Jev matches 40 client requests to GitHub issues at 150x lower decision cost
Inside the TrueForge agent harness, Jev matched client requests to GitHub issues as accurately as letting Opus make every match, at 150x lower decision cost.
i just put Jev inside an open agent harness. (150x cheaper decision-making than LLM) agents burn a lot of tokens on decisions that are really just picking one option from a short list, like matching a client request to the right GitHub issue. so i built a weekly client follow-up agent that does exactly that. i tested it on 40 requests, and it was as accurate as letting Opus make every match, at 150x lower decision cost. here's the tech stack i used: → TrueForge as the agent harness → Jev for the matching decisions → Daytona as the sandbox for code execution Jev is a decision model from TypeSafe AI. it takes the options from your app and returns a structured choice with a calibrated confidence score, without generating any text. that confidence decides what gets a second look. low-confidence matches go back to Opus for review, and in my test, that review caught 5 wrong matches that would otherwise have gone through. TrueForge is TrueFoundry's harness, and it keeps context small. it loads tool definitions only when needed, writes large tool outputs to files, and lets the agent process data in scripts instead of the conversation. on DevRev's Enterprise Bench, it solved about as many tasks as Claude Managed Agents on roughly a third of the tokens. it also keeps credentials outside the sandbox and holds every write for approval. if you want to try this out, TrueForge is fully open source (MIT-licensed) and runs on your own machine with a single command. TrueForge GitHub: https://github.com/truefoundry/trueforge (don't forget to star 🌟) to help you reproduce my work and understand all the integration details, i wrote a detailed article. it's quoted below.
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