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Add Fibonacci Pipeline blog post (60/30/10 ratio for agent pipelines)
Standalone post on integrating AI agents into enterprise operations: 60% deterministic code, 30% LLM reasoning, 10% human judgment. Credits Nate Herk and AI Automation Society. Unlisted for review. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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‎src/pages/blog/fibonacci-pipeline.astro‎

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import BaseLayout from '../../layouts/BaseLayout.astro';
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---
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<BaseLayout
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title="The Fibonacci Pipeline: How Much of Your Business Should an Agent Run? ~/stubborncoder"
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description="A simple ratio for integrating AI agents into enterprise operations: 60% deterministic code, 30% LLM reasoning, 10% human judgment."
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activeLink="writing"
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>
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<section class="post-hero">
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<div class="post-hero-bg"></div>
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<div class="post-hero-overlay"></div>
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<div class="post-hero-content wrap">
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<a href="/#writing" class="post-back reveal">&#8592; Back to writing</a>
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<div class="post-meta-top reveal">
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<span>April 2026</span>
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<span>&middot;</span>
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<span>6 min read</span>
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</div>
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<h1 class="reveal">The <span class="em">Fibonacci Pipeline</span></h1>
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<p class="post-subtitle reveal">
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How much of a business process should an agent actually run?
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There's a ratio that works. And it's not 100%.
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</p>
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<div class="post-tags-row reveal">
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<span class="post-tag post-tag--ember">AI Agents</span>
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<span class="post-tag post-tag--gold">Enterprise</span>
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<span class="post-tag">Pipeline Design</span>
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<span class="post-tag">Architecture</span>
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</div>
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</div>
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</section>
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<main class="wrap">
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<article class="article">
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<h2 class="reveal">The question nobody's asking</h2>
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<p class="reveal">
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Everyone's talking about what agents can do. Very few people are asking how
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much of a business process they <em>should</em> do.
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</p>
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<p class="reveal">
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There's a temptation, especially now, to hand the whole thing over. Read the
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data, make the decision, take the action, done. Full autonomy. But if you've
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ever built something that runs inside a real company, you know that's not how
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trust works. Finance teams don't hand over the keys because your demo looked
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good. Operations teams don't let code approve purchase orders because it
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passed a benchmark.
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</p>
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<p class="reveal">
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So you need a ratio. How much is code, how much is model, how much is human.
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And it turns out there's one that works.
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</p>
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<h2 class="reveal">60 / 30 / 10</h2>
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<div class="ratio-visual reveal">
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<div class="ratio-bar">
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<div class="ratio-segment ratio-segment--code" style="flex: 6;">
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<span class="ratio-pct">60%</span>
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<span class="ratio-label">Deterministic</span>
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</div>
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<div class="ratio-segment ratio-segment--llm" style="flex: 3;">
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<span class="ratio-pct">30%</span>
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<span class="ratio-label">LLM</span>
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</div>
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<div class="ratio-segment ratio-segment--human" style="flex: 1;">
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<span class="ratio-pct">10%</span>
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<span class="ratio-label">Human</span>
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</div>
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</div>
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</div>
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<p class="reveal">
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<strong>60% deterministic.</strong> Code. Rules. Arithmetic. Structured queries.
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The stuff that should never hallucinate because it never involves a model.
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Matching a PO against an invoice is math. Checking a threshold is a comparison.
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Validating a field is a regex. These steps are the backbone of the pipeline
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and they need to be rock solid.
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</p>
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<p class="reveal">
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<strong>30% LLM-assisted.</strong> This is where the model earns its keep.
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Reasoning about context. Classifying things that don't fit neatly into rules.
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Drafting communication that needs to sound human. Weighing materiality when the
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numbers alone don't tell the full story. You want the model here because these
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are the steps where code alone falls short.
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</p>
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<p class="reveal">
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<strong>10% human.</strong> Approve. Override. Make the final call on the edge
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cases. This isn't busywork, it's the part that keeps the business accountable.
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A human looks at what the pipeline produced, confirms it makes sense, and hits
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go. Or doesn't, and says why.
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</p>
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<h2 class="reveal">Why this ratio works</h2>
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<p class="reveal">
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It's not magic. It's about matching each step to whatever does it best.
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</p>
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<p class="reveal">
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The deterministic 60% gives you <em>reliability</em>. These steps produce the
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same output every time for the same input. No temperature, no prompt
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sensitivity, no "it worked yesterday but not today." When something goes wrong,
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you can trace it. When an auditor asks what happened, you can show them exactly.
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</p>
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<p class="reveal">
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The LLM 30% gives you <em>intelligence</em>. These are the steps that used to
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require a person not because they were hard, but because they needed judgment.
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"Is this price variance a mistake or a renegotiated contract?" "Should we
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escalate this or wait?" "How do I write this email so the vendor actually fixes
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it?" Code can't do that. Models can, if you scope them right.
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</p>
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<p class="reveal">
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The human 10% gives you <em>trust</em>. The pipeline doesn't run off into the
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dark. Someone is in the loop, but only for the decisions that matter. They're
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not reviewing every invoice or reading every email. They're reviewing the ones
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the agent flagged as interesting.
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</p>
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<blockquote class="reveal">
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<p>The goal isn't to remove people from the process. It's to stop wasting them on the parts that don't need them.</p>
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</blockquote>
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<h2 class="reveal">What happens when the ratio is off</h2>
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<p class="reveal">
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Too much LLM and not enough deterministic? You get inconsistency. The same
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invoice gets different results on Tuesday than it got on Monday. Your audit
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trail becomes "the model thought this was fine." Good luck explaining that to
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compliance.
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</p>
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<p class="reveal">
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Too much deterministic and not enough LLM? You're back to where you were five
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years ago. A rules engine that breaks every time reality doesn't fit the rules.
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Nobody can explain the edge cases because nobody built for them.
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</p>
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<p class="reveal">
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Too much human and not enough automation? Then you haven't actually automated
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anything. You've just added a dashboard to someone's already full day. The whole
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point was to free up the team for the work that actually needs their brain.
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</p>
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<h2 class="reveal">How to think about it for your process</h2>
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<p class="reveal">
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Take any business process and break it into steps. For each step, ask one
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question: <em>does this need judgment or just execution?</em>
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</p>
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<p class="reveal">
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If it's execution, make it deterministic. Write it in code. Test it. Don't
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involve a model.
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</p>
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<p class="reveal">
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If it needs judgment, ask a second question: <em>is the judgment routine or
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genuinely complex?</em> Routine judgment (classify this, draft that, pick from
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these options) is the model's territory. Complex judgment (approve a &euro;500K
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exception, override a vendor relationship decision) stays with a person.
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</p>
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<p class="reveal">
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You'll find that most processes naturally land around 60/30/10. Not exactly,
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because the numbers aren't the point. The principle is: <strong>build the
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pipeline around code, use the model where it adds something code can't, and
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keep humans on the decisions that carry real weight.</strong>
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</p>
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<h2 class="reveal">Credit where it's due</h2>
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<p class="reveal">
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This idea crystallized for me after conversations with
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<a href="https://www.skool.com/ai-automation-society/about" target="_blank" class="inline-link">Nate Herk</a>
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and the AI Automation Society community. The framing of agents as participants
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in a larger flow, not replacements for the flow, is something I keep coming
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back to when designing systems for enterprise clients. The Fibonacci name is
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mine, the intuition behind the ratio is shared.
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</p>
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<div class="article-footer reveal">
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<div class="article-footer-label">Connect</div>
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<div class="resource-links">
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<a href="https://linkedin.com/in/daviddiaztrapero" target="_blank" class="original-link">LinkedIn &#8594;</a>
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<a href="https://www.skool.com/ai-automation-society/about" target="_blank" class="original-link">AI Automation Society &#8594;</a>
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</div>
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</div>
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</article>
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<section class="more-posts">
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<div class="more-posts-title">More writing</div>
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<a href="/blog/invoice-verification-agent" class="more-row">
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<span class="more-date">2026.04</span>
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<span class="more-title">Let AP Clerks Do the Work That Needs a Human</span>
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<span class="more-src">CFO Living Examples, Part 2</span>
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</a>
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<a href="/blog/ar-collections-agent" class="more-row">
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<span class="more-date">2026.04</span>
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<span class="more-title">Your AR Team Just Got an Agent That Never Sleeps</span>
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<span class="more-src">CFO Living Examples, Part 1</span>
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</a>
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<a href="/blog/multimodal-embeddings" class="more-row">
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<span class="more-date">2026.04</span>
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<span class="more-title">Multimodal Embeddings: One Model, One Vector Space</span>
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<span class="more-src">LinkedIn</span>
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</a>
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</section>
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</main>
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</BaseLayout>
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