# OpenAI and Ironclad: Turning Contract Workflows Into Agent Evals > Source: > Published: 2026-10-07 00:07:07+00:00 OpenAI and Ironclad just published a case study on using production contract workflows as both training data and evaluation benchmarks for computer-use agents. This is not a demo. It is a partnership where a SaaS company opens its workflow engine to become an agent training ground. The plumbing question is simple: how do you turn a multi-step contract approval flow into a reproducible eval without leaking customer data, and how do you keep that eval valid when the underlying product changes? Most computer-use benchmarks are synthetic. They simulate browser tasks or API calls in controlled environments. Ironclad's contract lifecycle management platform offers something different: real multi-step workflows with approval chains, redlining, negotiation loops, and conditional branching. These workflows are stateful. A contract might move from draft to legal review, back to sales for edits, then to finance for approval. Each step has different permissions, different UI surfaces, and different failure modes. If an agent can navigate this, it can navigate most SaaS tools. The partnership gives OpenAI access to: Turning a production workflow into an agent eval requires solving three problems: **1. Data sanitization** You cannot train on customer contracts. Ironclad must generate synthetic contracts that preserve workflow complexity without exposing real terms, parties, or negotiation history. This means: **2. Reproducibility** A contract workflow is not deterministic. Different users take different paths. To make this an eval, you need: **3. Version drift** Ironclad ships product updates. When the UI changes, the eval breaks. The infrastructure must: Here is the likely flow: ``` ┌─────────────────┐ │ Ironclad Prod │ │ Workflow Engine │ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Workflow Logger │ ← Captures state transitions, UI events, API calls └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Sanitization │ ← Strips PII, generates synthetic contracts │ Pipeline │ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Eval Snapshot │ ← Versioned environment + workflow definition │ Generator │ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Agent Executor │ ← OpenAI agent runs against snapshot └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Failure Labeler │ ← Human annotators classify agent errors └─────────────────┘ ``` The workflow logger is the critical piece. It must capture: When an agent fails on a contract task, someone has to classify why. This is not automated. The labeling taxonomy likely includes: | Failure Type | Example | Fix Strategy | |---|---|---| | Navigation error | Clicked wrong button | Improve UI element detection | | State misread | Thought contract was approved when pending | Better state extraction from DOM | | Logic error | Skipped required approval step | Refine workflow graph understanding | | Timeout | Took too long on redline comparison | Optimize document parsing | | Permission boundary | Tried to approve without authority | Teach role-based access model | Ironclad employees likely do the initial labeling. OpenAI uses those labels to retrain. The loop tightens over time. A reproducible eval needs a snapshot format. Here is a plausible schema: ``` python from dataclasses import dataclass from typing import List, Dict, Any @dataclass class WorkflowSnapshot: version: str # Ironclad product version workflow_id: str initial_state: Dict[str, Any] # Contract metadata, approvers, etc. steps: List[Dict[str, Any]] # Ordered list of expected actions success_criteria: Dict[str, Any] # What "done" looks like ui_snapshots: List[str] # DOM or screenshot hashes per step def validate_agent_run(self, agent_trace: List[Dict]) -> bool: """ Compare agent actions against expected workflow steps. Returns True if agent reached success criteria. """ for i, expected_step in enumerate(self.steps): if i >= len(agent_trace): return False # Agent stopped early actual = agent_trace[i] if not self._step_matches(expected_step, actual): return False return self._check_success(agent_trace[-1]) def _step_matches(self, expected: Dict, actual: Dict) -> bool: # Fuzzy match on action type and target element return ( expected["action_type"] == actual["action_type"] and expected["target_element"] in actual["dom_path"] ) def _check_success(self, final_state: Dict) -> bool: # Check if contract reached expected status return final_state.get("contract_status") == self.success_criteria.get("status") ``` This snapshot is versioned. When Ironclad ships a UI update, old snapshots remain valid for regression testing. New snapshots get generated for the updated product. To debug agent failures, you need full trace data: The observability stack must correlate these streams. If an agent clicks the wrong button, you need to see: Without this correlation, failure labeling becomes guesswork. When Ironclad changes a workflow (new approval step, different UI layout), the eval suite must adapt. Two strategies: **Pinned snapshots** Keep old product versions running in isolated environments. Agents train against v1.2, v1.3, v1.4 simultaneously. This catches regressions but requires infrastructure to run multiple product versions. **Adaptive evals** Update eval snapshots when the product changes. Mark old snapshots as deprecated. This keeps evals current but loses historical comparison. Most teams use a hybrid: pin critical workflows, adapt the rest. Ironclad cannot give OpenAI direct access to production. The sanitization pipeline must run inside Ironclad's VPC. The output (synthetic contracts and workflow snapshots) gets exported to OpenAI's training environment. Key boundaries: The sanitization pipeline is the trust boundary. If it leaks PII, the partnership fails. | Dimension | Production Workflows | Synthetic Benchmarks | |---|---|---| | Realism | High (real complexity) | Low (simplified tasks) | | Data privacy | Hard (requires sanitization) | Easy (no real data) | | Reproducibility | Hard (version drift) | Easy (static) | | Failure diversity | High (real edge cases) | Low (designed cases) | | Setup cost | High (partnership required) | Low (build in-house) | Production workflows expose real failure modes. Synthetic benchmarks are easier to control. The best eval suites use both. **Use this approach when:** **Avoid this approach when:** The OpenAI-Ironclad partnership shows that production workflows can become agent training grounds. The plumbing is non-trivial: sanitization, versioning, failure labeling, and observability all need custom infrastructure. But the payoff is agents that handle real work, not just demos.