Response Validator Agent

The Response Validator agent validates generated responses against configurable quality and compliance criteria. It evaluates a response and returns a pass/fail judgment with specific issues identified when validation fails. Use this agent as a quality gate in any workflow where AI-generated content must meet defined standards before reaching the end user.

Use Cases

You need automated quality checks on AI-generated responses before they reach the user.
Compliance rules require that responses meet specific criteria (no PII, no unauthorized claims, appropriate tone).
You want to gate responses and route failures to human review or a retry loop.
You want to validate that action calling results or search-grounded responses meet accuracy and formatting standards before delivery.

Inputs

Input	Type	Description
`response`	Text	The response to validate.
`query`	Text	(Optional) The original query, for relevance checking.
`search_results`	SearchResults	(Optional) The source material, for grounding checks.

Outputs

Output	Type	Description
`is_valid`	Boolean	Whether the response passed all validation criteria.
`issues`	Text	Description of any validation failures. Empty if valid.

Configurations

Parameter	Description	Default
`criteria`	List of validation criteria. Each criterion has a name, description, and severity.	Required
`instructions`	Additional validation instructions.	None

Defining Validation Criteria

Each criterion is defined in natural language with a name and description. Common validation patterns include:

Grounding -- "The response must be supported by the provided search results. Do not include claims that are not backed by a source."
Tone and brand compliance -- "The response must use a professional, empathetic tone. Avoid jargon or overly casual language."
PII protection -- "The response must not include personal identifiable information such as email addresses, phone numbers, or account numbers."
Relevance -- "The response must directly address the user's query. Flag responses that go off-topic or provide unrequested information."
Completeness -- "The response must fully answer the question. If partial information is available, the response should acknowledge what is missing."

Quality Gating Patterns

The Response Validator is typically used as a quality gate between a response agent and the workflow output. When validation fails, you can:

Route to human review -- Send invalid responses to a human reviewer via the Human Collaboration agent.
Abstain from answering -- Suppress the response entirely using the Abstain from Answering agent and return a safe fallback message.
Branch on severity -- Use the is_valid output as a condition to branch the workflow: valid responses proceed to output, while invalid responses take an alternate path.

How to Use This Agent

Quality gate with human fallback:

chat_trigger -> knowledge_search -> respond_using_search_results -> response_validator
 -> [valid] -> workflow_output
 -> [invalid] -> human_collaboration -> workflow_output

Validate action calling results before delivery:

chat_trigger -> intelligent_actions -> respond_using_action_calling_results -> response_validator
 -> [valid] -> workflow_output
 -> [invalid] -> abstain_from_answering -> workflow_output

Rule Validation -- validates documents or data against rule sets (not specifically responses).
Abstain from Answering -- for suppressing responses entirely.
Human Collaboration -- for routing failed validations to human review.
Respond using Search Results -- a common upstream agent whose output benefits from validation.
Respond using Action Calling Results -- validate action summaries before they reach the user.

Last updated: Jul 3, 2026