enterprise-workflows

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#enterprise-workflows

ThinkingBox: Solving an agent task once vs. solving it 20/20: 507 stateful workflows graded on terminal database state [R]

Reddit r/MachineLearning ↗ · 2026-10-09

微软发布 ThinkingBox-Bench,包含 507 个状态化企业工作流任务,每个任务独立运行 20 次并以终端数据库状态评分,揭示 pass@20 与 all-20 排名几乎完全相反,且 67% 的失败轨迹在完成式代理下会被误判为成功。研究显示发现能力与可重复性对模型的排名差异巨大,Kimi-K3 覆盖最广但重复率低,Claude Opus 5 重复稳定性更高。

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#enterprise-workflows

GDPevo: Evaluating Agent Self-Evolution on Real Business Tasks

Hugging Face Daily Papers ↗ · 2026-08-04 Cached

GDPevo is a benchmark for evaluating agent self-evolution on real business tasks, covering CRM, ERP, finance, healthcare, legal, and data-centric workflows. The authors release an automated pipeline and find that self-evolution improves held-out accuracy by up to 16.44 percentage points, though agents remain well below an oracle ceiling.

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#enterprise-workflows

@alibaba_cloud: We’re excited to integrate TinyFish’s Mako execution engine into Alibaba Cloud’s desktop agent—bringing a web-native, w…

X AI KOLs Timeline ↗ · 2026-07-24 Cached

Alibaba Cloud integrates TinyFish's Mako execution engine into its desktop agent to deliver a web-native, workflow-trained intelligence layer for handling complex multi-step tasks with lower latency and fewer stalls.

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#enterprise-workflows

@OpenAI: GPT‑5.6 improves artifact quality across presentations, documents, and spreadsheets, and works better with your templat…

X AI KOLs ↗ · 2026-07-09 Cached

OpenAI's GPT-5.6 update improves artifact quality across presentations, documents, and spreadsheets, enhancing template compatibility and enterprise workflow integration.

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#enterprise-workflows

@googledevs: Most agent tutorials stop at stateless agents. Real workflows run for weeks. Build long-running AI agents that pause fo…

X AI KOLs Following ↗ · 2026-05-29 Cached

A tutorial from Google on building long-running AI agents that can pause for days, survive restarts, and resume without losing context using the Agent Development Kit (ADK), with code and step-by-step guidance for enterprise workflows like new hire onboarding.

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#enterprise-workflows

Anchor: Mitigating Artifact Drift in Agent Benchmark Generation

arXiv cs.AI ↗ · 2026-05-27 Cached

Anchor is a task-generation pipeline that addresses artifact drift in AI agent benchmarks by jointly producing instructions, environments, solutions, and verifiers from a single constraint optimization specification, yielding consistent and auditable evaluation tasks for enterprise workflows. The paper introduces ERP-Bench, a benchmark of 300 long-horizon tasks in a production ERP system, showing that frontier models satisfy explicit constraints in 26.1% of trials but reach optimal solutions in only 17.4%.

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