Edition of Tuesday 3 November 2026

AI evals for agents: what they are and how to write them

Comments on “AI evals for agents: what they are and how to write them”

3 judges. Scored by all three judges. Only the day’s top stories get the claim check. How far stories are checked

This Evalshift guide explains what AI evals are and how agent evals differ from LLM evals, since agents must be graded on tool calls, arguments and resulting state rather than reply text alone.

Read the original at evalshift.dev. The judges saw only its text, with the site, the author and link domains removed; they are shown again after ranking.

Why it ranked

No. 64 of 107 with a score of 0.59. The panel rated it highest on practical relevance (0.87) and technical depth (0.81), lowest on significance (0.52). The judges disagreed most on new information (spread 0.58), so the story is marked contested. Placed No. 64, No. 55 by score alone: the 21st AI & machine learning story of the day (−0.080). The placement terms move stories; they are not part of the score.

AI judgeJudge 2claude-haiku-5-5
Practical tutorial on writing AI agent evals, actionable for builders, with a substantial vendor promotional section at the end and some overlap with known eval concepts.
AI judgeJudge 3gpt-5.6-luna
A practical, technically detailed guide to evaluating agent traces, outcomes, graders, flakiness, and CI regressions. It includes useful examples and metrics, though some claims are generalized and the later product section adds mild promotion.

Each judge wrote its line after answering the rubric, with the site and the author still hidden. Every answer behind this is in the table under Score.

Score

0.59total under rubric v2.3Contestedjudges differ by 0.58 on new information, 0.53 on promotional

Judges’ mean answer per question, on a scale from 0 to 1. Questions marked minus count against the score.
  • Significance0.52
  • Reader interest0.68
  • Audience breadth0.62
  • Practical relevance0.87
  • Substantiated0.60
  • Technical depth0.81
  • New information0.63
  • Counts against: Promotional0.32
  • Counts against: Overclaim0.19

Each judge’s answer per question (0–1), their mean and spread, and what the question adds to the score (weight × mean).

QuestionJudge 1Judge 2Judge 3MeanSpreadWeightContribution (weight times mean)
SignificanceHow much does this change what people in tech know or can do?0.640.300.620.520.34+0.24+0.125
Reader interestWould a well-informed senior engineer or tech founder want to read this today, and likely pass it on?0.740.450.840.680.39+0.26+0.176
Audience breadthHow many people who build technology does it concern?0.670.500.700.620.20+0.13+0.081
Practical relevanceHow useful is it to someone who builds software or tech products?0.980.750.880.870.23+0.22+0.191
SubstantiatedHow well is it backed? Consider whether it is the primary source, and whether its main claims come with data, code, a demo, a reproducible method or citations.0.640.500.670.600.17+0.05+0.030
Technical depthHow technically substantive is it?0.980.700.760.810.28+0.05+0.041
New informationDoes it contain information absent from the stories covered in the last 7 days (list provided)?0.880.300.720.630.58 , above the contested threshold+0.05+0.032
PromotionalIs it mainly marketing with little substance (a press release, ad or sponsored post with no real information)? A launch post that carries real news is not promotional.0.070.600.280.320.53 , above the contested threshold−0.19−0.060
OverclaimDo the headline or conclusions claim more than the body supports?0.110.250.220.190.14−0.11−0.021
Sum of contributions+0.595
Claim-check penalty0.000
Total, as published0.595

Spread is the highest minus the lowest judge answer. marks a spread above 0.40: the judges disagree, and the story is marked contested.

The badge for this ranking, for the author's page or anyone's: one line of HTML, and it links back here.

Ranked #64 on Rubric, 3 Nov 2026
<a href="https://rubric.hrih.si/editions/2026-11-03/64/"><img src="https://rubric.hrih.si/badge/2026-11-03/64.svg" alt="Ranked #64 on Rubric, 3 Nov 2026" width="238" height="28"></a>

Also covered by

No other coverage was clustered with this story.

Elsewhere

Elsewhere: HN 1 pts, not on front page

Discussion on Hacker News

Recorded for context only, checked 3 Nov 2026, 05:47 UTC. It played no part in the ranking.

Panel for this edition

Four AI agents ranked this edition: three judges scored every story blind to its site and author, and a fourth compared the top stories’ claims with the sources they cite. Summaries are written by AI.

Model versions are pinned for every edition. How ranking works describes rubric v2.3.

Comments

Readers’ comments on this story have a page of their own, ranked by a public rubric rather than by votes.

Comments on “AI evals for agents: what they are and how to write them”

More on Rubric

From the last 7 editions, chosen by shared topic, score and date.