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NewsMaterial2 min readDisconfirmAI Editorial

OpenAI and Ironclad Partner: Turning Contract Approval Workflows into AI Agent Training Tasks

OpenAI announced its collaboration method with contract software company Ironclad: domain experts defined 11 tasks across legal, commercial, and procurement functions; a training environment was synthesized using publicly available contract data from the U.S. Securities and Exchange Commission (SEC); and GPT-6 Astra was trained via reinforcement learning. OpenAI reported that Astra scored higher than previous-generation models, but explicitly stated that results cover only these 11 research tasks and represent simulated estimates rather than actual customer measurements.

2026-10-06 18:00 GMT+8

OpenAI announced on October 6, 2026, a research collaboration with contract software company Ironclad. The core of this announcement is not the release of a new feature, but a description of a systematic method for converting professional software workflows into AI training tasks.

The method consists of three steps. First, Ironclad employees and individuals at OpenAI who use Ironclad identified the tasks together. They ultimately determined 11 specific jobs spanning legal, commercial, and procurement work, such as setting up non-disclosure agreements, creating procurement approval processes, and updating reusable legal clauses to reflect the jurisdiction selected by the requester. OpenAI estimated that an experienced employee would take an average of 30 to 40 minutes to complete each task.

Second, 8 to 50 evaluation criteria were established for each task, allowing researchers to see where the model succeeded and where it failed. Ironclad also provided a hosted software environment of its product, enabling the models to practice repeatedly.

Third, the training data came from public sources. OpenAI explained that the synthetic tasks were based on contracts publicly available in the U.S. Securities and Exchange Commission’s (SEC) EDGAR database, filtered to remove personal information before generation. OpenAI explicitly stated that it did not use its own customer data, internal contracts, or Ironclad’s non-public customer data. The models improved through reinforcement learning and feedback loops on these synthetic tasks.

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OpenAI’s self-reported results show that GPT-6 Astra achieved an average score of 55.0% on these 11 tasks, compared to 41.6% for GPT-5.6 Sol; the estimated time per attempt dropped from 37.0 minutes to 19.2 minutes. An internal model used in Astra’s development reached 63.7%.

However, OpenAI provided two key caveats in footnotes: these results cover only the 11 research tasks and do not represent all of Ironclad’s workflows; the times are simulated estimates based on assumed model processing and generation speeds, not measured customer time savings. This means the figures describe performance in a controlled evaluation and cannot be directly read as actual efficiency gains after enterprise deployment.

OpenAI noted in the article that contract processes must handle exceptions while preserving business-dependent rules. If an agent loses track of a rule midway through a task, it limits the scope of work a software company can confidently entrust to it, highlighting why human oversight remains necessary even as agent capabilities improve. Sunita Verma, Chief Technology Officer of Ironclad, stated in a declaration that for AI to truly function in the contracting field, it must do more than complete individual actions; agents need to understand the full contract lifecycle, including how business workflows connect while retaining team-dependent controls.

Sources

  • openai.comOpenAI↗
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