Agentic Flakiness: conceptual agent-reliability evaluation and reporting
An indexed publisher abstract connects flaky-test analysis with repeated-task reliability in LLM-agent evaluation and advocates publishing per-task trial counts. This is a bounded conceptual-method recommendation. Full proofs, fitted models and quantitative claims were not independently checked.
Workflow level n/a Source: Peer-reviewed source Outcome: Not empirically evaluated here
Research applicability
An informative indexed publisher abstract describes a conceptual agent-evaluation/reporting method connecting flaky-test analysis with repeated-task reliability and advocating per-task trial reporting. The bounded method is useful without requiring execution or proof reproduction; retain the access/provenance and workflow gaps explicitly.
Getting started
Status: Untested AI editorial adoption suggestion; not an executed recipe Steps: - Read the publisher abstract and obtain full methods lawfully before relying on theorem or estimator details - For any future agent evaluation, preserve per-task trial counts and task conditions - Validate definitions, assumptions and estimator uncertainty before reporting reproducibility claims
Maintenance
Dated source snapshot only; present availability, maintenance and version parity require separate checks.
License
Source visibility is not reuse clearance; source/model/data licenses and rights require separate checking.
Workflow & human oversight
Scope: Abstract-described conceptual agent-evaluation/reporting method Human control: Specific goal-setting/checkpoint/output-validation arrangements are not established by the abstract. Future researcher checks are editorial suggestions. Stage connections: Abstract connects flaky-test concepts, reliability analysis and reporting; exact executed research pipeline and human control are not documented. Stopping: Not documented; no theorem-implied operational stopping rule inferred. Missing evidence: Concrete research-assistant actions and researcher orchestration/validation sufficient for L1–L4; exact full methods needed for quantitative adoption.
Workflow level is not an evidence score; higher is not better evidence.
Approach
- Agents are evaluated as task-performing systems; an AI research-assistant role and controls are not established in inspected abstract.
Outcomes (as reported by the originating team)
Documented method or intended capability; no empirical research benefit established by this review.
Limitations
- Indexed publisher abstract only; direct DOI/publisher reads unavailable, no PDF or full proof inspected. - No theorem, latent-model fit, Flake Gap value or numerical reliability improvement independently verified or adopted. - Formula encoding is imperfect in retrieved abstract; no silent normalization or equation-based claim made. - Proposed evaluation/reporting method is distinct from a demonstrated tool-using research assistant; no workflow level forced. - No positive evaluation, autonomy, installation or open-data prerequisite introduced.
Reproducibility
AI editorial source review only. No candidate code, model, agent, experiment or reported outcome independently executed or reproduced.
Adoption requirements
- Agent-evaluation and statistical expertise - Lawful access to full definitions/proofs before adopting quantitative results - No installation, API inference, benchmark execution or paid access performed
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