DCL Evaluator
Cryptographic audit trail for every AI agent decision
What it does
Can you prove what your AI agent actually decided? DCL Evaluator gives you cryptographic proof of every LLM decision — deterministic, tamper-evident, bit-for-bit reproducible. Every output is evaluated against your policy. COMMIT or NO_COMMIT. Each decision gets a SHA-256 hash, chained to the previous one. Works with Ollama, Claude, GPT-4, Grok, Gemini. 100% offline. Desktop-first.
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Agent Eval KitJul 2026 · iisacc-justmoong.github.io · ▲7Deterministic offline release evidence for AI agents
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Hi HN! Run it: OPENROUTER_API_KEY="sk" npx bff-eval --demo We built a tool to help people take LLM outputs and easily grade them / eval them to know how good an assistant response is. We've built a number of LLM apps, and while we could ship decent tech demos, we were disappointed with how they'd perform over time. We worked with a few companies who had the same problem, and found out scientifically building prompts and evals is far from a solved problem... writing these things feels more like directing a play than coding. Inspired by Anthropic's constitutional ai concepts, and amazing…
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I've been working on a site [1] to give people control of their LLM workflows through AI evals - automated checks that, once defined, let you move fast without regressions and cut through hype with proof. That one-liner is aimed at software engineers, but I've spent my career helping cross-functional teams collaborate, and that's really what this is about. AI agents make powerful workflows very plausible, but only if teams can grow them incrementally without losing control - no vendor lock-in, no discipline silos, no blind trust in outputs. The site tries to meet different audiences where…
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