chore: import upstream snapshot with attribution

examples/financial_research_agent/agents/verifier_agent.py

@@ -0,0 +1,48 @@
+from typing import Literal
+
+from pydantic import BaseModel
+
+from agents import Agent
+
+# Agent to sanity‑check a synthesized report for consistency and recall.
+# This can be used to flag potential gaps or obvious mistakes.
+VERIFIER_PROMPT = (
+    "You are a meticulous evidence auditor. You will receive an original request, an explicit "
+    "research cutoff date, a financial report, and structured web research evidence with source "
+    "URLs. Judge the report only against that supplied evidence; do not reject or approve claims "
+    "based on your own memory. Check that material numeric and time-sensitive claims are supported "
+    "by the evidence, that citations use supplied URLs, that the report is internally consistent, "
+    "and that uncertainty is appropriately caveated. Treat information published on or before the "
+    "research cutoff as potentially available. Mark unsupported claims separately from claims that "
+    "the evidence directly contradicts."
+)
+
+
+class VerificationIssue(BaseModel):
+    claim: str
+    """The report claim that needs attention."""
+
+    category: Literal["unsupported", "contradicted", "stale_or_unreleased", "other"]
+    """The evidence problem associated with the claim."""
+
+    explanation: str
+    """Why the evidence does not support the claim."""
+
+    source_urls: list[str]
+    """Relevant supplied source URLs, if any."""
+
+
+class VerificationResult(BaseModel):
+    verified: bool
+    """Whether the report is coherent and supported by the supplied evidence."""
+
+    issues: list[VerificationIssue]
+    """Evidence-based issues that must be corrected before publication."""
+
+
+verifier_agent = Agent(
+    name="VerificationAgent",
+    instructions=VERIFIER_PROMPT,
+    model="gpt-5.6-sol",
+    output_type=VerificationResult,
+)