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The runtime layer that makes AI agents cheaper and more reliable
ReasonBlocks is the runtime layer that makes AI agents cheaper and more reliable. We correct agent reasoning and compress tokens mid-run, with gains compounding across runs. On SWE-Bench pro we have seen a 52% token reduction and 42% accuracy lift.
ReasonBlocks provides a runtime layer for AI agents that remember how they solved problems, aiming to reduce debugging time and token costs. It positions itself as a solution to improve reliability and efficiency of AI agents.
A runtime layer for AI agents that retains and references past problem-solving steps to improve consistency and reduce token usage and debugging effort. The product emphasizes memory of solution processes to streamline agent execution and lower operational costs related to tokens.
Who it’s for: AI/product teams building autonomous agents and decision-making workflows who want more reliable agent behavior and lower token costs
Formerly at Stanford CS. Published in Nature Sustainable Agriculture (ML × agriculture). UN Best Paper. USAMO qualifier. 11 years building with co-founder Rohan. Currently making AI agents smarter and more reliable @ ReasonBlocks
Formerly at CMU, studying Information Systems & Artificial Intelligence. Molecular dynamics + AI research. AI for distributed energy at ENGIE. IoT research at UC Davis. 11 years building with co-founder Sajeev; we're making LLMs cheaper and more reliable.
Infra layer for smarter and cheaper AI agents
ReasonBlocks is a runtime layer for AI agents that catches mid-run failures, compresses redundant context from message history, and builds a private reasoning library from production data to improve agent accuracy and reduce token costs.
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Reasoning and verification infra for AI
Sandboxed coding agents for everyone on your team.
