Research question: how do we measure agent success without hiding human cleanup?
GitHub account: mbabby · open · Updated 2026-10-09T09:07:55Z
**Status: Unreviewed / maintainer-seeded research question**
**Original source:** [Any resources on tracking and measuring Performance?](https://www.reddit.com/r/AI_Agents/comments/1v5q9cn/any_resources_on_tracking_and_measuring/) (r/AI_Agents).
The source is a quality-management question about defining agent performance measures before an implementation rollout. It highlights the need to include monitoring in the plan. We are treating that question as motivation, not as evidence that any measurement scheme has been validated.
**Question to investigate:** What minimal scorecard separates an agent's claimed completion from externally accepted outcomes, while counting the work humans must do afterward?
**First feasible contribution:** Propose an acceptance rubric and a tiny synthetic task set for one workflow, such as extracting specified fields from documents with known answers. Include at least one plausible but incorrect output and one output that requires human correction. Define what counts as an attempt, retry, accepted task, and intervention.
**Proposed comparison:** Score the same outputs using agent-reported completion and independently checked acceptance. Add a manual-work baseline if a volunteer actually measures one; do not invent timing data. Track correction time separately from waiting time.
**Evidence criteria:** Publish inputs, outputs, expected answers, evaluator rules, and a per-task ledger. Report accepted outcomes / all assigned tasks, false completion claims, human correction minutes, latency, and total measured cost per accepted outcome. Include failed and abandoned tasks in the denominator, disclose missing measurements, and show sample size. If human judgment is needed, record disagreements and resolution rules.
At initial seeding, no experiment or baseline measurement had been run for this issue. A later synthetic scoring contribution is linked below; no real model or human-time baseline has been measured. The rubric is a maintainer proposal, and the original poster is not represented as a participant or endorser.
To start, contribute five synthetic cases and explicit acceptance checks, or identify a denominator that could make this scorecard misleading.
Prepared with AI assistance by the maintainer. Source accessed: 2026-10-09.
## Maintainer-organized simulation update — 2026-10-09
A separate Codex researcher session contributed an actual, narrowly scoped synthetic study using the same maintainer account. This is not external participation or an official accepted report. [Draft contribution](https://github.com/mbabby/agent-research-commons/issues/29#issuecomment-6076987865) · [Exercise results, review and limitations](https://github.com/mbabby/agent-research-commons/issues/31). Independent-session reproduction passed; see the linked review for exact scope. The broader research question stays open.
## Help wanted now
Contribute prospective records of attempts and independently checked outcomes, or test the denominator rules on another workflow. Human correction effort, latency and cost need actual measurements; the synthetic example supplies none.
Parallel contributions are welcome. An optional intent does not reserve this question. Use the [help-needed board](https://mbabby.github.io/agent-research-commons/community/needs.html) and [contribution guide](https://mbabby.github.io/agent-research-commons/collaboration-guide.md) to submit your own fixed-version artifact or scoped feedback. Existing simulation comments remain historical discussion, not automatically imported endorsements or credit.