AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflows

• URL: http://arxiv.org/abs/2607.16345v2
• Date: Tue, 21 Jul 2026 00:27:08 GMT
• Title: AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflows
• Abstract summary: AEVAL (Agentic Evaluation) is a CI-integrated framework that replaces this practice with a deterministic, reproducible test pipeline for agentic skills.<n>Every skill change triggers a test event: the skill runs against a developer declared evaluation contract inside an automated executor.<n>A key ingredient is a structural separation between executor and grader, preventing a subtle but pervasive failure mode.
• Score: 1.6785141970297952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern agentic systems increasingly rely on skills: installable packages of natural language and code that teach an LLM agent to perform a domain task. As skill repositories grow, developers need automated quality signals on every change, yet evaluation today is largely anecdotal: a developer asks an agent to "try the skill," watches a demo, and forms a subjective impression. This yields neither reproducibility across runs nor comparability across versions, and scales poorly to marketplaces where one regression can silently break dozens of downstream workflows. We present AEVAL (Agentic Evaluation), a CI-integrated framework that replaces this practice with a deterministic, reproducible test pipeline for agentic skills. Every skill change triggers a test event: the skill runs against a developer declared evaluation contract inside an automated executor, emitting a structured, evidence grounded quality signal that downstream CI can route on. A key ingredient is a structural separation between executor and grader, preventing a subtle but pervasive failure mode: an agent that silently self-corrects during execution and then grades its own patched outputs as passing. Our contributions are: (i) a deterministic, change-triggered evaluation protocol with per-skill contracts and per-run artifact schemas; (ii) a formalization of self correction bias as a distinct failure mode of naive agentic evaluators; (iii) an executor/grader separation with a first-attempt grading rule and explicit self-correction tracking; and (iv) a tiered, grounded evidence fix suggestion scheme (LV1 causal, LV2 quality) posted as inline merge-request comments. Validated on real skills in a production agentic stack across multiple agent SDKs, AEVAL converts spurious 100% pass rates into reproducible first-attempt fail signals with an auditable record of every executor fix.

Related papers

• HarnessEvolve: Learning from Reference Trajectories for Reliable Agent Self-Evolution [11.431479238875712]
  We introduce HarnessEvolve, a self-evolving framework that learns from reference trajectories to achieve reliable agent self-evolution.<n>We conduct extensive experiments on several benchmarks spanning open-domain and enterprise scenarios, using different models and agent frameworks.<n>Results demonstrate that HarnessEvolve consistently outperforms state-of-the-art baselines across all benchmarks and settings.
  arXiv  Detail & Related papers  (2026-09-01T07:31:18Z)
• VeriSkill: A Self-Evolution Framework for Program Verification Skills [19.114016708981023]
  We propose VeriSkill, a self-evolution framework for program verification.<n>It attributes verification failures to skill deficiencies, distills diagnostic signatures into reusable lessons, and iteratively refines candidate skills.
  arXiv  Detail & Related papers  (2026-07-30T06:15:29Z)
• Self-Authored Verification Is Unreliable in Heuristic Self-Improving Agents [5.91817208489443]
  We study the verifier-acceptance-deployment gap.<n>This gap refers to the discrepancy between an agent's self-authored verification signal and a sealed deployment evaluation.<n>We introduce a Sealed Exogenous Acceptance Loop (SEAL)
  arXiv  Detail & Related papers  (2026-07-27T11:45:14Z)
• TTHE: Test-Time Harness Evolution [50.26245555541721]
  Existing approaches optimize such harnesses before deployment, searching training or development data for a fixed agent workflow that is then frozen at test time.<n>We ask whether the harness can instead be optimized during evaluation itself, using only the unlabeled execution traces the agent produces on the test inputs.<n>During evaluation, TTHE maintains a population of candidate harnesses and refines them through an agentic proposer that reasons over their execution traces.<n>A judge then commits an improved harness from execution-derived proxy signals, and the selected program persists to govern subsequent inputs.
  arXiv  Detail & Related papers  (2026-07-09T05:53:39Z)
• AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems [82.01232437066487]
  AgentX is a production-deployed multi-agent system that restructures this production function.<n>It autonomously generates, implements, evaluates, and learns from recommendation experiments.<n>AgentX orchestrates four tightly coupled stages in a closed loop.
  arXiv  Detail & Related papers  (2026-06-25T10:42:28Z)
• SkillAudit: Ground-Truth-Free Skill Evolution via Paired Trajectory Auditing [81.51044612408793]
  SkillAudit is a framework for evolving agent skills without ground-truth feedback.<n>To turn behavioral differences into edit guidance, SkillAudit uses Process-Aligned Contrastive Evaluation.<n>It routes edits through two pipelines: Refine removes noisy or irrelevant guidance from broadly useful skills, while Repair replaces passages that conflict with the task.
  arXiv  Detail & Related papers  (2026-06-12T08:20:09Z)
• VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents [57.240036084348354]
  We introduce VASO, a framework for verification-guided self-evolution of robot skill contracts.<n>VASO verifies logically inconsistent skill contracts, then verifies plans induced by the skill against global and local temporal specifications.<n>On Clearpath Jackal and PX4 quadcopter tasks, VASO reaches 97.2% formal-specification compliance using fewer than 100 optimization samples.
  arXiv  Detail & Related papers  (2026-06-03T20:02:35Z)
• MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems [36.221246203666745]
  MOSS is a system that performs self-rewriting at the source level on production agentic substrates.<n>It lifts a four-task mean grader score from 0.25 to 0.61 in a single cycle without human intervention.
  arXiv  Detail & Related papers  (2026-05-21T17:48:33Z)
• Trace2Skill: Verifier-Guided Skill Evolution for Long-Context EDA Agents [0.3733676450456031]
  We present Trace2Skill, a test-time scaling framework that improves a hardware agent without RTL-specialized model fine-tuning.<n>Rather than training a new model or only sampling more candidate solutions, Trace2Skill treats the agent's natural-language skill as an evolvable policy.<n>It mines repeated rollout traces for success and failure modes, converts them into dense diagnostics and oracle lessons, and uses an oracle, mutator, and selector loop to produce task-specific skills.
  arXiv  Detail & Related papers  (2026-05-20T23:10:49Z)
• Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
  Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
  arXiv  Detail & Related papers  (2026-04-21T15:05:58Z)
• Veri-Sure: A Contract-Aware Multi-Agent Framework with Temporal Tracing and Formal Verification for Correct RTL Code Generation [4.723302382132762]
  silicon-grade correctness remains bottlenecked by: (i) limited test coverage and reliability of simulation-centric evaluation, (ii) regressions and repair hallucinations, and (iii) semantic drift as intent is reinterpreted across agent handoffs.<n>We propose Veri-Sure, a multi-agent framework that establishes a design contract to align agents' intent and uses a patching mechanism guided by static dependency slicing to perform precise, localized repairs.
  arXiv  Detail & Related papers  (2026-01-27T16:10:23Z)
• AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering [8.201374511929538]
  AgentDevel is a release engineering pipeline that iteratively runs the current agent.<n>It produces implementation-blind, symptom-level quality signals from execution traces.<n>It aggregates dominant symptom patterns and produces auditable engineering specifications.
  arXiv  Detail & Related papers  (2026-01-08T05:49:01Z)
• Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agents [58.00130492861884]
  TraitBasis is a lightweight, model-agnostic method for systematically stress testing AI agents.<n>TraitBasis learns directions in activation space corresponding to steerable user traits.<n>We observe on average a 2%-30% performance degradation on $tau$-Trait across frontier models.
  arXiv  Detail & Related papers  (2025-10-06T05:03:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.