Autonomous agents are increasingly deployed across sequences of related tasks, yet they often fail to retain and improve their knowledge over time. Existing approaches convert interaction trajectories into memories or reusable skills, but experience is not automatically reliable knowledge. Automatically collected skills and memories can propagate errors, mislead, and corrupt useful knowledge. To address this gap, we introduce Knowledge Evolution from Expertise and Lessons (KEEL), an agent-agnostic framework that uses verifier evidence to transform successful behavior into reusable skills and failures into corrective lessons. As new evidence accumulates, individual skill claims can be supported, contradicted, replaced, or retired. On SkillFlow benchmark, KEEL improves task success by 4.2 to 21.7 percentage points over the no-knowledge baseline, compared with gains of 2.4 to 9.6 points from SkillFlow's default method. Ablations show that skills and lessons are complementary. These results show that reliable continual agent learning depends not simply on accumulating experience, but on continually validating and evolving the knowledge derived from it. Our code is available at: https://github.com/microsoft/KEEL-TTCL26.
Figures & tables
Figure 1: Overview of KEEL. After completing each task, the agent’s trajectory is analyzed to extract reusable knowledge from its experience. The Memory Curator distills corrective lessons from observed mistakes and failures, while the Skill Curator extracts procedural skills from successful behaviors. These lessons and skills are made available to the agent and continuously accumulate and evolve across subsequent tasks, enabling continual improvement from experience.
Figure 2: Knowledge management in KEEL. The Analyzer separates task experience into positive evidence, failure evidence, and feedback on existing claims. The Curators convert positive and failure evidence into candidate skills and lessons, respectively, while deterministic managers validate all updates before modifying the persistent knowledge banks.
Figure 3: Lifecycle of a skill claim. Cross-task evidence promotes a claim, direct counterevidence quarantines it, and a grounded replacement retires the old claim while introducing a corrected claim under a new identifier.
Agent
Model
No Skills
SkillFlow
KEEL
Terminus-2
Claude Opus 4.6
44.0%
53.6% ( ↑ 9.6pp )
65.7% ( ↑ 21.7pp )
Terminus-2
GPT-5.6 Sol
59.6%
63.3% ( ↑ 3.6pp )
65.7% ( ↑ 6.0pp )
Terminus-2
GPT-5.4
39.8%
42.2% ( ↑ 2.4pp )
44.0% ( ↑ 4.2pp )
Table 1: Task pass rates for Terminus-2 across three models. Parentheses report absolute percentage point changes relative to No Skills .
Figure 4: (a) Pass rate versus average inference cost per task. Colors identify methods, and markers identify models. Lines connect results obtained with the same method. (b) Pass rate by task position within a workflow family, aggregated across the 20 families and the three models. Positions beyond eight are omitted because they are not shared by all families.
As the capability frontier of autonomous agents continues to expand, they are increasingly able to complete specialized tasks through plug-and-play external skills. Yet current benchmarks mostly test whether models can use provided skills, leaving open whether they can discover skills from experience, repair them after failure, and maintain a coherent library over time. We introduce SkillFlow, a benchmark of 166 tasks across 20 families in which task construction within each family follows a Domain-Agnostic Execution Flow (DAEF) that defines an agent workflow framework, allowing these tasks to share a consistent workflow. Agents are evaluated under an Agentic Lifelong Learning protocol in which they begin without skills, solve tasks sequentially within each family, externalize lessons through trajectory- and rubric-driven skill patches, and carry the updated library forward. Experiments reveal a substantial capability gap. For Claude Opus 4.6, lifelong skill evolution improves task success from 62.65% to 71.08% (+8.43 points). However, high skill usage does not necessarily imply high utility: Kimi K2.5 gains only +0.60 points despite 66.87% skill usage, while Qwen-Coder-Next reaches only a 44.58% task completion rate and still regresses relative to the vanilla setting. SkillFlow contributes a structured testbed for this direction and an in-depth empirical analysis of skill discovery, patching, transfer, and their failure modes under lifelong evaluation.
Ziao Zhang, Kou Shi, Shiting Huang +13
University of Science and Technology of China · University of Toronto · University of Sydney
Agent skills package specialized knowledge and workflows into reusable resources that extend AI agent capabilities. Recent work automatically discovers such skills from agent experience, which enables agents to progressively adapt through interaction. However, the insights that guide skill development typically remain scattered across optimization histories, limiting their systematic reuse across iterations. We introduce WikiSkill, a framework that co-evolves agent skills with a persistent knowledge base (wiki). At a high level, WikiSkill separates raw execution experience, accumulated knowledge, and executable skills, while continuously consolidating experience into the wiki, which subsequent skill updates can build on. Across diverse benchmarks and models, WikiSkill consistently outperforms state-of-the-art skill-evolution methods and improves over no-skill baselines in most model-benchmark settings. We find that skill evolution complements model scaling: larger models generally benefit more from evolved skills, while smaller models with skills can outperform substantially larger models without them. We also find that evolved skills transfer effectively across models and model families, and skills evolved by other models can outperform self-evolved skills. Finally, our ablation studies confirm that persistent knowledge accumulation in the wiki is critical for effective skill evolution. These results demonstrate the benefits of systematically accumulating and refining agent experience for developing reusable and transferable skills.
Large language model (LLM) agents can continually improve without parameter updates by converting historical experience into reusable procedural knowledge. However, existing methods often consolidate experience based on semantic similarity or LLM judgments, which may merge superficially related but behaviorally incompatible strategies and thereby degrade performance. To address the issue, we propose SkillCommit, an online skill evolution framework that continuously transforms experience into a hierarchical library of reusable skills. Rather than generalizing from individual experiences immediately, SkillCommit first preserves each experience as an instance-specific patch, retaining its locally validated behavior. As patches accumulate, SkillCommit abstracts those sharing a common behavioral mechanism into higher-level skills. Specifically, for each incoming skill, embedding-based retrieval first identifies candidate related skills. Cross-instance replay and an LLM-based mechanism check determine whether these skills transfer across cases and share a common underlying mechanism. Candidates that pass both checks are abstracted into a higher-level skill and committed only if it preserves the validated behavior of all constituent skills. Experiments on RuleArena, OpenExempt and KOR-Bench demonstrate that SkillCommit consistently improves agent performance across diverse domains. Moreover, the learned skills transfer across model scales and families, enabling cross-model experience transfer.
Yu He, Weikai Yang
Nanjing University · The Hong Kong University of Science and Technology (Guangzhou)