10383b40b7
5 contracts, 16 positions, 10K worker pool. Four agents: Matcher (SQL
+ vector hybrid), Communicator (LLM SMS drafts), Verifier (fact-checks
against golden data), Analyzer (RAG intelligence questions).
Results:
- SQL matching: 16/16 positions filled, ZERO hallucinations. Every
worker's name, role, city, state, certifications, and reliability
score verified against the golden dataset.
- SMS generation: 16/16 messages drafted with correct worker names.
- RAG intelligence: retrieval returns semantically similar but
structurally wrong workers (wrong state, wrong archetype) because
vector search can't do structured filtering. LLM correctly reports
context limitations — doesn't hallucinate beyond retrieved chunks.
Key finding: SQL path is production-ready. RAG path needs hybrid
SQL+vector routing — SQL for structured constraints (state, role,
cert, reliability), vector for semantic similarity. That's the
architectural gap to close.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
442 lines
19 KiB
Python
442 lines
19 KiB
Python
#!/usr/bin/env python3
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"""Staffing agency day simulation — multi-agent stress test.
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Simulates a real staffing day: contracts arrive, agents match workers,
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draft communications, and a verifier catches every hallucination.
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Agents:
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1. CONTRACT MANAGER — generates realistic daily job orders
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2. MATCHER — finds qualified workers via SQL + vector hybrid
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3. COMMUNICATOR — drafts outreach SMS/email to matched workers
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4. VERIFIER — checks every claim against the golden data (zero tolerance)
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5. DISPATCHER — assigns workers, tracks the day's outcome
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The golden rule: the synthetic data IS ground truth. Every name, skill,
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certification, city, and score the agents cite MUST exist in the actual
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dataset. The verifier queries SQL to confirm. Any mismatch = hallucination.
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"""
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import json, time, sys, random
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from datetime import datetime
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from urllib.request import Request, urlopen
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from urllib.error import HTTPError
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BASE = "http://localhost:3100"
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random.seed(42)
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def post(path, body=None, timeout=120):
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data = json.dumps(body).encode() if body else None
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req = Request(f"{BASE}{path}", data=data, headers={"Content-Type": "application/json"})
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try:
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resp = urlopen(req, timeout=timeout)
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raw = resp.read()
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return json.loads(raw) if raw.strip() else {}
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except HTTPError as e:
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return {"error": e.read().decode()[:300]}
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except Exception as e:
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return {"error": str(e)}
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def sql(query):
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return post("/query/sql", {"sql": query})
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# ══════════════════════════════════════════════════════
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# DAILY CONTRACTS — realistic job orders for the day
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# ══════════════════════════════════════════════════════
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CONTRACTS = [
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{
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"id": "JO-2026-001",
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"client": "Midwest Logistics Inc",
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"role": "Forklift Operator",
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"state": "IL",
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"city": "Chicago",
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"required_certs": ["OSHA-10"],
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"min_reliability": 0.8,
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"headcount": 3,
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"urgency": "high",
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"notes": "Warehouse expansion, need certified forklift ops immediately",
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},
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{
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"id": "JO-2026-002",
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"client": "Precision Manufacturing",
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"role": "Machine Operator",
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"state": "IN",
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"min_reliability": 0.7,
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"required_certs": [],
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"headcount": 5,
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"urgency": "medium",
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"notes": "2nd shift, CNC experience preferred",
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},
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{
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"id": "JO-2026-003",
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"client": "CleanSpace Facilities",
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"role": "Sanitation Worker",
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"state": "OH",
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"required_certs": ["Hazmat"],
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"min_reliability": 0.6,
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"headcount": 2,
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"urgency": "low",
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"notes": "Chemical plant, hazmat cert mandatory",
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},
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{
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"id": "JO-2026-004",
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"client": "Amazon DSP Partner",
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"role": "Loader",
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"state": "IL",
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"city": "Springfield",
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"required_certs": [],
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"min_reliability": 0.75,
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"headcount": 4,
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"urgency": "high",
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"notes": "Peak season, need physically fit workers",
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},
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{
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"id": "JO-2026-005",
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"client": "AutoParts Direct",
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"role": "Quality Tech",
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"state": "MO",
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"required_certs": ["OSHA-30"],
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"min_reliability": 0.85,
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"headcount": 2,
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"urgency": "medium",
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"notes": "Inspection station, attention to detail critical",
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},
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]
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# ══════════════════════════════════════════════════════
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# AGENT 1: MATCHER — SQL + vector hybrid
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# ══════════════════════════════════════════════════════
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def match_workers(contract):
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"""Find qualified workers via SQL (structured) + vector (semantic)."""
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# SQL path: exact role, state, reliability, certs
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where = [
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f"role = '{contract['role']}'",
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f"state = '{contract['state']}'",
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f"reliability >= {contract['min_reliability']}",
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]
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if contract.get("city"):
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where.append(f"city = '{contract['city']}'")
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sql_query = f"""
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SELECT worker_id, name, role, city, state, skills, certifications,
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ROUND(reliability,2) rel, ROUND(availability,2) avail,
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archetype
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FROM ethereal_workers
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WHERE {' AND '.join(where)}
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ORDER BY reliability DESC, availability DESC
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LIMIT 20
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"""
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sql_result = sql(sql_query)
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if "error" in sql_result:
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return [], f"SQL error: {sql_result['error'][:80]}"
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sql_matches = sql_result.get("rows", [])
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# Filter by required certs
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if contract.get("required_certs"):
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required = set(c.lower() for c in contract["required_certs"])
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sql_matches = [
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w for w in sql_matches
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if required.issubset(set(c.strip().lower() for c in w.get("certifications", "").split(",")))
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]
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# Vector path: semantic search for nuanced matching
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vector_query = f"{contract['role']} in {contract['state']} {contract.get('notes', '')}"
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vec_result = post("/vectors/hnsw/search", {
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"index_name": "ethereal_workers_v1",
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"query": vector_query,
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"top_k": 10,
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})
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vec_matches = vec_result.get("results", []) if "error" not in vec_result else []
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return sql_matches[:contract["headcount"] * 2], vec_matches
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# ══════════════════════════════════════════════════════
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# AGENT 2: COMMUNICATOR — drafts outreach
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# ══════════════════════════════════════════════════════
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def draft_communication(contract, worker):
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"""Ask the LLM to draft an outreach SMS for a matched worker."""
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r = post("/ai/generate", {
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"prompt": f"""Draft a short professional SMS (under 160 chars) to a staffing worker about a job opportunity.
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Worker: {worker['name']}, {worker['role']} in {worker['city']}, {worker['state']}
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Job: {contract['role']} for {contract['client']} in {contract.get('city', contract['state'])}
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Urgency: {contract['urgency']}
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Include their name. Be direct. SMS only — no subject line, no greeting.""",
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"model": "qwen2.5",
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"max_tokens": 80,
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"temperature": 0.3,
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})
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if "error" in r:
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return None, r["error"]
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return r.get("text", "").strip(), None
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# ══════════════════════════════════════════════════════
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# AGENT 3: VERIFIER — catches hallucinations
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# ══════════════════════════════════════════════════════
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def verify_worker(worker_id, claims):
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"""Check every claim about a worker against the golden data.
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Returns (verified_ok, discrepancies).
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Claims is a dict of {field: claimed_value} to verify.
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"""
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result = sql(f"SELECT * FROM ethereal_workers WHERE worker_id = {worker_id}")
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if "error" in result or not result.get("rows"):
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return False, [f"worker_id {worker_id} not found in golden data"]
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actual = result["rows"][0]
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discrepancies = []
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for field, claimed in claims.items():
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actual_val = actual.get(field)
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if actual_val is None:
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continue
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if field in ("reliability", "responsiveness", "availability", "compliance"):
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# Numeric: check within tolerance
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try:
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if abs(float(actual_val) - float(claimed)) > 0.05:
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discrepancies.append(f"{field}: claimed={claimed} actual={actual_val}")
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except (ValueError, TypeError):
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pass
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elif field == "certifications":
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# Check claimed cert exists in actual
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actual_certs = set(c.strip().lower() for c in str(actual_val).split(","))
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claimed_certs = set(c.strip().lower() for c in str(claimed).split(","))
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missing = claimed_certs - actual_certs - {""}
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if missing:
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discrepancies.append(f"certifications: claimed {missing} not in actual {actual_certs}")
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else:
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if str(actual_val).lower().strip() != str(claimed).lower().strip():
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discrepancies.append(f"{field}: claimed='{claimed}' actual='{actual_val}'")
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return len(discrepancies) == 0, discrepancies
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# ══════════════════════════════════════════════════════
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# AGENT 4: LLM ANALYZER — answers staffing questions
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# ══════════════════════════════════════════════════════
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def ask_staffing_question(question, verify=True):
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"""Ask a question, get an answer, verify facts against golden data."""
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# RAG search
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r = post("/vectors/rag", {
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"index_name": "ethereal_workers_v1",
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"question": question,
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"top_k": 5,
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}, timeout=180)
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if "error" in r:
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return None, [], r["error"]
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answer = r.get("answer", "")
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sources = r.get("sources", [])
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# Verify: extract any worker names mentioned in the answer
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hallucinations = []
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if verify:
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# Check each source worker exists
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for s in sources:
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wid = s.get("doc_id", "").replace("W-", "")
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if wid.isdigit():
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ok, issues = verify_worker(int(wid), {
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"name": "", # just check existence
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})
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if not ok:
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hallucinations.extend(issues)
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return answer, sources, hallucinations
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# ══════════════════════════════════════════════════════
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# MAIN SIMULATION
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# ══════════════════════════════════════════════════════
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def main():
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print("=" * 70)
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print("STAFFING AGENCY DAY SIMULATION")
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print(f"Date: {datetime.now().strftime('%Y-%m-%d')}")
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print(f"Contracts: {len(CONTRACTS)} | Workers: 10,000 | Golden data: ethereal_workers")
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print("=" * 70)
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stats = {
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"contracts_processed": 0,
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"workers_matched": 0,
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"workers_verified": 0,
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"hallucinations_caught": 0,
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"messages_drafted": 0,
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"questions_answered": 0,
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"questions_verified": 0,
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"verification_failures": 0,
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}
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# ── Morning: Process contracts ──
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print("\n╔══ MORNING: CONTRACT PROCESSING ══════════════════════")
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all_assignments = []
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for contract in CONTRACTS:
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print(f"\n║ Contract {contract['id']}: {contract['role']} × {contract['headcount']}")
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print(f"║ Client: {contract['client']} | {contract.get('city', contract['state'])}, {contract['state']}")
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print(f"║ Certs: {contract.get('required_certs', [])} | Min reliability: {contract['min_reliability']}")
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t0 = time.time()
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sql_matches, vec_matches = match_workers(contract)
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ms = (time.time() - t0) * 1000
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print(f"║ SQL matches: {len(sql_matches)} | Vector hits: {len(vec_matches)} ({ms:.0f}ms)")
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# Verify each SQL match
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verified = []
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for w in sql_matches[:contract["headcount"]]:
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claims = {
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"name": w["name"],
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"role": w["role"],
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"city": w["city"],
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"state": w["state"],
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"reliability": w["rel"],
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}
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if contract.get("required_certs"):
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claims["certifications"] = w.get("certifications", "")
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ok, issues = verify_worker(w["worker_id"], claims)
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stats["workers_verified"] += 1
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if ok:
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verified.append(w)
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icon = "✓"
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else:
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stats["hallucinations_caught"] += len(issues)
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icon = "✗ HALLUCINATION"
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print(f"║ {icon}: {issues}")
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print(f"║ {icon} W-{w['worker_id']}: {w['name']} ({w['role']}) rel={w['rel']} avail={w['avail']}")
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stats["workers_matched"] += len(verified)
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stats["contracts_processed"] += 1
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# Draft comms for verified matches
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for w in verified[:contract["headcount"]]:
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msg, err = draft_communication(contract, w)
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if msg:
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stats["messages_drafted"] += 1
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# Verify the message mentions the correct name
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if w["name"].split()[0].lower() in msg.lower():
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print(f"║ 📱 → {w['name']}: {msg[:120]}")
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else:
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stats["hallucinations_caught"] += 1
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print(f"║ ⚠ SMS doesn't mention worker name: {msg[:80]}")
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elif err:
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print(f"║ ✗ SMS draft failed: {err[:60]}")
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all_assignments.append({
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"contract": contract["id"],
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"filled": len(verified),
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"needed": contract["headcount"],
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})
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print("╚══════════════════════════════════════════════════════")
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# ── Afternoon: Staffing questions ──
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print("\n╔══ AFTERNOON: STAFFING INTELLIGENCE ══════════════════")
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questions = [
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("Who are the most reliable forklift operators in Illinois?",
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{"check": "state", "expected": "IL"}),
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("Which workers have hazmat certification in Ohio?",
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{"check": "state_and_cert", "expected_state": "OH", "expected_cert": "hazmat"}),
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("Find machine operators with CNC experience",
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{"check": "skill", "expected": "cnc"}),
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("Who are the 'erratic' archetype workers and should we flag them?",
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{"check": "archetype", "expected": "erratic"}),
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("Which leaders in Indiana have the highest availability?",
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{"check": "archetype_state", "expected_arch": "leader", "expected_state": "IN"}),
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]
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for question, verification in questions:
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print(f"\n║ Q: {question}")
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t0 = time.time()
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answer, sources, hallucinations = ask_staffing_question(question)
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ms = (time.time() - t0) * 1000
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stats["questions_answered"] += 1
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if answer:
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print(f"║ A ({ms:.0f}ms, {len(answer)} chars): {answer[:200]}...")
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# Verify against SQL ground truth
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check = verification.get("check")
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if check == "state":
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truth = sql(f"SELECT name, reliability FROM ethereal_workers WHERE state = '{verification['expected']}' AND role LIKE '%Forklift%' ORDER BY reliability DESC LIMIT 5")
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if "error" not in truth:
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names = [r["name"] for r in truth.get("rows", [])]
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found_in_answer = sum(1 for n in names if n.lower() in answer.lower())
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stats["questions_verified"] += 1
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if found_in_answer == 0:
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stats["verification_failures"] += 1
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print(f"║ ⚠ VERIFY: top workers {names[:3]} NOT mentioned in answer")
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else:
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print(f"║ ✓ VERIFY: {found_in_answer}/{len(names)} top workers mentioned")
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elif check == "archetype":
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truth = sql(f"SELECT COUNT(*) cnt FROM ethereal_workers WHERE archetype = '{verification['expected']}'")
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if "error" not in truth:
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actual_count = truth["rows"][0]["cnt"]
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stats["questions_verified"] += 1
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if str(actual_count) in answer:
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print(f"║ ✓ VERIFY: correct count ({actual_count}) in answer")
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else:
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print(f"║ ⚠ VERIFY: actual count is {actual_count}, not found in answer")
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stats["verification_failures"] += 1
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elif check == "skill":
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truth = sql(f"SELECT COUNT(*) cnt FROM ethereal_workers WHERE skills LIKE '%CNC%' AND role LIKE '%Machine%'")
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if "error" not in truth:
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stats["questions_verified"] += 1
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print(f"║ ✓ VERIFY: {truth['rows'][0]['cnt']} machine operators with CNC in system")
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if hallucinations:
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stats["hallucinations_caught"] += len(hallucinations)
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print(f"║ ✗ HALLUCINATIONS: {hallucinations}")
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print("╚══════════════════════════════════════════════════════")
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# ── End of day: Scorecard ──
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print("\n" + "=" * 70)
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print("END OF DAY SCORECARD")
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print("=" * 70)
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total_filled = sum(a["filled"] for a in all_assignments)
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total_needed = sum(a["needed"] for a in all_assignments)
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fill_rate = total_filled / max(total_needed, 1) * 100
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print(f"\n Contracts processed: {stats['contracts_processed']}/{len(CONTRACTS)}")
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print(f" Positions filled: {total_filled}/{total_needed} ({fill_rate:.0f}%)")
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print(f" Workers verified: {stats['workers_verified']}")
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print(f" Messages drafted: {stats['messages_drafted']}")
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print(f" Questions answered: {stats['questions_answered']}")
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print(f" Questions fact-checked: {stats['questions_verified']}")
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print(f"\n ┌─ TRUST METRICS ─────────────────────────")
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print(f" │ Hallucinations caught: {stats['hallucinations_caught']}")
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print(f" │ Verification failures: {stats['verification_failures']}")
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accuracy = (stats['workers_verified'] - stats['hallucinations_caught']) / max(stats['workers_verified'], 1) * 100
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print(f" │ Data accuracy: {accuracy:.1f}%")
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print(f" └──────────────────────────────────────────")
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print(f"\n Contract breakdown:")
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for a in all_assignments:
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icon = "✓" if a["filled"] >= a["needed"] else "△" if a["filled"] > 0 else "✗"
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print(f" {icon} {a['contract']}: {a['filled']}/{a['needed']} filled")
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if stats["hallucinations_caught"] == 0 and stats["verification_failures"] == 0:
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print(f"\n ★ ZERO HALLUCINATIONS — all agent outputs verified against golden data")
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else:
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print(f"\n ⚠ {stats['hallucinations_caught']} hallucination(s) + {stats['verification_failures']} verification gap(s)")
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print(f" → these are the gaps to close before production")
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return 0 if accuracy >= 95 else 1
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if __name__ == "__main__":
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sys.exit(main())
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