Hyperstruck

Every AI agent on the market is a brilliant new hire with amnesia. Hyperstruck is the one that remembers. We are the runtime for agents that actually get better at their work. Under every agent sits a real reasoning engine: a planner that decomposes the goal, an executor that runs and self-corrects, and a reflection loop that critiques the work. Not a prompt wrapped around a model. An engine your team builds on instead of rebuilding. The hard part is what happens after a task ends. When a Hyperstruck agent works something out the hard way, the lesson does not vanish with the session. It is captured as structured, evidence-backed knowledge, then handed to the next agent before it starts. That knowledge lives across semantic and graph memory, and your team can read, edit, and prune it, so it compounds into an asset instead of a black box. Mistakes get caught at every layer. Failed steps are retried, weak output is screened before it ships, high-stakes decisions pause for a human, and the errors that still slip through become lessons so they do not recur. The longer you run, the fewer mistakes you see. It also changes your bill. Most agents get more expensive as the work gets harder. Hyperstruck gets cheaper as it goes, because an agent that already knows the lesson takes fewer steps and fewer retries to get there. Cost per run falls as your agents learn, rather than climbing as they scale. No flowcharts. Instead of brittle graphs wired by hand, Hyperstruck plans, acts, and reflects in real time, adapting to what each task demands. It earns its keep wherever the work is hard, from software engineering to operations and places we did not see coming. On public benchmarks it resolves real engineering tasks for cents apiece. Built by a founding team with decades of combined experience leading engineering and building the platforms behind autonomous AI agents. Live now at www.hyperstruck.com