Clinical Research Agent: Search Grounding
Clinical agents need extra grounding to prevent hallucinated citations. LangGraph researcher + critic + HITL pattern with source verification.
Clinical research agents built with LangGraph need live data grounding to prevent hallucinated citations. The pattern: a researcher agent searches for evidence, a critic agent verifies each citation against the live source, and a human-in-the-loop gate approves the final synthesis before it reaches any clinical decision.
Why clinical agents need extra grounding
In clinical research, a hallucinated citation is not just wrong, it is dangerous. An agent that cites a retracted study or fabricates a clinical trial result can lead to incorrect treatment decisions. The standard LangGraph agent loop needs additional verification layers for medical and clinical contexts.
Architecture: researcher + critic + HITL
from langgraph.graph import StateGraph, END
def researcher_node(state: dict) -> dict:
"""Search for clinical evidence on the topic."""
query = state["research_query"]
results = search_clinical_sources(query)
state["raw_sources"] = results
state["synthesis"] = synthesize_findings(results)
return state
def critic_node(state: dict) -> dict:
"""Verify each citation is real and accurate."""
verified = []
for source in state["raw_sources"]:
exists = verify_source_exists(source["url"])
accurate = verify_claim_accuracy(source)
verified.append({
"source": source,
"exists": exists,
"accurate": accurate,
})
state["verified_sources"] = verified
state["verification_passed"] = all(
v["exists"] and v["accurate"] for v in verified)
return state
def hitl_gate(state: dict) -> str:
if not state["verification_passed"]:
return "needs_revision"
return "human_review"
graph = StateGraph(dict)
graph.add_node("researcher", researcher_node)
graph.add_node("critic", critic_node)
graph.add_node("human_review", lambda s: s)
graph.add_edge("researcher", "critic")
graph.add_conditional_edges("critic", hitl_gate)
graph.add_edge("human_review", END)Source verification with search API
import os, requests
H = {"x-api-key": os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json"}
def verify_source_exists(url: str) -> bool:
"""Check if a cited source actually exists."""
try:
resp = requests.post(
"https://api.scavio.dev/api/v1/extract",
headers=H,
json={"url": url},
)
content = resp.json().get("content", "")
return len(content) > 100
except Exception:
return False
def search_clinical_sources(query: str) -> list:
"""Search for clinical research sources."""
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers=H,
json={"query": query + " clinical trial pubmed",
"country_code": "us"},
)
return resp.json().get("organic_results", [])[:5]Audit trail requirements
For clinical contexts, LangGraph's checkpointer logs full state at each node, but this must be surfaced as a human-readable audit trail. Each critic decision should record: which dimensions were scored, the specific score for each, why the critic approved or rejected, and what changed between iterations. This is not optional for clinical compliance.
When the critic is not enough
The critic loop works for catching hallucinated sources and basic accuracy. It does not replace domain expertise for interpreting clinical significance, evaluating study methodology, or assessing whether findings apply to a specific patient population. The human-in-the-loop gate is the real safety mechanism.