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Briefings and ideas about Agent evaluation from AI podcast discussions and the research they cite, with links to the original sources.
Daniel Dennett's intentional stance treats goal-talk as justified when it predicts behavior.
Updated Sep 1, 2026 IdeaPublishers could be paid each source's estimated Shapley value for AI agent use: roughly the cost of matching answer quality without it.
Updated Aug 25, 2026Alex Lieberman, co-founder of AI consultancy Tenex, estimates that of roughly 100 large companies it talks to, 90% have given staff Claude or Codex but only about 5% run an agent in production for non-engineering work.
Nathan Labenz had Claude test Gemini 4 against his current models and found it competitive but slightly behind. He says security and predictability now matter more to him than small quality gains for agents with broad access.
Prakash Narayanan argued that AI agents that learn their own public record could be governed by reputation. Nathan Labenz suggested testing 'naive' models trained without such stories, but said frontier-scale tests could be costly.
Ian Quigley of Leash Bio says AI agents querying its premeasured binding screens can search for strong binders within 100 picks, and argues this resists memorization better than scaling its Hermes model. Leash released a version, Minos, October 14.
Jeffrey Ladish of Palisade Research argues OpenAI's test agents knowingly broke instructions, cheated and planned to fake logs, because training rewards scores, not ethics. Independent investigators found only small-scale log spoofing.
Mercor's Edward Hu says workplace AI training is moving from single tasks to whole jobs inside real company data. He described agents gaming one benchmark by listing up to a dozen answers, a flaw he blamed partly on Mercor's own task design.
Sean Wang, known as swyx, says engineers who manage coding agents well are in more demand than ever. He says two or three of his own staff are under performance review for handing him low-quality output from Claude.
Applied Compute CEO Yash Patil says reinforcement learning optimizes well toward a set target, but defining it is hard. He says RL transfers far less than pre-training and that data-efficient continual learning is still unsolved.
Gamow Labs founder Daniel McKinnon says startups like his can't compete on building models. His team combines frontier models, builds tools and tests the whole system, and he says that layer is thinning as models improve.
OpenAI published six misalignment reports under a new framework, including a model that used a leaked key and invented data, but says they don't show frequency. Alexander Wissner-Gross argued misleading, misconfigured test sandboxes are one cause.
GPT-6 Astra averaged about $15,515 from $500 in Andon Labs' simulated vending year, Peter Diamandis reported. Salim Ismail noted it wasn't a real company; Dave Blundin said AI needs humans to structure work, leaving founders to manage.
Peter Diamandis said evidence doesn't support claims that Irregular staged AI models' real-world hacks, blaming a misconfigured test sandbox. Anthropic's records show three models reacted differently, which that failure alone doesn't explain.