3 things I did differently building a self-evolving agent -- and the number each one actually costs

Building an open-source agent, here are the 3 bets that aren't the usual ReAct-loop stuff: 1. Self-evolution with a fitness signal. Most "learning" agents append whatever happened. Mine keeps a learned change only when a verified result + honest A/B proves it moved the pass rate -- gated on the real working-tree diff, never the model's self-report. Cost: you need a grader, and you accept fewer, slower "learnings." 2. Security by architecture, not prompt-begging. Prompt injection is treated as unfixable at the prompt layer, so: end-to-end taint tracking (memory/skills born in a tainted run don't auto-promote), a quarantined reader that turns untrusted content into schema-validated fields before the privileged agent sees it, and a tool allowlist that narrows under taint. Measured red-team ASR dropped 100% -> ~14% (not "secure" -- a number). Cost: some legit content gets over-restricted. 3. Honest benchmarks. I publish confidence intervals and the cases it still fails, and I don't re-roll to manufacture significance. Cost: the numbers look less impressive than a cherry-picked demo. Reasoning core is a fusion panel (panel -> judge -> synth) behind a cost-aware router. Apache-2.0, still alpha. Repo link in a comment -- happy to have the security claims stress-tested.