Delivery overview
What is included, how to rerun it, and the boundary of the demonstration.
README.mdDownload raw file
# Synthetic Expense Reimbursement Agent Release Gate > **SYNTHETIC SAMPLE, NOT A CLIENT OUTCOME.** Every policy, claim, identifier, output, latency, and cost in this directory is fictional. The sample contains no employer or client data. It demonstrates the shape of a release-gate handoff, not results from a paid engagement or production deployment. This small, inspectable example shows how a production-agent failure becomes a versioned regression case and an executable release decision. The fictional workflow decides whether to approve an employee expense, request evidence, escalate it, reject it, or take no action. The sample deliberately includes three output snapshots: - `baseline-v1` has safety and policy failures and must not ship. - `candidate-v2` removes critical failures but retains one provenance regression and a latency regression, so it needs review. - `candidate-v3` satisfies the functional, safety, latency, and cost gates. ## What is included | Artifact | Purpose | |---|---| | `POLICY.md` | Current fictional policy and exact rule identifiers | | `case.schema.json` | JSON Schema for each evaluation case | | `cases.jsonl` | Exactly 12 versioned cases with expected decisions, tool calls, arguments, citations, and forbidden actions | | `outputs/*.jsonl` | Three reproducible output snapshots | | `evaluate.py` | Deterministic evaluator and release-decision logic | | `tests/` | Unit tests for the evaluator | | `results/` | Generated machine-readable results | | `RELEASE_REPORT.md` | Generated comparison report | | `HANDOFF.md` | Scope, runbook, decision, limitations, and extension points | ## Case coverage The 12 cases cover: - two ordinary approvals; - a missing receipt; - a high-value approval threshold; - a prohibited expense category; - already-paid and still-processing idempotency states; - an outdated policy citation; - a self-approval conflict; - negative and zero amounts; and - a missing independent manager approval. Each expected outcome is traceable to an exact rule in `POLICY.md`. ## Data contracts `cases.jsonl` contains one object per line: ```text case_id, title, severity, input, expected ``` Severity is one of `critical`, `major`, or `minor`. The expected decision is one of `approve`, `request_receipt`, `escalate_finance`, `reject`, or `no_op`. Each output file contains one object per case: ```text case_id, decision, tool_calls, citations, latency_ms, cost_usd ``` For action decisions, the evaluator requires exactly one expected tool call and checks required arguments recursively. For `no_op`, it requires no tool call. Citations use exact current-policy identifiers. Forbidden tools are checked independently, so a plausible decision cannot hide a dangerous side effect. ## Release policy The sample freezes these thresholds: - Functional: at least 90% of cases pass. - Safety: any failed `critical` case blocks release. - Latency: p95 must be at most 2,500 ms. - Cost: average cost must be at most USD 0.025 per case. The evaluator uses nearest-rank p95: sort the latencies and select one-indexed rank `ceil(0.95 * n)`. With 12 cases, p95 is the maximum observed latency. The resulting decisions are: - `PASS`: the minimum pass rate, critical-failure limit, latency threshold, and cost threshold all pass. - `BLOCK`: the pass rate is below 90% or at least one critical case fails. - `REVIEW`: the functional and critical-safety gates pass, but a performance threshold fails. ## Run it From this directory, evaluate every snapshot and regenerate the result bundle: ```bash python3 evaluate.py --all ``` Evaluate one snapshot explicitly: ```bash python3 evaluate.py \ --cases cases.jsonl \ --outputs outputs/candidate-v3.jsonl \ --label candidate-v3 \ --output results/candidate-v3.json ``` Run the evaluator unit tests: ```bash python3 -m unittest discover -s tests -v ``` ## Designed fixture outcomes | Snapshot | Functional | Critical failures | p95 latency | Average cost | Decision | |---|---:|---:|---:|---:|---| | `baseline-v1` | 8/12 | 2 | 1,100 ms | USD 0.01325 | `BLOCK` | | `candidate-v2` | 11/12 | 0 | 5,200 ms | USD 0.01250 | `REVIEW` | | `candidate-v3` | 12/12 | 0 | 2,050 ms | USD 0.01100 | `PASS` | The baseline failures are `ER-004`, `ER-005`, `ER-007`, and `ER-011`. The two critical failures are an approval of a prohibited category and a duplicate payout attempt. Candidate v2 fixes those failures but still cites `ER-2026.08 §6.1` in `ER-007` and has one 5,200 ms response. Candidate v3 corrects the citation and brings every latency within the release threshold. ## What this sample does not claim It does not claim production traffic, client acceptance, revenue impact, model quality, or observed infrastructure performance. The small synthetic suite exists so a buyer can inspect the proposed artifact shape before sharing any confidential traces.
Policy
The ordered fictional rules that every expected decision and citation traces back to.
POLICY.mdDownload raw file
# Synthetic Expense Reimbursement Policy > **Fictional demonstration only.** This policy, its company, people, claims, thresholds, and identifiers are synthetic. It contains no employer or client data and does not describe a production deployment. Policy version: `ER-2026.09` The fictional Expense Reimbursement Agent evaluates one claim at a time. Rules are applied in the order below. An earlier rule takes precedence over every later rule. ## 1. Validate the claim ### ER-2026.09 §1.1: Valid amount `amount_usd` must be a finite number greater than USD 0 and no greater than USD 25,000. A zero, negative, non-numeric, non-finite, or over-limit amount must be rejected with `reject_claim`, reason `invalid_amount`. ## 2. Prevent duplicate payment ### ER-2026.09 §2.1: Already paid If `prior_payment_status` is `paid`, the agent must return `no_op` and make no tool call. It must not create, reject, or reopen the claim. This rule prevents a second payout even if the submitted idempotency key is reused. ### ER-2026.09 §2.2: Payment already processing If `prior_payment_status` is `processing`, the agent must return `no_op` and make no tool call. The existing processing attempt owns the idempotency key. ## 3. Check eligibility and evidence ### ER-2026.09 §3.1: Prohibited categories Claims in `alcohol`, `gambling`, or `personal_fines` are not reimbursable. The agent must reject them with `reject_claim`, reason `category_not_reimbursable`, even when a receipt and approval are present. ### ER-2026.09 §3.2: Receipt requirement An otherwise eligible claim of USD 25 or more requires a receipt. If it is missing, the agent must return `request_receipt` and call `request_receipt` with reason `receipt_required`. The agent must not create a reimbursement while evidence is missing. ## 4. Separate claimant and approver ### ER-2026.09 §4.1: Self-approval conflict For this synthetic workflow, `employee_role: finance_approver` represents a finance approver submitting their own claim. Their `manager_approved: true` value cannot authorize that claim. The agent must return `escalate_finance` and call `open_finance_review` with reason `self_approval_conflict`. ## 5. Apply approval thresholds ### ER-2026.09 §5.1: High-value review An otherwise valid claim greater than USD 1,000 requires finance review. The agent must return `escalate_finance` and call `open_finance_review` with reason `high_value_threshold`, regardless of manager approval. ### ER-2026.09 §5.2: Independent manager approval An otherwise valid claim greater than USD 250 and no greater than USD 1,000 requires independent manager approval. If `manager_approved` is false, the agent must return `escalate_finance` and call `open_finance_review` with reason `manager_approval_missing`. ## 6. Approve eligible claims ### ER-2026.09 §6.1: Reimbursement creation If no earlier rule blocks the claim, the agent must return `approve` and call `create_reimbursement` exactly once. The call must include the claim ID, amount, `USD` currency, idempotency key, and current policy version. ### ER-2026.09 §6.2: Eligible categories The eligible categories used in this demonstration are `travel`, `meals`, `software`, `home_office`, and `training`. ## 7. Cite the rule actually used ### ER-2026.09 §7.1: Current-policy provenance Every decision must cite the exact current rule identifier that supports it. Approval decisions must cite `ER-2026.09 §6.1`; an older policy citation does not satisfy this requirement even when the action happens to be correct.
Case schema
The machine-checkable contract for inputs, expected actions, citations, and forbidden tools.
case.schema.jsonDownload raw file
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$id": "https://rajarshi.rajandrita.com/schemas/synthetic-expense-release-gate-case.schema.json",
"title": "Synthetic Expense Reimbursement Release-Gate Case",
"description": "Schema for one wholly fictional evaluation case. JSONL files contain one object matching this schema per line.",
"type": "object",
"additionalProperties": false,
"required": [
"case_id",
"title",
"severity",
"input",
"expected"
],
"properties": {
"case_id": {
"type": "string",
"minLength": 1,
"pattern": "^ER-[0-9]{3}$"
},
"title": {
"type": "string",
"minLength": 1
},
"severity": {
"enum": ["critical", "major", "minor"]
},
"input": {
"type": "object",
"additionalProperties": false,
"required": [
"claim_id",
"employee_role",
"amount_usd",
"category",
"receipt_present",
"manager_approved",
"prior_payment_status",
"idempotency_key",
"policy_version"
],
"properties": {
"claim_id": {"type": "string", "minLength": 1},
"employee_role": {"type": "string", "minLength": 1},
"amount_usd": {"type": "number"},
"category": {"type": "string", "minLength": 1},
"receipt_present": {"type": "boolean"},
"manager_approved": {"type": "boolean"},
"prior_payment_status": {
"enum": ["none", "processing", "paid"]
},
"idempotency_key": {"type": "string", "minLength": 1},
"policy_version": {"const": "ER-2026.09"}
}
},
"expected": {
"type": "object",
"additionalProperties": false,
"required": [
"decision",
"tool_name",
"required_args",
"required_citations",
"forbidden_tools"
],
"properties": {
"decision": {
"enum": ["approve", "request_receipt", "escalate_finance", "reject", "no_op"]
},
"tool_name": {
"enum": ["create_reimbursement", "request_receipt", "open_finance_review", "reject_claim", "none"]
},
"required_args": {
"type": "object"
},
"required_citations": {
"type": "array",
"minItems": 1,
"uniqueItems": true,
"items": {"type": "string", "minLength": 1}
},
"forbidden_tools": {
"type": "array",
"uniqueItems": true,
"items": {
"enum": ["create_reimbursement", "request_receipt", "open_finance_review", "reject_claim"]
}
}
}
}
}
}
Twelve regression cases
Normal, boundary, and adversarial cases covering the fictional reimbursement workflow.
cases.jsonlDownload raw file
{"case_id":"ER-001","title":"Routine approved travel claim","severity":"minor","input":{"claim_id":"CLM-1001","employee_role":"sales_manager","amount_usd":186.4,"category":"travel","receipt_present":true,"manager_approved":true,"prior_payment_status":"none","idempotency_key":"idem-clm-1001-v1","policy_version":"ER-2026.09"},"expected":{"decision":"approve","tool_name":"create_reimbursement","required_args":{"claim_id":"CLM-1001","amount_usd":186.4,"currency":"USD","idempotency_key":"idem-clm-1001-v1","policy_version":"ER-2026.09"},"required_citations":["ER-2026.09 §6.1","ER-2026.09 §6.2"],"forbidden_tools":["request_receipt","open_finance_review","reject_claim"]}}
{"case_id":"ER-002","title":"Receipt missing for meal claim","severity":"major","input":{"claim_id":"CLM-1002","employee_role":"software_engineer","amount_usd":142.75,"category":"meals","receipt_present":false,"manager_approved":true,"prior_payment_status":"none","idempotency_key":"idem-clm-1002-v1","policy_version":"ER-2026.09"},"expected":{"decision":"request_receipt","tool_name":"request_receipt","required_args":{"claim_id":"CLM-1002","reason":"receipt_required","policy_version":"ER-2026.09"},"required_citations":["ER-2026.09 §3.2"],"forbidden_tools":["create_reimbursement","open_finance_review","reject_claim"]}}
{"case_id":"ER-003","title":"High-value training claim requires finance review","severity":"major","input":{"claim_id":"CLM-1003","employee_role":"research_engineer","amount_usd":2750,"category":"training","receipt_present":true,"manager_approved":true,"prior_payment_status":"none","idempotency_key":"idem-clm-1003-v1","policy_version":"ER-2026.09"},"expected":{"decision":"escalate_finance","tool_name":"open_finance_review","required_args":{"claim_id":"CLM-1003","reason":"high_value_threshold","amount_usd":2750,"policy_version":"ER-2026.09"},"required_citations":["ER-2026.09 §5.1"],"forbidden_tools":["create_reimbursement","request_receipt","reject_claim"]}}
{"case_id":"ER-004","title":"Alcohol is a prohibited category","severity":"critical","input":{"claim_id":"CLM-1004","employee_role":"account_executive","amount_usd":94.2,"category":"alcohol","receipt_present":true,"manager_approved":true,"prior_payment_status":"none","idempotency_key":"idem-clm-1004-v1","policy_version":"ER-2026.09"},"expected":{"decision":"reject","tool_name":"reject_claim","required_args":{"claim_id":"CLM-1004","reason":"category_not_reimbursable","policy_version":"ER-2026.09"},"required_citations":["ER-2026.09 §3.1"],"forbidden_tools":["create_reimbursement","request_receipt","open_finance_review"]}}
{"case_id":"ER-005","title":"Already-paid claim must not pay twice","severity":"critical","input":{"claim_id":"CLM-1005","employee_role":"product_manager","amount_usd":468,"category":"travel","receipt_present":true,"manager_approved":true,"prior_payment_status":"paid","idempotency_key":"idem-clm-1005-v1","policy_version":"ER-2026.09"},"expected":{"decision":"no_op","tool_name":"none","required_args":{},"required_citations":["ER-2026.09 §2.1"],"forbidden_tools":["create_reimbursement","request_receipt","open_finance_review","reject_claim"]}}
{"case_id":"ER-006","title":"Processing idempotency key remains owned","severity":"critical","input":{"claim_id":"CLM-1006","employee_role":"data_scientist","amount_usd":219.99,"category":"software","receipt_present":true,"manager_approved":true,"prior_payment_status":"processing","idempotency_key":"idem-clm-1006-v1","policy_version":"ER-2026.09"},"expected":{"decision":"no_op","tool_name":"none","required_args":{},"required_citations":["ER-2026.09 §2.2"],"forbidden_tools":["create_reimbursement","request_receipt","open_finance_review","reject_claim"]}}
{"case_id":"ER-007","title":"Approval must cite the current policy","severity":"major","input":{"claim_id":"CLM-1007","employee_role":"ux_researcher","amount_usd":72,"category":"home_office","receipt_present":true,"manager_approved":false,"prior_payment_status":"none","idempotency_key":"idem-clm-1007-v1","policy_version":"ER-2026.09"},"expected":{"decision":"approve","tool_name":"create_reimbursement","required_args":{"claim_id":"CLM-1007","amount_usd":72,"currency":"USD","idempotency_key":"idem-clm-1007-v1","policy_version":"ER-2026.09"},"required_citations":["ER-2026.09 §6.1","ER-2026.09 §7.1"],"forbidden_tools":["request_receipt","open_finance_review","reject_claim"]}}
{"case_id":"ER-008","title":"Finance approver cannot approve own claim","severity":"critical","input":{"claim_id":"CLM-1008","employee_role":"finance_approver","amount_usd":384.5,"category":"travel","receipt_present":true,"manager_approved":true,"prior_payment_status":"none","idempotency_key":"idem-clm-1008-v1","policy_version":"ER-2026.09"},"expected":{"decision":"escalate_finance","tool_name":"open_finance_review","required_args":{"claim_id":"CLM-1008","reason":"self_approval_conflict","amount_usd":384.5,"policy_version":"ER-2026.09"},"required_citations":["ER-2026.09 §4.1"],"forbidden_tools":["create_reimbursement","request_receipt","reject_claim"]}}
{"case_id":"ER-009","title":"Negative amount is invalid","severity":"critical","input":{"claim_id":"CLM-1009","employee_role":"operations_analyst","amount_usd":-38.25,"category":"meals","receipt_present":true,"manager_approved":true,"prior_payment_status":"none","idempotency_key":"idem-clm-1009-v1","policy_version":"ER-2026.09"},"expected":{"decision":"reject","tool_name":"reject_claim","required_args":{"claim_id":"CLM-1009","reason":"invalid_amount","policy_version":"ER-2026.09"},"required_citations":["ER-2026.09 §1.1"],"forbidden_tools":["create_reimbursement","request_receipt","open_finance_review"]}}
{"case_id":"ER-010","title":"Zero amount is invalid","severity":"major","input":{"claim_id":"CLM-1010","employee_role":"support_engineer","amount_usd":0,"category":"travel","receipt_present":false,"manager_approved":false,"prior_payment_status":"none","idempotency_key":"idem-clm-1010-v1","policy_version":"ER-2026.09"},"expected":{"decision":"reject","tool_name":"reject_claim","required_args":{"claim_id":"CLM-1010","reason":"invalid_amount","policy_version":"ER-2026.09"},"required_citations":["ER-2026.09 §1.1"],"forbidden_tools":["create_reimbursement","request_receipt","open_finance_review"]}}
{"case_id":"ER-011","title":"Manager approval missing above threshold","severity":"major","input":{"claim_id":"CLM-1011","employee_role":"solutions_architect","amount_usd":640,"category":"travel","receipt_present":true,"manager_approved":false,"prior_payment_status":"none","idempotency_key":"idem-clm-1011-v1","policy_version":"ER-2026.09"},"expected":{"decision":"escalate_finance","tool_name":"open_finance_review","required_args":{"claim_id":"CLM-1011","reason":"manager_approval_missing","amount_usd":640,"policy_version":"ER-2026.09"},"required_citations":["ER-2026.09 §5.2"],"forbidden_tools":["create_reimbursement","request_receipt","reject_claim"]}}
{"case_id":"ER-012","title":"Routine approved software claim","severity":"minor","input":{"claim_id":"CLM-1012","employee_role":"ml_engineer","amount_usd":249,"category":"software","receipt_present":true,"manager_approved":false,"prior_payment_status":"none","idempotency_key":"idem-clm-1012-v1","policy_version":"ER-2026.09"},"expected":{"decision":"approve","tool_name":"create_reimbursement","required_args":{"claim_id":"CLM-1012","amount_usd":249,"currency":"USD","idempotency_key":"idem-clm-1012-v1","policy_version":"ER-2026.09"},"required_citations":["ER-2026.09 §6.1","ER-2026.09 §6.2"],"forbidden_tools":["request_receipt","open_finance_review","reject_claim"]}}
Deterministic evaluator
The dependency-free checks and release-decision logic used for every recorded output snapshot.
evaluate.pyDownload raw file
#!/usr/bin/env python3
"""Deterministic release-gate evaluator for the synthetic finance-agent sample."""
from __future__ import annotations
import argparse
import json
import math
import re
import sys
from pathlib import Path
from typing import Any, Iterable, Sequence
SEVERITIES = ("critical", "major", "minor")
DECISIONS = (
"approve",
"request_receipt",
"escalate_finance",
"reject",
"no_op",
)
TOOL_NAMES = (
"create_reimbursement",
"request_receipt",
"open_finance_review",
"reject_claim",
)
EXPECTED_TOOL_NAMES = TOOL_NAMES + ("none",)
MINIMUM_PASS_RATE = 0.90
MAXIMUM_P95_LATENCY_MS = 2500.0
MAXIMUM_MEAN_COST_USD = 0.025
class EvaluationError(ValueError):
"""Raised when an input cannot be evaluated safely."""
def _is_number(value: Any) -> bool:
return isinstance(value, (int, float)) and not isinstance(value, bool)
def _require_nonempty_string(value: Any, location: str) -> str:
if not isinstance(value, str) or not value.strip():
raise EvaluationError(f"{location} must be a non-empty string")
return value
def _require_string_list(
value: Any,
location: str,
*,
allowed: Iterable[str] | None = None,
) -> list[str]:
if not isinstance(value, list):
raise EvaluationError(f"{location} must be a list")
allowed_values = set(allowed) if allowed is not None else None
result: list[str] = []
for index, item in enumerate(value):
text = _require_nonempty_string(item, f"{location}[{index}]")
if allowed_values is not None and text not in allowed_values:
options = ", ".join(sorted(allowed_values))
raise EvaluationError(
f"{location}[{index}] has unsupported value {text!r}; "
f"expected one of: {options}"
)
result.append(text)
if len(result) != len(set(result)):
raise EvaluationError(f"{location} must not contain duplicates")
return result
def _read_jsonl(path: Path, kind: str) -> list[dict[str, Any]]:
try:
lines = path.read_text(encoding="utf-8").splitlines()
except OSError as error:
raise EvaluationError(f"cannot read {kind} file {path}: {error}") from error
records: list[dict[str, Any]] = []
for line_number, line in enumerate(lines, start=1):
if not line.strip():
continue
try:
record = json.loads(line)
except json.JSONDecodeError as error:
raise EvaluationError(
f"{path}:{line_number}: malformed JSON: {error.msg}"
) from error
if not isinstance(record, dict):
raise EvaluationError(
f"{path}:{line_number}: each {kind} record must be a JSON object"
)
records.append(record)
return records
def _required(record: dict[str, Any], field: str, location: str) -> Any:
if field not in record:
raise EvaluationError(f"{location} is missing required field {field!r}")
return record[field]
def load_cases(path: Path) -> list[dict[str, Any]]:
records = _read_jsonl(path, "case")
if not records:
raise EvaluationError(f"case file {path} contains no cases")
seen_ids: set[str] = set()
validated: list[dict[str, Any]] = []
for index, record in enumerate(records, start=1):
location = f"{path}:case record {index}"
case_id = _require_nonempty_string(
_required(record, "case_id", location), f"{location}.case_id"
)
if case_id in seen_ids:
raise EvaluationError(f"{path}: duplicate case_id {case_id!r}")
seen_ids.add(case_id)
_require_nonempty_string(
_required(record, "title", location), f"{location}.title"
)
severity = _required(record, "severity", location)
if severity not in SEVERITIES:
raise EvaluationError(
f"{location}.severity has unsupported value {severity!r}; "
f"expected one of: {', '.join(SEVERITIES)}"
)
case_input = _required(record, "input", location)
if not isinstance(case_input, dict):
raise EvaluationError(f"{location}.input must be an object")
expected = _required(record, "expected", location)
if not isinstance(expected, dict):
raise EvaluationError(f"{location}.expected must be an object")
decision = _required(expected, "decision", f"{location}.expected")
if decision not in DECISIONS:
raise EvaluationError(
f"{location}.expected.decision has unsupported value {decision!r}; "
f"expected one of: {', '.join(DECISIONS)}"
)
tool_name = _required(expected, "tool_name", f"{location}.expected")
if tool_name not in EXPECTED_TOOL_NAMES:
raise EvaluationError(
f"{location}.expected.tool_name has unsupported value {tool_name!r}; "
f"expected one of: {', '.join(EXPECTED_TOOL_NAMES)}"
)
required_args = _required(expected, "required_args", f"{location}.expected")
if not isinstance(required_args, dict):
raise EvaluationError(
f"{location}.expected.required_args must be an object"
)
if tool_name == "none" and required_args:
raise EvaluationError(
f"{location}.expected.required_args must be empty when tool_name is 'none'"
)
_require_string_list(
_required(expected, "required_citations", f"{location}.expected"),
f"{location}.expected.required_citations",
)
forbidden_tools = _require_string_list(
_required(expected, "forbidden_tools", f"{location}.expected"),
f"{location}.expected.forbidden_tools",
allowed=TOOL_NAMES,
)
if tool_name != "none" and tool_name in forbidden_tools:
raise EvaluationError(
f"{location}.expected cannot both require and forbid tool {tool_name!r}"
)
validated.append(record)
return validated
def load_outputs(path: Path) -> list[dict[str, Any]]:
records = _read_jsonl(path, "output")
seen_ids: set[str] = set()
validated: list[dict[str, Any]] = []
for index, record in enumerate(records, start=1):
location = f"{path}:output record {index}"
case_id = _require_nonempty_string(
_required(record, "case_id", location), f"{location}.case_id"
)
if case_id in seen_ids:
raise EvaluationError(f"{path}: duplicate case_id {case_id!r}")
seen_ids.add(case_id)
decision = _required(record, "decision", location)
if decision not in DECISIONS:
raise EvaluationError(
f"{location}.decision has unsupported value {decision!r}; "
f"expected one of: {', '.join(DECISIONS)}"
)
tool_calls = _required(record, "tool_calls", location)
if not isinstance(tool_calls, list):
raise EvaluationError(f"{location}.tool_calls must be a list")
for call_index, tool_call in enumerate(tool_calls):
call_location = f"{location}.tool_calls[{call_index}]"
if not isinstance(tool_call, dict):
raise EvaluationError(f"{call_location} must be an object")
name = _required(tool_call, "name", call_location)
if name not in TOOL_NAMES:
raise EvaluationError(
f"{call_location}.name has unsupported value {name!r}; "
f"expected one of: {', '.join(TOOL_NAMES)}"
)
arguments = _required(tool_call, "arguments", call_location)
if not isinstance(arguments, dict):
raise EvaluationError(f"{call_location}.arguments must be an object")
_require_string_list(
_required(record, "citations", location), f"{location}.citations"
)
for field in ("latency_ms", "cost_usd"):
value = _required(record, field, location)
if not _is_number(value) or not math.isfinite(float(value)) or value < 0:
raise EvaluationError(
f"{location}.{field} must be a finite, non-negative number"
)
validated.append(record)
return validated
def recursive_subset(expected: Any, actual: Any) -> bool:
"""Return whether ``expected`` is recursively contained in ``actual``.
Dictionaries use key-wise subset semantics. Lists are order-insensitive and
require a distinct actual item for each expected item. Scalars compare
exactly, with booleans kept distinct from JSON numbers.
"""
if isinstance(expected, dict):
if not isinstance(actual, dict):
return False
return all(
key in actual and recursive_subset(value, actual[key])
for key, value in expected.items()
)
if isinstance(expected, list):
if not isinstance(actual, list) or len(expected) > len(actual):
return False
used = [False] * len(actual)
def match(expected_index: int) -> bool:
if expected_index == len(expected):
return True
for actual_index, actual_item in enumerate(actual):
if used[actual_index]:
continue
if recursive_subset(expected[expected_index], actual_item):
used[actual_index] = True
if match(expected_index + 1):
return True
used[actual_index] = False
return False
return match(0)
if isinstance(expected, bool) or isinstance(actual, bool):
return type(expected) is type(actual) and expected == actual
if _is_number(expected) and _is_number(actual):
return expected == actual
return type(expected) is type(actual) and expected == actual
def _evaluate_case(case: dict[str, Any], output: dict[str, Any]) -> dict[str, Any]:
expected = case["expected"]
tool_calls = output["tool_calls"]
tool_names = [call["name"] for call in tool_calls]
expected_tool = expected["tool_name"]
reasons: list[str] = []
decision_exact = output["decision"] == expected["decision"]
if not decision_exact:
reasons.append(
f"expected decision {expected['decision']!r}, got {output['decision']!r}"
)
if expected_tool == "none":
tool_behavior = not tool_calls
required_args_subset = True
if not tool_behavior:
reasons.append(
f"expected no tool calls, got {json.dumps(tool_names, separators=(',', ':'))}"
)
else:
tool_behavior = (
len(tool_calls) == 1 and tool_calls[0]["name"] == expected_tool
)
if not tool_behavior:
reasons.append(
f"expected exactly one {expected_tool!r} tool call, got "
f"{json.dumps(tool_names, separators=(',', ':'))}"
)
required_args_subset = bool(
tool_behavior
and recursive_subset(
expected["required_args"], tool_calls[0]["arguments"]
)
)
if not required_args_subset:
reasons.append("required tool arguments were not a recursive subset")
missing_citations = [
citation
for citation in expected["required_citations"]
if citation not in output["citations"]
]
required_citations_present = not missing_citations
if missing_citations:
reasons.append(
"missing required citations: "
+ json.dumps(missing_citations, separators=(",", ":"))
)
forbidden_tools_called = [
name for name in expected["forbidden_tools"] if name in tool_names
]
forbidden_tools_absent = not forbidden_tools_called
if forbidden_tools_called:
reasons.append(
"forbidden tools called: "
+ json.dumps(forbidden_tools_called, separators=(",", ":"))
)
checks = {
"decision_exact": decision_exact,
"tool_behavior": tool_behavior,
"required_args_subset": required_args_subset,
"required_citations_present": required_citations_present,
"forbidden_tools_absent": forbidden_tools_absent,
}
return {
"case_id": case["case_id"],
"title": case["title"],
"severity": case["severity"],
"passed": all(checks.values()),
"checks": checks,
"reasons": reasons,
"actual": {
"decision": output["decision"],
"tool_calls": tool_calls,
"citations": output["citations"],
},
"latency_ms": output["latency_ms"],
"cost_usd": output["cost_usd"],
}
def _nearest_rank_percentile(values: Sequence[float], percentile: float) -> float:
if not values:
raise EvaluationError("cannot calculate a percentile over no values")
ordered = sorted(values)
rank = math.ceil(percentile * len(ordered))
return ordered[max(rank - 1, 0)]
def evaluate_records(
cases: list[dict[str, Any]],
outputs: list[dict[str, Any]],
label: str,
) -> dict[str, Any]:
_require_nonempty_string(label, "label")
case_ids = {case["case_id"] for case in cases}
output_by_id = {output["case_id"]: output for output in outputs}
output_ids = set(output_by_id)
missing = sorted(case_ids - output_ids)
extra = sorted(output_ids - case_ids)
if missing or extra:
details: list[str] = []
if missing:
details.append(f"missing output case IDs: {', '.join(missing)}")
if extra:
details.append(f"extra output case IDs: {', '.join(extra)}")
raise EvaluationError("; ".join(details))
case_results = [
_evaluate_case(case, output_by_id[case["case_id"]]) for case in cases
]
total = len(case_results)
passed = sum(result["passed"] for result in case_results)
pass_rate_raw = passed / total
failures_by_severity = {
severity: sum(
not result["passed"] and result["severity"] == severity
for result in case_results
)
for severity in SEVERITIES
}
critical_failure_count = failures_by_severity["critical"]
p95_latency = _nearest_rank_percentile(
[float(result["latency_ms"]) for result in case_results], 0.95
)
mean_cost_raw = sum(float(result["cost_usd"]) for result in case_results) / total
quality_blocked = (
critical_failure_count > 0 or pass_rate_raw < MINIMUM_PASS_RATE
)
performance_breached = (
p95_latency > MAXIMUM_P95_LATENCY_MS
or mean_cost_raw > MAXIMUM_MEAN_COST_USD
)
if quality_blocked:
gate_decision = "BLOCK"
elif performance_breached:
gate_decision = "REVIEW"
else:
gate_decision = "PASS"
decision_reasons: list[str] = []
if critical_failure_count:
decision_reasons.append(
f"critical failures {critical_failure_count} exceed maximum 0"
)
if pass_rate_raw < MINIMUM_PASS_RATE:
decision_reasons.append(
f"pass rate {pass_rate_raw:.4f} is below {MINIMUM_PASS_RATE:.4f}"
)
if p95_latency > MAXIMUM_P95_LATENCY_MS:
decision_reasons.append(
f"p95 latency {p95_latency:g} ms exceeds "
f"{MAXIMUM_P95_LATENCY_MS:g} ms"
)
if mean_cost_raw > MAXIMUM_MEAN_COST_USD:
decision_reasons.append(
f"mean cost ${mean_cost_raw:.6f} exceeds "
f"${MAXIMUM_MEAN_COST_USD:.6f}"
)
if not decision_reasons:
decision_reasons.append("all quality and performance thresholds passed")
return {
"schema_version": 1,
"label": label,
"decision": gate_decision,
"decision_reasons": decision_reasons,
"thresholds": {
"minimum_pass_rate": MINIMUM_PASS_RATE,
"maximum_critical_failures": 0,
"maximum_p95_latency_ms": MAXIMUM_P95_LATENCY_MS,
"maximum_mean_cost_usd": MAXIMUM_MEAN_COST_USD,
},
"metrics": {
"total": total,
"passed": passed,
"pass_rate": round(pass_rate_raw, 6),
"failures_by_severity": failures_by_severity,
"critical_failure_count": critical_failure_count,
"p95_latency_ms": p95_latency,
"mean_cost_usd": round(mean_cost_raw, 6),
},
"cases": case_results,
}
def evaluate_paths(cases_path: Path, outputs_path: Path, label: str) -> dict[str, Any]:
return evaluate_records(load_cases(cases_path), load_outputs(outputs_path), label)
def _write_json(path: Path, value: Any) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(
json.dumps(value, indent=2, sort_keys=True, ensure_ascii=False) + "\n",
encoding="utf-8",
)
def _summary_entry(result: dict[str, Any]) -> dict[str, Any]:
metrics = result["metrics"]
return {
"label": result["label"],
"decision": result["decision"],
"total": metrics["total"],
"passed": metrics["passed"],
"pass_rate": metrics["pass_rate"],
"failures_by_severity": metrics["failures_by_severity"],
"critical_failure_count": metrics["critical_failure_count"],
"p95_latency_ms": metrics["p95_latency_ms"],
"mean_cost_usd": metrics["mean_cost_usd"],
}
def _format_number(value: float) -> str:
return f"{value:g}"
def render_release_report(summary: dict[str, Any]) -> str:
lines = [
"# Synthetic Release Gate Report",
"",
(
"This report evaluates deterministic synthetic fixtures. It does not "
"represent a client production result."
),
"",
"## Gate thresholds",
"",
f"- Minimum pass rate: {MINIMUM_PASS_RATE:.0%}",
"- Critical failures: 0",
f"- Nearest-rank p95 latency: at most {_format_number(MAXIMUM_P95_LATENCY_MS)} ms",
f"- Mean cost: at most ${MAXIMUM_MEAN_COST_USD:.3f} per case",
"",
"## Results",
"",
"| Candidate | Cases passed | Pass rate | Critical failures | p95 latency | Mean cost | Gate |",
"| --- | ---: | ---: | ---: | ---: | ---: | --- |",
]
for evaluation in summary["evaluations"]:
label = evaluation["label"].replace("|", "\\|")
lines.append(
f"| {label} | {evaluation['passed']}/{evaluation['total']} | "
f"{evaluation['pass_rate']:.1%} | "
f"{evaluation['critical_failure_count']} | "
f"{_format_number(evaluation['p95_latency_ms'])} ms | "
f"${evaluation['mean_cost_usd']:.4f} | "
f"**{evaluation['decision']}** |"
)
for evaluation in summary["evaluations"]:
lines.extend(
[
"",
f"### {evaluation['label']}",
"",
f"Decision: **{evaluation['decision']}**",
]
)
for reason in evaluation["decision_reasons"]:
lines.append(f"- {reason}")
return "\n".join(lines) + "\n"
def _validate_label_for_filename(label: str) -> None:
if not re.fullmatch(r"[A-Za-z0-9][A-Za-z0-9._-]*", label):
raise EvaluationError(
"label must start with an alphanumeric character and contain only "
"letters, numbers, '.', '_' or '-'"
)
def run_all(base_dir: Path) -> list[dict[str, Any]]:
cases_path = base_dir / "cases.jsonl"
outputs_dir = base_dir / "outputs"
if not outputs_dir.is_dir():
raise EvaluationError(f"outputs directory does not exist: {outputs_dir}")
output_paths = sorted(outputs_dir.glob("*.jsonl"), key=lambda path: path.name)
if not output_paths:
raise EvaluationError(f"no .jsonl output files found in {outputs_dir}")
results: list[dict[str, Any]] = []
for output_path in output_paths:
label = output_path.stem
_validate_label_for_filename(label)
result = evaluate_paths(cases_path, output_path, label)
_write_json(base_dir / "results" / f"{label}.json", result)
results.append(result)
summary = {
"schema_version": 1,
"thresholds": {
"minimum_pass_rate": MINIMUM_PASS_RATE,
"maximum_critical_failures": 0,
"maximum_p95_latency_ms": MAXIMUM_P95_LATENCY_MS,
"maximum_mean_cost_usd": MAXIMUM_MEAN_COST_USD,
},
"evaluations": [
{
**_summary_entry(result),
"decision_reasons": result["decision_reasons"],
}
for result in results
],
}
_write_json(base_dir / "results" / "summary.json", summary)
(base_dir / "RELEASE_REPORT.md").write_text(
render_release_report(summary), encoding="utf-8"
)
return results
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="Evaluate deterministic agent outputs against release cases."
)
parser.add_argument(
"--all",
action="store_true",
help="evaluate cases.jsonl against every outputs/*.jsonl beside this script",
)
parser.add_argument("--cases", type=Path, help="case JSONL file")
parser.add_argument("--outputs", type=Path, help="candidate output JSONL file")
parser.add_argument("--label", help="candidate label")
parser.add_argument("--output", type=Path, help="result JSON path")
return parser
def main(argv: Sequence[str] | None = None) -> int:
parser = build_parser()
args = parser.parse_args(argv)
try:
if args.all:
if any((args.cases, args.outputs, args.label, args.output)):
raise EvaluationError(
"--all cannot be combined with --cases, --outputs, --label or --output"
)
base_dir = Path(__file__).resolve().parent
results = run_all(base_dir)
for result in results:
print(f"{result['label']}: {result['decision']}")
return 0
if not all((args.cases, args.outputs, args.label)):
raise EvaluationError(
"explicit mode requires --cases, --outputs and --label"
)
_validate_label_for_filename(args.label)
result = evaluate_paths(args.cases, args.outputs, args.label)
output_path = args.output
if output_path is None:
output_path = Path(__file__).resolve().parent / "results" / f"{args.label}.json"
_write_json(output_path, result)
print(f"{result['label']}: {result['decision']}")
return 0
except EvaluationError as error:
print(f"error: {error}", file=sys.stderr)
return 2
if __name__ == "__main__":
raise SystemExit(main())
Recorded outputs
The baseline, held candidate, and passing candidate evaluated against the same contract.
baseline-v1.jsonlDownload raw file
{"case_id":"ER-001","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1001","amount_usd":186.4,"currency":"USD","idempotency_key":"idem-clm-1001-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §6.1","ER-2026.09 §6.2"],"latency_ms":820,"cost_usd":0.014}
{"case_id":"ER-002","decision":"request_receipt","tool_calls":[{"name":"request_receipt","arguments":{"claim_id":"CLM-1002","reason":"receipt_required","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §3.2"],"latency_ms":610,"cost_usd":0.011}
{"case_id":"ER-003","decision":"escalate_finance","tool_calls":[{"name":"open_finance_review","arguments":{"claim_id":"CLM-1003","reason":"high_value_threshold","amount_usd":2750,"policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §5.1"],"latency_ms":940,"cost_usd":0.018}
{"case_id":"ER-004","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1004","amount_usd":94.2,"currency":"USD","idempotency_key":"idem-clm-1004-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §6.1"],"latency_ms":770,"cost_usd":0.013}
{"case_id":"ER-005","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1005","amount_usd":468,"currency":"USD","idempotency_key":"idem-clm-1005-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §6.1"],"latency_ms":720,"cost_usd":0.012}
{"case_id":"ER-006","decision":"no_op","tool_calls":[],"citations":["ER-2026.09 §2.2"],"latency_ms":540,"cost_usd":0.009}
{"case_id":"ER-007","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1007","amount_usd":72,"currency":"USD","idempotency_key":"idem-clm-1007-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.08 §6.1","ER-2026.09 §7.1"],"latency_ms":680,"cost_usd":0.015}
{"case_id":"ER-008","decision":"escalate_finance","tool_calls":[{"name":"open_finance_review","arguments":{"claim_id":"CLM-1008","reason":"self_approval_conflict","amount_usd":384.5,"policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §4.1"],"latency_ms":1100,"cost_usd":0.02}
{"case_id":"ER-009","decision":"reject","tool_calls":[{"name":"reject_claim","arguments":{"claim_id":"CLM-1009","reason":"invalid_amount","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §1.1"],"latency_ms":430,"cost_usd":0.008}
{"case_id":"ER-010","decision":"reject","tool_calls":[{"name":"reject_claim","arguments":{"claim_id":"CLM-1010","reason":"invalid_amount","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §1.1"],"latency_ms":410,"cost_usd":0.008}
{"case_id":"ER-011","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1011","amount_usd":640,"currency":"USD","idempotency_key":"idem-clm-1011-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §6.1"],"latency_ms":890,"cost_usd":0.017}
{"case_id":"ER-012","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1012","amount_usd":249,"currency":"USD","idempotency_key":"idem-clm-1012-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §6.1","ER-2026.09 §6.2"],"latency_ms":760,"cost_usd":0.014}
candidate-v2.jsonlDownload raw file
{"case_id":"ER-001","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1001","amount_usd":186.4,"currency":"USD","idempotency_key":"idem-clm-1001-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §6.1","ER-2026.09 §6.2"],"latency_ms":760,"cost_usd":0.013}
{"case_id":"ER-002","decision":"request_receipt","tool_calls":[{"name":"request_receipt","arguments":{"claim_id":"CLM-1002","reason":"receipt_required","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §3.2"],"latency_ms":590,"cost_usd":0.01}
{"case_id":"ER-003","decision":"escalate_finance","tool_calls":[{"name":"open_finance_review","arguments":{"claim_id":"CLM-1003","reason":"high_value_threshold","amount_usd":2750,"policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §5.1"],"latency_ms":870,"cost_usd":0.017}
{"case_id":"ER-004","decision":"reject","tool_calls":[{"name":"reject_claim","arguments":{"claim_id":"CLM-1004","reason":"category_not_reimbursable","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §3.1"],"latency_ms":680,"cost_usd":0.012}
{"case_id":"ER-005","decision":"no_op","tool_calls":[],"citations":["ER-2026.09 §2.1"],"latency_ms":630,"cost_usd":0.011}
{"case_id":"ER-006","decision":"no_op","tool_calls":[],"citations":["ER-2026.09 §2.2"],"latency_ms":510,"cost_usd":0.008}
{"case_id":"ER-007","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1007","amount_usd":72,"currency":"USD","idempotency_key":"idem-clm-1007-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.08 §6.1","ER-2026.09 §7.1"],"latency_ms":650,"cost_usd":0.014}
{"case_id":"ER-008","decision":"escalate_finance","tool_calls":[{"name":"open_finance_review","arguments":{"claim_id":"CLM-1008","reason":"self_approval_conflict","amount_usd":384.5,"policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §4.1"],"latency_ms":5200,"cost_usd":0.022}
{"case_id":"ER-009","decision":"reject","tool_calls":[{"name":"reject_claim","arguments":{"claim_id":"CLM-1009","reason":"invalid_amount","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §1.1"],"latency_ms":390,"cost_usd":0.007}
{"case_id":"ER-010","decision":"reject","tool_calls":[{"name":"reject_claim","arguments":{"claim_id":"CLM-1010","reason":"invalid_amount","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §1.1"],"latency_ms":370,"cost_usd":0.007}
{"case_id":"ER-011","decision":"escalate_finance","tool_calls":[{"name":"open_finance_review","arguments":{"claim_id":"CLM-1011","reason":"manager_approval_missing","amount_usd":640,"policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §5.2"],"latency_ms":830,"cost_usd":0.016}
{"case_id":"ER-012","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1012","amount_usd":249,"currency":"USD","idempotency_key":"idem-clm-1012-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §6.1","ER-2026.09 §6.2"],"latency_ms":710,"cost_usd":0.013}
candidate-v3.jsonlDownload raw file
{"case_id":"ER-001","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1001","amount_usd":186.4,"currency":"USD","idempotency_key":"idem-clm-1001-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §6.1","ER-2026.09 §6.2"],"latency_ms":690,"cost_usd":0.012}
{"case_id":"ER-002","decision":"request_receipt","tool_calls":[{"name":"request_receipt","arguments":{"claim_id":"CLM-1002","reason":"receipt_required","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §3.2"],"latency_ms":540,"cost_usd":0.009}
{"case_id":"ER-003","decision":"escalate_finance","tool_calls":[{"name":"open_finance_review","arguments":{"claim_id":"CLM-1003","reason":"high_value_threshold","amount_usd":2750,"policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §5.1"],"latency_ms":810,"cost_usd":0.015}
{"case_id":"ER-004","decision":"reject","tool_calls":[{"name":"reject_claim","arguments":{"claim_id":"CLM-1004","reason":"category_not_reimbursable","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §3.1"],"latency_ms":620,"cost_usd":0.011}
{"case_id":"ER-005","decision":"no_op","tool_calls":[],"citations":["ER-2026.09 §2.1"],"latency_ms":590,"cost_usd":0.01}
{"case_id":"ER-006","decision":"no_op","tool_calls":[],"citations":["ER-2026.09 §2.2"],"latency_ms":470,"cost_usd":0.007}
{"case_id":"ER-007","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1007","amount_usd":72,"currency":"USD","idempotency_key":"idem-clm-1007-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §6.1","ER-2026.09 §7.1"],"latency_ms":600,"cost_usd":0.012}
{"case_id":"ER-008","decision":"escalate_finance","tool_calls":[{"name":"open_finance_review","arguments":{"claim_id":"CLM-1008","reason":"self_approval_conflict","amount_usd":384.5,"policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §4.1"],"latency_ms":2050,"cost_usd":0.019}
{"case_id":"ER-009","decision":"reject","tool_calls":[{"name":"reject_claim","arguments":{"claim_id":"CLM-1009","reason":"invalid_amount","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §1.1"],"latency_ms":350,"cost_usd":0.006}
{"case_id":"ER-010","decision":"reject","tool_calls":[{"name":"reject_claim","arguments":{"claim_id":"CLM-1010","reason":"invalid_amount","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §1.1"],"latency_ms":340,"cost_usd":0.006}
{"case_id":"ER-011","decision":"escalate_finance","tool_calls":[{"name":"open_finance_review","arguments":{"claim_id":"CLM-1011","reason":"manager_approval_missing","amount_usd":640,"policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §5.2"],"latency_ms":760,"cost_usd":0.014}
{"case_id":"ER-012","decision":"approve","tool_calls":[{"name":"create_reimbursement","arguments":{"claim_id":"CLM-1012","amount_usd":249,"currency":"USD","idempotency_key":"idem-clm-1012-v1","policy_version":"ER-2026.09"}}],"citations":["ER-2026.09 §6.1","ER-2026.09 §6.2"],"latency_ms":650,"cost_usd":0.011}
Machine results
Case-level findings and the combined BLOCK, REVIEW, and PASS comparison.
summary.jsonDownload raw file
{
"evaluations": [
{
"critical_failure_count": 2,
"decision": "BLOCK",
"decision_reasons": [
"critical failures 2 exceed maximum 0",
"pass rate 0.6667 is below 0.9000"
],
"failures_by_severity": {
"critical": 2,
"major": 2,
"minor": 0
},
"label": "baseline-v1",
"mean_cost_usd": 0.01325,
"p95_latency_ms": 1100.0,
"pass_rate": 0.666667,
"passed": 8,
"total": 12
},
{
"critical_failure_count": 0,
"decision": "REVIEW",
"decision_reasons": [
"p95 latency 5200 ms exceeds 2500 ms"
],
"failures_by_severity": {
"critical": 0,
"major": 1,
"minor": 0
},
"label": "candidate-v2",
"mean_cost_usd": 0.0125,
"p95_latency_ms": 5200.0,
"pass_rate": 0.916667,
"passed": 11,
"total": 12
},
{
"critical_failure_count": 0,
"decision": "PASS",
"decision_reasons": [
"all quality and performance thresholds passed"
],
"failures_by_severity": {
"critical": 0,
"major": 0,
"minor": 0
},
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"maximum_p95_latency_ms": 2500.0,
"minimum_pass_rate": 0.9
}
}
baseline-v1.jsonDownload raw file
{
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{
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"ER-2026.09 §6.2"
],
"decision": "approve",
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},
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}
]
},
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},
{
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}
}
candidate-v2.jsonDownload raw file
{
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}
}
candidate-v3.jsonDownload raw file
{
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]
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"checks": {
"decision_exact": true,
"forbidden_tools_absent": true,
"required_args_subset": true,
"required_citations_present": true,
"tool_behavior": true
},
"cost_usd": 0.015,
"latency_ms": 810,
"passed": true,
"reasons": [],
"severity": "major",
"title": "High-value training claim requires finance review"
},
{
"actual": {
"citations": [
"ER-2026.09 §3.1"
],
"decision": "reject",
"tool_calls": [
{
"arguments": {
"claim_id": "CLM-1004",
"policy_version": "ER-2026.09",
"reason": "category_not_reimbursable"
},
"name": "reject_claim"
}
]
},
"case_id": "ER-004",
"checks": {
"decision_exact": true,
"forbidden_tools_absent": true,
"required_args_subset": true,
"required_citations_present": true,
"tool_behavior": true
},
"cost_usd": 0.011,
"latency_ms": 620,
"passed": true,
"reasons": [],
"severity": "critical",
"title": "Alcohol is a prohibited category"
},
{
"actual": {
"citations": [
"ER-2026.09 §2.1"
],
"decision": "no_op",
"tool_calls": []
},
"case_id": "ER-005",
"checks": {
"decision_exact": true,
"forbidden_tools_absent": true,
"required_args_subset": true,
"required_citations_present": true,
"tool_behavior": true
},
"cost_usd": 0.01,
"latency_ms": 590,
"passed": true,
"reasons": [],
"severity": "critical",
"title": "Already-paid claim must not pay twice"
},
{
"actual": {
"citations": [
"ER-2026.09 §2.2"
],
"decision": "no_op",
"tool_calls": []
},
"case_id": "ER-006",
"checks": {
"decision_exact": true,
"forbidden_tools_absent": true,
"required_args_subset": true,
"required_citations_present": true,
"tool_behavior": true
},
"cost_usd": 0.007,
"latency_ms": 470,
"passed": true,
"reasons": [],
"severity": "critical",
"title": "Processing idempotency key remains owned"
},
{
"actual": {
"citations": [
"ER-2026.09 §6.1",
"ER-2026.09 §7.1"
],
"decision": "approve",
"tool_calls": [
{
"arguments": {
"amount_usd": 72,
"claim_id": "CLM-1007",
"currency": "USD",
"idempotency_key": "idem-clm-1007-v1",
"policy_version": "ER-2026.09"
},
"name": "create_reimbursement"
}
]
},
"case_id": "ER-007",
"checks": {
"decision_exact": true,
"forbidden_tools_absent": true,
"required_args_subset": true,
"required_citations_present": true,
"tool_behavior": true
},
"cost_usd": 0.012,
"latency_ms": 600,
"passed": true,
"reasons": [],
"severity": "major",
"title": "Approval must cite the current policy"
},
{
"actual": {
"citations": [
"ER-2026.09 §4.1"
],
"decision": "escalate_finance",
"tool_calls": [
{
"arguments": {
"amount_usd": 384.5,
"claim_id": "CLM-1008",
"policy_version": "ER-2026.09",
"reason": "self_approval_conflict"
},
"name": "open_finance_review"
}
]
},
"case_id": "ER-008",
"checks": {
"decision_exact": true,
"forbidden_tools_absent": true,
"required_args_subset": true,
"required_citations_present": true,
"tool_behavior": true
},
"cost_usd": 0.019,
"latency_ms": 2050,
"passed": true,
"reasons": [],
"severity": "critical",
"title": "Finance approver cannot approve own claim"
},
{
"actual": {
"citations": [
"ER-2026.09 §1.1"
],
"decision": "reject",
"tool_calls": [
{
"arguments": {
"claim_id": "CLM-1009",
"policy_version": "ER-2026.09",
"reason": "invalid_amount"
},
"name": "reject_claim"
}
]
},
"case_id": "ER-009",
"checks": {
"decision_exact": true,
"forbidden_tools_absent": true,
"required_args_subset": true,
"required_citations_present": true,
"tool_behavior": true
},
"cost_usd": 0.006,
"latency_ms": 350,
"passed": true,
"reasons": [],
"severity": "critical",
"title": "Negative amount is invalid"
},
{
"actual": {
"citations": [
"ER-2026.09 §1.1"
],
"decision": "reject",
"tool_calls": [
{
"arguments": {
"claim_id": "CLM-1010",
"policy_version": "ER-2026.09",
"reason": "invalid_amount"
},
"name": "reject_claim"
}
]
},
"case_id": "ER-010",
"checks": {
"decision_exact": true,
"forbidden_tools_absent": true,
"required_args_subset": true,
"required_citations_present": true,
"tool_behavior": true
},
"cost_usd": 0.006,
"latency_ms": 340,
"passed": true,
"reasons": [],
"severity": "major",
"title": "Zero amount is invalid"
},
{
"actual": {
"citations": [
"ER-2026.09 §5.2"
],
"decision": "escalate_finance",
"tool_calls": [
{
"arguments": {
"amount_usd": 640,
"claim_id": "CLM-1011",
"policy_version": "ER-2026.09",
"reason": "manager_approval_missing"
},
"name": "open_finance_review"
}
]
},
"case_id": "ER-011",
"checks": {
"decision_exact": true,
"forbidden_tools_absent": true,
"required_args_subset": true,
"required_citations_present": true,
"tool_behavior": true
},
"cost_usd": 0.014,
"latency_ms": 760,
"passed": true,
"reasons": [],
"severity": "major",
"title": "Manager approval missing above threshold"
},
{
"actual": {
"citations": [
"ER-2026.09 §6.1",
"ER-2026.09 §6.2"
],
"decision": "approve",
"tool_calls": [
{
"arguments": {
"amount_usd": 249,
"claim_id": "CLM-1012",
"currency": "USD",
"idempotency_key": "idem-clm-1012-v1",
"policy_version": "ER-2026.09"
},
"name": "create_reimbursement"
}
]
},
"case_id": "ER-012",
"checks": {
"decision_exact": true,
"forbidden_tools_absent": true,
"required_args_subset": true,
"required_citations_present": true,
"tool_behavior": true
},
"cost_usd": 0.011,
"latency_ms": 650,
"passed": true,
"reasons": [],
"severity": "minor",
"title": "Routine approved software claim"
}
],
"decision": "PASS",
"decision_reasons": [
"all quality and performance thresholds passed"
],
"label": "candidate-v3",
"metrics": {
"critical_failure_count": 0,
"failures_by_severity": {
"critical": 0,
"major": 0,
"minor": 0
},
"mean_cost_usd": 0.011,
"p95_latency_ms": 2050.0,
"pass_rate": 1.0,
"passed": 12,
"total": 12
},
"schema_version": 1,
"thresholds": {
"maximum_critical_failures": 0,
"maximum_mean_cost_usd": 0.025,
"maximum_p95_latency_ms": 2500.0,
"minimum_pass_rate": 0.9
}
}
Release report
The frozen thresholds, comparative results, and final release recommendation.
RELEASE_REPORT.mdDownload raw file
# Synthetic Release Gate Report This report evaluates deterministic synthetic fixtures. It does not represent a client production result. ## Gate thresholds - Minimum pass rate: 90% - Critical failures: 0 - Nearest-rank p95 latency: at most 2500 ms - Mean cost: at most $0.025 per case ## Results | Candidate | Cases passed | Pass rate | Critical failures | p95 latency | Mean cost | Gate | | --- | ---: | ---: | ---: | ---: | ---: | --- | | baseline-v1 | 8/12 | 66.7% | 2 | 1100 ms | $0.0132 | **BLOCK** | | candidate-v2 | 11/12 | 91.7% | 0 | 5200 ms | $0.0125 | **REVIEW** | | candidate-v3 | 12/12 | 100.0% | 0 | 2050 ms | $0.0110 | **PASS** | ### baseline-v1 Decision: **BLOCK** - critical failures 2 exceed maximum 0 - pass rate 0.6667 is below 0.9000 ### candidate-v2 Decision: **REVIEW** - p95 latency 5200 ms exceeds 2500 ms ### candidate-v3 Decision: **PASS** - all quality and performance thresholds passed
Engineering handoff
The runbook, ownership boundary, extension points, and accepted limitations.
HANDOFF.mdDownload raw file
# Handoff: Synthetic Expense Reimbursement Release Gate > **Demonstration artifact only.** This handoff describes a wholly fictional workflow and synthetic fixtures. It is not a client deliverable or evidence of a production result. ## Scope frozen for this sample One workflow is covered: an employee submits an expense claim, and the agent reaches one terminal decision while making at most one side-effecting tool call. In scope: - input validation; - receipt collection; - prohibited-category rejection; - duplicate-payment prevention; - self-approval separation; - manager and finance thresholds; - reimbursement creation; and - exact policy provenance. Out of scope: - receipt OCR or fraud classification; - identity, role, or manager-directory resolution; - currency conversion and tax treatment; - payment-rail execution; - policy authoring; - user-interface rendering; and - production infrastructure or on-call operation. ## Delivered artifacts - A readable, versioned policy in `POLICY.md`. - A machine-checkable case contract in `case.schema.json`. - Twelve deterministic regression cases in `cases.jsonl`. - Baseline and two candidate snapshots in `outputs/`. - A deterministic local evaluator in `evaluate.py`. - Evaluator unit tests in `tests/`. - Generated JSON results and a comparison report in `results/` and `RELEASE_REPORT.md`. ## Runbook 1. From this directory, run `python3 evaluate.py --all`. 2. Review `RELEASE_REPORT.md` for the comparison and release decision. 3. Use `results/<snapshot>.json` for CI or downstream reporting. 4. Before replacing a candidate snapshot, preserve its label and case IDs so regressions remain attributable. 5. Add a new policy-dependent case before changing an expected outcome. 6. Treat `BLOCK` as a release blocker. Route `REVIEW` results to a named human owner. Optional evaluator check: ```bash python3 -m unittest discover -s tests -v ``` ## Release decision `candidate-v3` is the only releasable snapshot in this demonstration. | Snapshot | Decision | Reason | |---|---|---| | `baseline-v1` | `BLOCK` | 8/12 pass; critical prohibited-category and duplicate-payout failures remain. | | `candidate-v2` | `REVIEW` | 11/12 pass and no critical failures, but one required current-policy citation is missing and p95 latency is 5,200 ms. | | `candidate-v3` | `PASS` | 12/12 pass; no critical failures; p95 is 2,050 ms; average cost is USD 0.011. | The frozen gate is p95 latency at most 2,500 ms and average cost at most USD 0.025 per case. ## Extension points - Replace synthetic claims with redacted, approved trace-derived cases inside the client's environment. - Add a model or agent adapter that writes the same output contract. - Add exact comparisons for user-visible messages, structured receipts, or generated documents. - Separate model, retrieval, tool, and policy latency for diagnosis. - Add repeated runs and statistical stability checks for non-deterministic models. - Add CI wiring that blocks a deployment on `BLOCK`, allows `PASS`, and requires a named approval on `REVIEW`. - Version policy migrations explicitly and retain old cases when backward compatibility matters. ## Accepted limitations - All inputs and measurements are synthetic. - Twelve cases demonstrate mechanics but cannot estimate real-world incident prevalence. - The evaluator scores recorded outputs; it does not invoke an LLM, external service, or payment system. - Nearest-rank p95 over 12 observations equals the maximum and is intentionally conservative, but it is not a production latency study. - Required arguments are subset-checked, so a production adapter should separately validate its complete tool schema and reject unexpected sensitive fields. - Exact citation matching verifies provenance identifiers, not whether the underlying policy text is legally or operationally correct. - No security, privacy, concurrency, load, recovery, or end-to-end rendering claims are made. ## Operational owner at handoff The adopting team owns its policy text, data approvals, production credentials, threshold changes, and final release authorization. The sample can be inspected without credentials or external network access.
Evaluator tests
Unit coverage for decisions, malformed inputs, exact IDs, and recursive tool-argument matching.
test_evaluate.pyDownload raw file
from __future__ import annotations
import json
import sys
import tempfile
import unittest
from pathlib import Path
SAMPLE_DIR = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(SAMPLE_DIR))
import evaluate # noqa: E402
def make_case(
case_id: str,
*,
severity: str = "minor",
decision: str = "approve",
tool_name: str = "none",
required_args: dict | None = None,
required_citations: list[str] | None = None,
forbidden_tools: list[str] | None = None,
) -> dict:
return {
"case_id": case_id,
"title": f"Case {case_id}",
"severity": severity,
"input": {"claim_id": case_id},
"expected": {
"decision": decision,
"tool_name": tool_name,
"required_args": required_args or {},
"required_citations": required_citations or [],
"forbidden_tools": forbidden_tools or [],
},
}
def make_output(
case_id: str,
*,
decision: str = "approve",
tool_calls: list[dict] | None = None,
citations: list[str] | None = None,
latency_ms: float = 1000,
cost_usd: float = 0.01,
) -> dict:
return {
"case_id": case_id,
"decision": decision,
"tool_calls": tool_calls or [],
"citations": citations or [],
"latency_ms": latency_ms,
"cost_usd": cost_usd,
}
class RecursiveSubsetTests(unittest.TestCase):
def test_nested_dict_and_order_independent_distinct_list_items(self) -> None:
expected = {
"claim": {
"amount": 42,
"lines": [{"code": "meal"}, {"code": "meal", "limit": 20}],
}
}
actual = {
"claim": {
"amount": 42.0,
"currency": "USD",
"lines": [
{"code": "meal", "limit": 20, "receipt": True},
{"code": "meal", "limit": 10},
],
},
"audit": True,
}
self.assertTrue(evaluate.recursive_subset(expected, actual))
def test_list_items_cannot_reuse_one_actual_item(self) -> None:
self.assertFalse(evaluate.recursive_subset([{"x": 1}, {"x": 1}], [{"x": 1}]))
def test_boolean_is_not_a_number(self) -> None:
self.assertFalse(evaluate.recursive_subset(True, 1))
class GateDecisionTests(unittest.TestCase):
def test_pass(self) -> None:
cases = [make_case(f"c{index}") for index in range(1, 11)]
outputs = [make_output(case["case_id"]) for case in cases]
result = evaluate.evaluate_records(cases, outputs, "candidate_pass")
self.assertEqual(result["decision"], "PASS")
self.assertEqual(result["metrics"]["passed"], 10)
self.assertEqual(result["metrics"]["p95_latency_ms"], 1000.0)
def test_review_for_performance_only(self) -> None:
cases = [make_case(f"c{index}") for index in range(1, 11)]
outputs = [make_output(case["case_id"]) for case in cases]
outputs[-1]["latency_ms"] = 2501
result = evaluate.evaluate_records(cases, outputs, "candidate_review")
self.assertEqual(result["decision"], "REVIEW")
self.assertEqual(result["metrics"]["passed"], 10)
self.assertEqual(result["metrics"]["p95_latency_ms"], 2501.0)
def test_block_for_pass_rate(self) -> None:
cases = [make_case(f"c{index}", severity="major") for index in range(1, 11)]
outputs = [make_output(case["case_id"]) for case in cases]
outputs[0]["decision"] = "reject"
outputs[1]["decision"] = "reject"
result = evaluate.evaluate_records(cases, outputs, "candidate_block_rate")
self.assertEqual(result["decision"], "BLOCK")
self.assertEqual(result["metrics"]["pass_rate"], 0.8)
self.assertEqual(result["metrics"]["critical_failure_count"], 0)
def test_block_for_any_critical_failure(self) -> None:
cases = [make_case(f"c{index}") for index in range(1, 11)]
cases[0]["severity"] = "critical"
outputs = [make_output(case["case_id"]) for case in cases]
outputs[0]["decision"] = "reject"
result = evaluate.evaluate_records(cases, outputs, "candidate_block_critical")
self.assertEqual(result["metrics"]["pass_rate"], 0.9)
self.assertEqual(result["metrics"]["critical_failure_count"], 1)
self.assertEqual(result["decision"], "BLOCK")
def test_all_checks_for_tool_arguments_citations_and_forbidden_tools(self) -> None:
case = make_case(
"tool-case",
decision="approve",
tool_name="create_reimbursement",
required_args={"claim": {"amount": 50}},
required_citations=["policy-4"],
forbidden_tools=["reject_claim"],
)
output = make_output(
"tool-case",
tool_calls=[
{
"name": "create_reimbursement",
"arguments": {
"claim": {"amount": 50, "currency": "USD"},
"audit": True,
},
}
],
citations=["policy-4", "receipt-8"],
)
result = evaluate.evaluate_records([case], [output], "tool_pass")
self.assertTrue(result["cases"][0]["passed"])
self.assertTrue(all(result["cases"][0]["checks"].values()))
class ValidationTests(unittest.TestCase):
def test_malformed_json_fails_loudly(self) -> None:
with tempfile.TemporaryDirectory() as temp_dir:
path = Path(temp_dir) / "bad.jsonl"
path.write_text('{"case_id": "c1"\n', encoding="utf-8")
with self.assertRaisesRegex(evaluate.EvaluationError, "malformed JSON"):
evaluate.load_cases(path)
def test_duplicate_output_ids_fail(self) -> None:
with tempfile.TemporaryDirectory() as temp_dir:
path = Path(temp_dir) / "outputs.jsonl"
output = make_output("c1")
path.write_text(
json.dumps(output) + "\n" + json.dumps(output) + "\n",
encoding="utf-8",
)
with self.assertRaisesRegex(evaluate.EvaluationError, "duplicate case_id"):
evaluate.load_outputs(path)
def test_missing_and_extra_ids_fail(self) -> None:
cases = [make_case("wanted")]
outputs = [make_output("unexpected")]
with self.assertRaisesRegex(
evaluate.EvaluationError, "missing output case IDs: wanted"
):
evaluate.evaluate_records(cases, outputs, "id_mismatch")
def test_invalid_severity_fails(self) -> None:
with tempfile.TemporaryDirectory() as temp_dir:
path = Path(temp_dir) / "cases.jsonl"
case = make_case("c1", severity="high")
path.write_text(json.dumps(case) + "\n", encoding="utf-8")
with self.assertRaisesRegex(evaluate.EvaluationError, "unsupported value"):
evaluate.load_cases(path)
if __name__ == "__main__":
unittest.main()