SuperBizAgent-java/openspec/specs/chat-verifier-agent/spec.md

chat-verifier-agent Specification

Purpose

TBD - created by archiving change chat-verifier-agent. Update Purpose after archive.

Requirements

Requirement: Verifier SHALL fact-check Executor answers

The system SHALL have a Verifier Agent that reads the Executor's answer and the tool call history, then produces a structured verdict.

Scenario: PASS verdict when all claims have evidence

• WHEN all critical facts in the Executor's answer have direct or indirect support in tool call results
• AND at least one critical fact has direct evidence
• AND no critical fact is contradicted
• THEN the Verifier SHALL output verdict="PASS" with groundedness_score ≥ 0.5

Scenario: LOW_CONFID verdict with partial evidence

• WHEN no critical fact contradicts the tool results
• AND some critical facts have no supporting evidence
• THEN the Verifier SHALL output verdict="LOW_CONFID"

Scenario: LOW_CONFID verdict with only indirect support

• WHEN no critical fact contradicts the tool results
• AND all critical facts are only indirectly supported
• THEN the Verifier SHALL output verdict="LOW_CONFID"

Scenario: REJECT verdict when claims contradict evidence

• WHEN any critical fact in the Executor's answer contradicts tool call results
• OR the answer fabricates a key entity, error code, or conclusion that does not exist in the tool evidence
• THEN the Verifier SHALL output verdict="REJECT"

Requirement: Verifier SHALL output structured JSON

The Verifier SHALL output a JSON object with verdict, groundedness_score, facts_checked array, and rationale.

Scenario: Output format validation

• WHEN the Verifier completes its analysis
• THEN the output SHALL contain "verdict", "groundedness_score", "facts_checked", and "rationale" fields
• AND groundedness_score SHALL be a float between 0.0 and 1.0
• AND verdict SHALL be one of "PASS", "LOW_CONFID", or "REJECT"

Scenario: strict schema output

• WHEN the Verifier returns its result
• THEN it SHALL output exactly one JSON object
• AND it SHALL NOT output Markdown, code fences, or explanatory text outside the JSON object
• AND the JSON object SHALL include critical_fact_count
• AND each facts_checked item SHALL include fact, is_critical, verification, and detail

Requirement: facts_checked SHALL use a fixed classification set

Each checked fact SHALL be labeled using a fixed evidence classification.

Scenario: fact classification values

• WHEN the Verifier emits facts_checked
• THEN each fact SHALL use one of direct_evidence, indirect_support, no_evidence, or contradicted

Requirement: groundedness_score SHALL be derived from fact classifications

The groundedness score SHALL be computed from critical fact classifications instead of being freely chosen by the model.

Scenario: contradicted fact forces reject

• WHEN any critical fact is labeled contradicted
• THEN the Verifier SHALL output verdict="REJECT"
• AND groundedness_score SHALL be 0.0

Scenario: score derived from supported facts

• WHEN no critical fact is contradicted
• THEN groundedness_score SHALL be computed from the mapped values of critical facts
• AND the implementation SHALL use the fixed mapping direct_evidence=1.0, indirect_support=0.6, no_evidence=0.0
• AND the result SHALL be clamped into [0.0, 1.0]

Requirement: ChatService SHALL route based on Verifier verdict

The system SHALL use ChatService for explicit single-round Planner → Executor → Verifier orchestration and SHALL use ChatService to control whether an additional round is allowed.

Scenario: PASS → direct output

• WHEN Verifier outputs verdict="PASS"
• THEN the system SHALL output the Executor's answer directly

Scenario: LOW_CONFID score≥0.5 → output with disclaimer

• WHEN Verifier outputs verdict="LOW_CONFID" with groundedness_score ≥ 0.5
• THEN the system SHALL output the Executor's answer prefixed with a fixed confidence disclaimer

Scenario: LOW_CONFID score<0.5 → trigger one additional round

• WHEN Verifier outputs verdict="LOW_CONFID" with groundedness_score < 0.5 and this is the first callback
• THEN the ChatService SHALL invoke one additional Planner → Executor → Verifier round to supplement evidence
• AND after the second Verifier run, verdict="LOW_CONFID" SHALL be output with a confidence disclaimer
• AND after the second Verifier run, verdict="REJECT" SHALL still produce a degraded output

Scenario: REJECT does not enter retry round

• WHEN Verifier outputs verdict="REJECT"
• THEN the system SHALL NOT start a retry round for evidence补充
• AND it SHALL produce a degraded output directly

Scenario: REJECT → degraded output

• WHEN Verifier outputs verdict="REJECT"
• THEN the system SHALL output a degraded result indicating the answer cannot be reliably generated
• AND it SHALL NOT pass through the raw Executor answer

Requirement: User-facing verifier outputs SHALL follow fixed templates

The system SHALL use fixed output protocols for LOW_CONFID and REJECT user-facing responses.

Scenario: LOW_CONFID uses disclaimer template

• WHEN the final verdict is LOW_CONFID
• THEN the user-facing response SHALL prepend a fixed disclaimer before the Executor answer
• AND optional evidence gaps, if present, SHALL come only from verifier-identified critical gaps

Scenario: REJECT uses degraded template

• WHEN the final verdict is REJECT
• THEN the user-facing response SHALL use a degraded template
• AND it SHALL include only confirmed facts, evidence gaps, and next-step suggestions
• AND it SHALL NOT include unverified raw answer content

Requirement: Verifier SHALL be observable

The Verifier's verdict SHALL be persisted for observability.

Scenario: verdict written to self_evaluation

• WHEN the Verifier produces a verdict
• THEN the ChatService SHALL write the verdict data under diagnosis_session.self_evaluation.verifier_evaluation
• AND existing rule_evaluation data SHALL be preserved

Requirement: self_evaluation SHALL be a container object

The diagnosis_session.self_evaluation field SHALL store multiple evaluation channels in one JSON object.

Scenario: rule evaluation stored separately

• WHEN the rule-based evidence scoring completes
• THEN the EvaluationService SHALL write the result under rule_evaluation
• AND existing verifier_evaluation data SHALL be preserved

Scenario: verifier evaluation stored separately

• WHEN the Verifier completes
• THEN the ChatService SHALL write the result under verifier_evaluation
• AND existing rule_evaluation data SHALL be preserved

Scenario: no whole-object overwrite after initialization

• WHEN either evaluation channel updates self_evaluation
• THEN the implementation SHALL use read-modify-write semantics
• AND it SHALL NOT replace the whole JSON object except when initializing from null

Requirement: Verifier SHALL consume explicit verification inputs

The Verifier SHALL receive explicit verification inputs rather than inferring them only from raw conversation history.

Scenario: explicit input blocks available to Verifier

• WHEN the Verifier starts
• THEN the system SHALL provide original_query, executor_final_answer, and tool_trace_summary as explicit inputs
• AND retry_context SHALL be provided on the second round only
• AND message filtering MAY be used only to remove intermediate reasoning or unrelated noise

Scenario: tool trace summary derived from tool facts

• WHEN the system prepares verifier inputs
• THEN tool_trace_summary SHALL be generated from tool invocation facts
• AND each summary item SHALL include tool name, success state, input summary, output summary, and evidence level
• AND raw conversation history SHALL NOT be the only source of verifier evidence context

Scenario: tool trace summary preserves invocation references

• WHEN the system prepares verifier inputs
• THEN each summary item SHALL include a stable trace_ref
• AND each summary item SHALL preserve source_invocation_ids for the tool invocation rows that contributed to the summary
• AND each summary item SHOULD include query samples, retrieval layers, relevance levels, and source document labels when available

Scenario: only evidence-bearing tools included

• WHEN the system generates tool_trace_summary
• THEN it SHALL include only evidence-bearing tool invocations
• AND non-evidence helper tools such as time or formatting tools SHALL be excluded by default

Scenario: failed evidence calls preserved as evidence gaps

• WHEN an evidence-bearing tool invocation fails or returns no usable evidence
• THEN the summary SHALL still include that invocation
• AND it SHALL mark the entry as unsuccessful with an evidence level representing no evidence

Scenario: repeated tool calls may be compacted

• WHEN repeated tool invocations concern the same tool, topic domain, and round
• THEN the system MAY compact them into a merged summary entry
• AND the merged entry SHALL preserve the first effective hit and the count of repeated, failed, or no-hit calls

Scenario: raw outputs not passed through in full

• WHEN a tool invocation returns large raw content
• THEN tool_trace_summary SHALL keep only a minimal evidence summary
• AND the raw output SHALL NOT be passed through in full to the Verifier

Scenario: MessagesModelHook used only for noise reduction

• WHEN a MessagesModelHook is used for the Verifier
• THEN it MAY remove intermediate reasoning or irrelevant messages
• AND it SHALL NOT be the primary source for assembling verifier business inputs

Requirement: Verifier facts SHALL be auditable

Verifier facts SHALL be linkable to the evidence summaries used during verification.

Scenario: facts_checked contains evidence refs

• WHEN the Verifier emits facts_checked
• THEN each fact SHALL include evidence_refs
• AND each evidence ref SHALL point to an existing tool_trace_summary.trace_ref
• AND each evidence ref SHALL preserve the relevant source_invocation_ids when available

Scenario: verifier evaluation persists traceability snapshot

• WHEN the ChatService persists verifier_evaluation
• THEN it SHALL include traceability_version
• AND it SHALL include the tool_trace_summary snapshot used by the Verifier