New Paper Introduces Human-on-the-Bridge for Scalable AI Agent Evaluation

Alpha Batcher@alphabatcher

@omarsar0 great paper for study

Scalable Evaluation for AI Agents interesting topic

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Om Kute@theomkute

@omarsar0 A paper on agent routing - http://arxiv.org/abs/2606.20047

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elvis@omarsar0

>> Scalable Evaluation for AI Agents <<

If you run agent evaluation in production, this one is worth your time.

It shows that front-loading human judgment into reusable evaluation assets is useful.

But why?

Agents reason across turns, call tools, hold context, follow policies, and act under uncertainty, so they have to be judged as behavioral systems.

Current methods each give a fragment. Benchmarks measure fixed capabilities, human review preserves judgment but does not scale, LLM-as-judge inherits the evaluator design problem, red teaming is episodic, and trace audits need explicit evidence rules.

Human-on-the-Bridge puts human expertise upstream, where experts curate reusable evaluation intelligence before testing rather than reviewing each output in the loop.

Paper: https://arxiv.org/abs/2606.16871

Learn to build effective AI agents in our academy: https://academy.dair.ai/

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Hussain Hashim | Building Sunday Back@itsthedonhashim

@omarsar0 @omarsar0 interesting take, makes me wonder how this might change agent training. could really shift how we look at adaptation in dynamic contexts

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GeniusPothead 💹🧲@GeniusPothead

@omarsar0 Evals are the real moat, not prompts

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Youssef El Manssouri@yoemsri

@omarsar0 I really like this concept since we still need our own human intuition to set the actual rules of the system.

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Zynex@0xzynex

@omarsar0 the hard part is keeping those assets updated

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AB4@AB401711043

@omarsar0 @dair_ai Mordida

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Karolina 🩵@Karolina_1403

@omarsar0 @dair_ai Spot on. Human-on-the-Bridge effectively addresses the LLM-as-a-judge bias through expert curation, but it introduces a new architectural challenge: how do we prevent the Harness from becoming too rigid in the face of emergent behaviors?

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V0LYX@0xV0LYX

@omarsar0 the real challenge is keeping human judgment reusable as the agent behavior space grows

wonder how the "bridge" generalizes across different agent architectures

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Strata@ChainZenit

@omarsar0 the logic behind front-loading these eval assets is super interesting.

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Hunter Gon@gonlenidefi

@omarsar0 this framing assumes human eval is the bottleneck

but the real bottleneck is getting agents to handle ambiguous edge cases consistently

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51-50_X@FiftyOne_50_

@omarsar0 @dair_ai Human-on-the-Bridge is the right direction.

But upstream human judgment is still not authority unless it can deny deployment, revoke access, preserve evidence, and stop consequence.

Evaluation intelligence is signal.

The brake is who can still say no.

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Ishi@Ishi_ish1011

@omarsar0 @dair_ai Agents aren't failing because they're bad at reasoning. They're failing because we still don't know how to measure reasoning at scale. 🎯

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DrewOnAI@Drew_OnAI

@omarsar0 most evals are just fancy hallucination detectors anyway. if you can't build a test that doesn't break in an hour, stop pretending it works

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Eclipse 🌖@ECLresearch

@omarsar0 Spot on — evaluating agents just by final outcome misses the key failure modes: tool misuse mid-trajectory and context decay over multi-turn. Front-loading reusable judgments on those layers actually surfaces regressions that end-task metrics flatten.

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