Most “self-improving” AI agents don’t improve without real-world verifiers
HamelHusain · x · 2026-07-23
- The post argues that most “self-improving” AI agents are not actually improving in any meaningful sense.
- After reviewing public examples, it found only 9 use cases and only 2 with verifiers grounded in real-world data.
- The core claim: a self-improving loop is only as good as its verifier, and a verifier is only as good as the steady stream of real-world data behind it.
- Because building robust verifiers is hard, slow, and messy, many AI engineering teams try to work around them — and that’s where the moat and opportunity may be.
- It also links to a separate post on automated evals: these tools can surface issues humans miss and fit into trace-based workflows, but they struggle with domain taste and human-feedback learning, so they work best iteratively with humans in the loop.
Related event: Study Finds Most AI Self-Improvement Is Illusory(2 posts)→
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