# Agent-ProSAT for CoT via verifiable programmatic solver coding for CoT synthesis - Feedback Appreciated! > Source: > Published: 2026-07-22 18:35:18+00:00 Dear [@John6666](https://discuss.huggingface.co/u/john6666) I am incredibly grateful to you for taking the time to write out such a detailed, rigorous breakdown. This is exactly the feedback I was hoping for when releasing Agent-ProSAT and the ProSAT dataset. I genuinely appreciate you diving into the architecture. You’ve essentially provided a blueprint for the next steps and answered questions I was actively wondering how to approach. THANK YOU! I want to confirm I understand correctly: your core thesis is to decouple the canonical audit record (what is computed) from the training view (how it is explained) as the agent works on `prosat.py` , right? Given that Agent-ProSAT represents an iterative autoresearch loop where the LLM writes and refines the solver code based on harness feedback, I realize I was making the agent’s job unnecessarily hard. By being obsessed with rigorous determinism, I was forcing it to simultaneously figure out the logic *and* bake in the natural language formatting. **Here my summary and plan of action through the lens of your comments:** **Updating Agent Directives:** I will instruct the agent to generate code that strictly outputs a structured `ExecutionWitness` (JSON events). A separate, non-agentic renderer will handle the Views (A, B, C, D). **Upgrading the Assessment Loop:** I’ll implement your 3-way verification cascade (Accept / Reject / Defer) in the evaluation harness to save token overhead and avoid forcing binary decisions on ambiguous cases. **Four-Arm Schema Ablation:** Once the agent generates the JSON-emitting solvers, I’ll run the empirical ablation on the four views and track the generator-to-evaluation lineage in a manifest. **Two quick questions for you on the verification cascade:** When you hit a “Defer” case (e.g., multiple rules fit the demonstrations), do you route that to a stronger LLM dynamically during the run, or queue them up for an offline/human review batch? Do you think a harness like my earlier work (autoresearch-MIL: [GitHub - habanwer/autoresearch-MIL: AI agents running research on single-GPU nanochat training automatically · GitHub](https://github.com/habanwer/autoresearch-MIL) ) would improve the aforementioned roadmap? Thanks again for the fantastic insights and pointing me toward the *Code Execution as Grounded Supervision* and *Think Like You Execute* precedents. I’m going to start building out the JSON audit schema and the 3-way verification cascade around the next week. I’ll update the thread once the first ablation results are in! Best, Shehab