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  "id": "744",
  "slug": "enterprise-agents-measured-work",
  "edition": "2026-10-28T12",
  "url": "https://hangingcontext.com/stories/2026-10-28/enterprise-agents-measured-work/",
  "newsroom": "artificial-intelligence",
  "beat": "developers",
  "newsroom_title": "Artificial Intelligence",
  "angle": "Enterprise agents move from demos to measured jobs: ticket routing, governed pipelines, and benchmarks on real work.",
  "headline": "Enterprise Agents Stop Demoing and Start Doing Measured Work",
  "dek": "Vestas deployed a ticket-routing agent with human review, Dataiku launched a headless bridge for coding agents, and new benchmarks on Snowflake real pull requests and 555 CRM tasks show where agents still struggle.",
  "published_at": "2026-10-28T12:32:43Z",
  "labels": {
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    "trust_text": "Confirmed · 4 sources",
    "newsroom": "Artificial Intelligence"
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  "publishers": [
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    {
      "text": "Vestas uses a Jira Ticket Agent built on Dataiku and Snowflake to recommend the most relevant component or team, targeting a cut in ticket assignment from a 72-hour SLA to under one hour, with a human in the loop.",
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      "text": "Dataiku says Dataiku Headless lets developers use coding agents to build and modify Dataiku projects, is available as a plugin for Claude Code, Codex, Cursor, Snowflake CoCo, AWS Kiro, and OpenCode, and respects the permissions of the Dataiku API key used.",
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    {
      "text": "Snowflake says SnowPR consists of 65 merged pull requests across 11 product areas, graded by hidden tests, with Cortex Code in REM mode solving 62 of 65 tasks at 6.34 dollars per trial versus 8.19 dollars for code mode.",
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    {
      "text": "In a preprint, CRM-Agent-Bench contains 555 stateful, multi-turn tasks across nine business domains, with strict success rates for 16 evaluated systems ranging from 64.32 percent to 83.60 percent.",
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  "sections": [
    {
      "heading": "A ticket agent with a human in the loop",
      "paragraphs": [
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          "markdown": "Vestas says it has deployed a Jira Ticket Agent built on Dataiku and Snowflake to support its wind turbine software environment, analysing new tickets to recommend the most relevant component or team. Vestas processes 100 to 150 support tickets per month, and the previous ticket assignment process had a 72-hour service level agreement, while the new target for ticket assignment is less than one hour. Vestas targets a 95 to 99 percent reduction in ticket assignment time and projects up to a 40 percent reduction in overall ticket handling and lead time, though those are targets and projections reported by the company, not measured results from a completed rollout."
        },
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          "markdown": "The deployment detail that matters most is the human-in-the-loop design. The agent currently posts recommendations as comments in Jira rather than automatically changing fields, and Vestas employs a human-in-the-loop approach for ticket triage, with engineers reviewing recommendations before final action. The project combines machine learning, large language models, and retrieval-based search, and Vestas is exploring the integration of turbine logs into the support workflow. That combination of a bounded job, a measurable target, and a human checkpoint is a pattern worth noting: the agent is not being asked to run support, but to route it faster while a person stays in control of the decision."
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    {
      "heading": "Coding agents meet governed pipelines",
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          "markdown": "Dataiku says it has launched Dataiku Headless, an integration that allows developers to build and manage Dataiku projects directly from coding agents. Dataiku Headless allows developers to use coding agents to build and modify Dataiku projects, uses Cobuild to generate data pipelines, models, and dashboards, and is available as a plugin for Claude Code, Codex, Cursor, Snowflake CoCo, AWS Kiro, and OpenCode. It can be connected to coding agents using an MCP server and skills, requires Cobuild to be enabled on the Dataiku instance for building capabilities, and is designed to ensure that work generated by coding agents is visual and inspectable."
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          "markdown": "Dataiku says Headless also supports migration of legacy workflows from SAS, Alteryx, and Excel into Dataiku, includes administrative tools for investigating and managing Dataiku environments, and allows administrators to find failing jobs within a project. Dataiku Headless respects the permissions of the Dataiku API key used to connect, which is the governance detail that makes the bridge usable in an enterprise setting: the coding agent inherits the permissions of the key, rather than gaining a separate, broader access path. The pitch is not that coding agents replace governed data work, but that governed data work can be built from the coding agent a developer already uses, with the resulting pipeline still visible and reviewable in Dataiku."
        }
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    {
      "heading": "Benchmarks on real work",
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          "markdown": "Snowflake says it introduced SnowPR, a benchmark designed to evaluate coding agents against its large Java and C++ monorepo, using 65 merged pull requests across 11 product areas that are graded using hidden tests that shipped with the original pull requests. SnowPR tasks are significantly larger than typical SWE-bench tasks, with an average of 4.0 files and 262 lines changed per fix, and the monorepo contains 63,000 nontest source files and 17 million lines of nontest code. Tasks that change both Java and C++ code had a 55 percent pass rate, compared with 80 percent for Java-only tasks, and Snowflake says the difficulty of crossing the language boundary is a primary factor in agent failure."
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          "markdown": "Snowflake says Cortex Code in REM mode solved 62 of 65 tasks in SnowPR, at 6.34 dollars per trial compared with 8.19 dollars for code mode, while noting that REM mode is currently internal only and not available to customers. Snowflake plans to add rubric-based language-model judges to evaluate design and explanation quality in future versions. In a separate preprint, CRM-Agent-Bench evaluates language agents on multi-turn business tasks in CRM environments using contract-based success criteria, assessing outcome, trajectory, viability, and compliance across 555 stateful, multi-turn tasks spanning nine business domains and 36 capability tags. Strict success rates for the 16 evaluated systems ranged from 64.32 percent to 83.60 percent, success is defined as the strict conjunction of all task-declared checks, and leading systems diverged sharply in cost and per-domain reliability."
        }
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    },
    {
      "heading": "What measured work changes",
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          "markdown": "The common thread across these four items is measurement on real jobs rather than on generic benchmarks. Vestas measures assignment time against a 72-hour baseline, Dataiku measures whether generated work remains inspectable and permission-bound, Snowflake measures pass rates and cost per trial on real pull requests, and CRM-Agent-Bench measures strict success, latency, and cost across business domains. That shift matters because enterprise buyers do not need another demonstration that an agent can do something once; they need to know how often it succeeds, what it costs per attempt, where it fails, and whether a human can still inspect and override it. The items in this edition give partial answers to each of those questions, and the gaps are instructive: Vestas reports targets, Snowflake reports an internal mode, and CRM-Agent-Bench reports a wide success range across systems."
        }
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  "unresolved": [
    "Whether Vestas reaches its under-one-hour assignment target in measured operation, not just as a target",
    "Whether Snowflake REM mode results hold for customers once it is available outside Snowflake",
    "Which CRM domains and capability tags drive the wide success range in CRM-Agent-Bench"
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      "A claim that any named agent system is production-ready for a specific enterprise",
      "A cost comparison beyond the per-trial figures Snowflake reports"
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      "Enterprise leaders scoping agent deployments with human review and measurable targets",
      "Platform teams bridging coding agents into governed data systems"
    ],
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    {
      "url": "https://hangingcontext.com/stories/2026-10-28/coding-agents-measured-not-shipped/",
      "headline": "Coding agents get measured: FrontierCode 2.0 tops out at 45 percent while teams learn to evaluate their own mods",
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      "url": "https://hangingcontext.com/stories/2026-10-28/dualchem-safety-gap/",
      "headline": "Models Know the Risk and Answer Anyway: The New Safety Gap in Chemistry and Robotics",
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      "url": "https://hangingcontext.com/stories/2026-10-28/ai-accountability-deployment-playbook/",
      "headline": "The Accountability Playbook: Assign Responsibility Before AI Scales",
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      "headline": "Intel Builds Its Enterprise Agent Pitch From the Warehouse to the Cloud",
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    "attribution": "Hanging Context, with provenance by Synorb",
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