Data Workers (Autonomous Agentic Data Platform)

Agents de données spécialisés, chacun étant un serveur MCP, qui construisent des pipelines, déboguent les incidents, surveillent la qualité des données et gèrent les migrations sur Snowflake, Databricks et BigQuery, avec des écritures gouvernées.

Voir sur dataworkers.io

Documentation

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<title>Data Workers, the Autonomous Agentic Data Platform</title>

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dataworkers
Who it’s for
Who it’s for
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Autonomous agentic data platform

Your data platform,
run by an
agentic workforce.

Introducing the Data Factory 2.0. Specialised data agents for every domain, connected by a living context graph, data teams running autonomously, and you in full control.

  • Autonomous
    operations
  • Agentic
    enterprise
  • Institutional
    memory
    <div class="dwc-input">
      <div class="dwc-ta">brief me on new insights and incident fixes to ads_data this week</div>
    </div>
    <div class="dwc-rail">
      <button class="dwc-mode" type="button"><span class="g">✦</span> data-agents swarm</button>
      <button class="dwc-mode" type="button"><span class="g">◆</span> context wizard</button>
      <button class="dwc-mode" type="button"><span class="g">⌕</span> agentic search</button>
      <button class="dwc-mode" type="button"><span class="g">⌁</span> build pipeline</button>
      <button class="dwc-mode" type="button"><span class="g">⊕</span> review</button>
      <span class="dwc-flex"></span>
      <button class="dwc-icon" type="button" title="attach · GitHub codebase or files">
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      </button>
      <button class="dwc-icon" type="button" title="dictate (Web Speech API)">
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      </button>
      <button class="dwc-send" type="button" title="send (⏎)">
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    </div>
    
    <div class="dwc-foot cfoot-b">
      <div class="dwc-foot-head">
        <span class="dwc-status-wrap"><span class="dwc-dot"></span><span class="dwc-live-status" id="dwStatus">Data Agents Working</span></span>
        <span class="dwc-flex"></span>
        <span class="dwc-counts">
          <span class="dwc-live-meta"><b id="dwAgents">1</b> agents</span>
          <span class="dwc-live-meta"><b id="dwTools">2</b> steps</span>
        </span>
      </div>
      <div class="dwc-tick" id="dwTick" style="opacity: 1;"><span class="ag">Incident Debugging</span> orders_daily freshness lag is 6h12m, past the 30m SLA · <span class="ok">breached</span></div>
    </div>
  </div>

<a class="dwc-cover" href="/book-demo/" aria-label="Book a demo"></a>
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By the Numbers

Unlock 8.9x more agentic, automated data operations.
4.1x developer productivity for data teams.

The move to specialized agents is already underway. General coding agents opened the door. Data agents walked through it. Fully autonomous data ops is where it lands, and it’s what we build for.

<div class="wfall" style="display:grid;grid-template-columns:minmax(0,.38fr) minmax(0,.62fr);gap:30px;margin-top:34px;align-items:stretch">
<div class="fig-box rv rvw rv-w1 in" style="display:flex;flex-direction:column;min-height:544px;padding:28px 28px 28px">
        <div style="margin:0 0 24px"><div style="font-family:var(--sans);font-size:19px;font-weight:700;letter-spacing:-.01em;color:var(--ink);line-height:1.25">State-of-the-art data agents,<br>at the top of the benchmarks.</div></div>
  <div style="flex:1;display:flex;flex-direction:column;justify-content:center"><svg viewBox="0 0 478 378" width="100%" style="display:block" font-family="var(--mono)" aria-label="Data-agent benchmark task-success scores by agent class">
    <g font-family="var(--sans)" font-size="15" font-weight="600" fill="var(--ink)">
      <text x="4" y="20">Data Workers <tspan font-family="var(--mono)" font-size="10" font-weight="400" fill="var(--ink-3)" letter-spacing="0.5">END-TO-END PLATFORM</tspan></text>
      <text x="4" y="118">Standalone data agents <tspan font-family="var(--mono)" font-size="10" font-weight="400" fill="var(--ink-3)" letter-spacing="0.5">OTHER PROVIDERS</tspan></text>
      <text x="4" y="216">Coding agents <tspan font-family="var(--mono)" font-size="10" font-weight="400" fill="var(--ink-3)" letter-spacing="0.5">OFF-THE-SHELF</tspan></text>
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      <text x="356" y="65" fill="var(--dw-deep)">74.4</text>
      <text x="283" y="163" fill="var(--ink-2)">58.6</text>
      <text x="203" y="261" fill="var(--ink-2)">41.2</text>
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      <text x="8" y="344">0</text>
      <text x="96" y="344">20</text>
      <text x="188" y="344">40</text>
      <text x="280" y="344">60</text>
      <text x="372" y="344">80</text>
      <text x="464" y="344">100</text>
    </g>
    <text x="234" y="370" font-size="11" letter-spacing="1.5" fill="var(--ink-3)" text-anchor="middle">TASK SUCCESS (%)</text>
  </svg></div>
  <p class="cap-p" style="margin-top:auto;padding-top:16px;font-size:12px;line-height:1.45">Fig. 1: Score on DataAgentBench (DAB, UC Berkeley EPIC Data Lab): 54 tasks across 12 real-world datasets and 4 database engines.<span class="nl-cap"> Full methodology in our white paper, <a href="/whitepaper/context-is-not-all-you-need/" style="color:var(--dw-deep);text-decoration:underline;text-underline-offset:2px">Context Is Not All You Need</a>.</span></p>
</div>

<div class="fig-box rv rvw rv-w2 in" style="display:flex;flex-direction:column;min-height:544px;padding:28px 28px 28px">
  <div style="margin:0 0 14px"><div style="font-family:var(--sans);font-size:19px;font-weight:700;letter-spacing:-.01em;color:var(--ink);line-height:1.25">With our Data Factory, routine data work gets done dramatically faster.</div></div>
  <div style="flex:1;display:flex;flex-direction:column;justify-content:flex-start"><svg viewBox="-46 0 946 535" width="100%" style="display:block" font-family="var(--mono)" aria-label="Time per task, current practice vs with agents, log scale">
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        <text x="722" y="14.5">CURRENT</text>
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        <text x="806" y="14.5">WITH AGENTS</text>
      </g>
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        <text x="196" y="76">Legacy platform migration</text>
        <text x="196" y="120">Audit prep (access evidence)</text>
        <text x="196" y="164">Streaming pipeline stood up</text>
        <text x="196" y="208">Model deployed &amp; monitored</text>
        <text x="196" y="252">New pipeline built &amp; validated</text>
        <text x="196" y="296">Access provisioning</text>
        <text x="196" y="340">Schema-change migration</text>
        <text x="196" y="384">Incident root-cause &amp; fix</text>
        <text x="196" y="428">Ad-hoc data question</text>
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  <div class="fig2-m" style="display:none"><div class="f2h"><span>Routine task</span><span>With agents</span></div><div class="f2r"><span>Legacy platform migration</span><b>4–8 wk</b></div><div class="f2r"><span>Audit prep (access evidence)</span><b>10–20 h</b></div><div class="f2r"><span>Streaming pipeline stood up</span><b>2–4 h</b></div><div class="f2r"><span>Model deployed &amp; monitored</span><b>hours</b></div><div class="f2r"><span>New pipeline built &amp; validated</span><b>hours</b></div><div class="f2r"><span>Schema-change migration</span><b>15–30 min</b></div><div class="f2r"><span>Incident root-cause &amp; fix</span><b>8–15 min</b></div><div class="f2r"><span>Access provisioning</span><b>5 min</b></div><div class="f2r"><span>Finding the right dataset</span><b>30 s</b></div><div class="f2r"><span>Ad-hoc data question</span><b>seconds</b></div></div><p class="cap-p" style="margin-top:auto;padding-top:16px;font-size:12px;line-height:1.45">Fig. 2: Time to complete ten routine data tasks, current practice (black) vs. agent swarm (green). <a href="/blog/estimating-the-productivity-of-an-autonomous-agentic-data-platform/" style="color:var(--dw-deep);text-decoration:underline;text-underline-offset:2px">Read how we measure this</a>.</p>
</div>

Where the numbers come from: the benchmark method is in our white paper, Context Is Not All You Need, and the turnaround model is in Estimating the Productivity of an Autonomous Agentic Data Platform.

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“Data Workers is the future of our industry.”
Sridhar Iyer Head of Data @ Meta
Data Workers Data Factory 2.0, the end-to-end agentic data platform

Introducing Data Factory 2.0.
Now your data team runs itself.

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Data-Agents Swarm

The autonomous data-engineering workforce
  • Agents that read, write and act, fully autonomously
  • Agents and skills that get better every run
  • Every coding agent, every cloud, every data stack
Agents

Data Context Wizard

The enterprise semantic & knowledge graph
  • The context layer built for your data ecosystem
  • Brings your existing data stack together on day one
  • Governed to enterprise data-governance standards
Context

Autonomous Data-Conductor

The agent that runs the whole loop
  • Autonomous data operations, in real time
  • One agent that owns the outcome, not the step
  • Autonomy you can turn all the way up
Orchestration

Spellbook Data Catalog

The control plane for the agentic fleet
  • The new data catalog, powered by agents
  • Data assets are now awesome
  • Ask a question, get staff-level data insights
Control plane
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