations, and formatting errors in production agents.
A finance agent generates a quarterly report. Output validation checks: all numbers trace back to source data (no hallucinated figures), the report follows the required template, no client names appear in sections visible to other clients, and the summary accurately reflects the underlying data.