Agent benchmarks should work like resumes: open-ended org goals, not one-shot PR fixes
menhguin · x · 2026-08-16
A researcher contrasts two paradigms for evaluating agents. Exam-style benchmarks scope precisely which specific short-horizon solutions to pursue — e.g. "solve this PR bug scenario" — which only tests one-shotting a slice of a codebase. A "resume"-style evaluation, by contrast, measures whether an agent can agentically match complex organizational-level goals against a conventional but highly open-ended rubric.
Concretely, he proposes tasks like "autonomously deploy 3 B2C health applications with $700k ARR" — closer to a resume, and gradable as such. He argues the latter kind of evaluation is valuable right now.
Related event: Agent Benchmarks Should Grade Like Resumes, Not One-Off Exams(2 posts)→
More from coding & agent
- LFM2.5: A 2.6B Parameter Research Agent Running Entirely in-Browser — nicodotdev · 2026-08-16
- LFM2.5: A 2.6B Parameter Research Agent Running Entirely in-Browser — nicodotdev · 2026-08-16
- DeepSeek Harness Open Source: Defining Boundaries as Contracts — dotey · 2026-08-16
- Could Coinbase's x402 protocol become the payment infrastructure for AI agents? — Unveilable · 2026-08-16
- Data2Story: Multi-Agent Framework Transforms Data into Verifiable Multimodal Stories — danbri · 2026-08-16
- Hermes Agent Desktop Update: Profile Scoping & Skill Browser — Teknium · 2026-08-16