ReasonBlocks is an infrastructure layer that makes production AI agents both smarter and cheaper by intervening during execution rather than after the fact. Most agent failures in production are repeats: the same infinite loops, the same dead-end tool calls, the same wasted reasoning tokens spent rediscovering paths the agent has already failed at before. ReasonBlocks treats this as an infrastructure problem, sitting in the runtime to catch failures mid-run, compress context that does not matter, and accumulate a private reasoning library that compounds in value every time the agent runs.
The platform plugs into existing agent stacks in minutes without requiring a framework rewrite. As traffic flows through, ReasonBlocks identifies recurring reasoning patterns, caches them as reusable blocks, and reuses them on future calls to cut token spend and latency. It also actively monitors execution for loop signatures and known failure modes, halting or rerouting agents before they burn budget. The net effect is an agent that gets cheaper and more reliable the longer it runs in production, with measurable improvements from the first call.
The product is aimed at teams running vertical agents in legal, finance, healthcare, security, and research, where accuracy and cost-per-task both matter. ReasonBlocks is part of Y Combinator's Spring 2026 (P26) batch, co-founded by Sajeev Magesh (ex-Stanford) and Rohan Vij (ex-CMU), who have been building software together since second grade.