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    <title>Interpret AI Blog</title>
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    <description>Automate AI agent debugging, annotation, and root cause analysis. The complete platform to turn black-box failures into actionable insights and deploy agents with confidence.</description>
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      <title>AEC Evals fills the missing agentic BIM layer</title>
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      <pubDate>Thu, 14 May 2026 02:18:31 GMT</pubDate>
      <description><![CDATA[A/E firms lose millions annually on unbillable code-review rework across data centers, fabs, and pharma plants. This post breaks down the margin math and introduces AEC Evals, an AI agent that reasons over BIM models and building codes together.]]></description>
      <author>hello@interpretai.tech (Gabriele Sorrento)</author>
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      <pubDate>Mon, 11 May 2026 21:19:00 GMT</pubDate>
      <description><![CDATA[Explore how matching and recommendation systems are evolving from multistage pipelines to generative models and what the emerging agentic layer means for e-commerce, hiring, dating, and insurance.]]></description>
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      <title>Continuously Improving Agents</title>
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      <pubDate>Mon, 04 May 2026 17:28:00 GMT</pubDate>
      <description><![CDATA[Continuous improvement for agents requires a flywheel that observes behavior at scale, curates traces into datasets, diagnoses failures, picks the cheapest intervention, evaluates candidates, and redeploys safely. Agent failures are behavioral and silent, not the loud exceptions traditional software produces, so the discipline lies in the weighting policy that resolves metric conflicts, the lineage that connects production traces to release decisions, and the rollback stack that covers prompts, tools, indices, and policies rather than just model weights. The companies that win the agent decade won't be the ones with the cleverest prompt or biggest fine tune; they'll be the ones whose learning infrastructure turns fastest.]]></description>
      <author>hello@interpretai.tech (ilian Herzi, Gabriele Sorrento)</author>
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      <title>RAG, RL, and the Judge You Need Before Either</title>
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      <pubDate>Fri, 24 Apr 2026 23:58:00 GMT</pubDate>
      <description><![CDATA[This post presents a practical framework for diagnosing and fixing AI agent failures by distinguishing between forgetfulness (context problems solved by RAG) and poor reasoning (behavior problems solved by RL), with LLM judges as the diagnostic layer connecting both.]]></description>
      <author>hello@interpretai.tech (ilian Herzi)</author>
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      <title>Playbook: Build Agents That Actually Don&apos;t Break</title>
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      <pubDate>Tue, 21 Apr 2026 20:45:18 GMT</pubDate>
      <description><![CDATA[A comprehensive playbook for building reliable AI agents, covering the five essential pillars: Model Context, Guardrails, Graceful Fails, Observability, and Continuous Improvement—with specific tools and strategies for each.]]></description>
      <author>hello@interpretai.tech (Gabriele Sorrento, ilian Herzi)</author>
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      <title>Silverstream AI: Achieving 95% Agent Reliability with Agent Root Cause Analysis</title>
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      <pubDate>Fri, 13 Feb 2026 02:49:00 GMT</pubDate>
      <description><![CDATA[Discover how Silverstream AI solved the 'black box' problem of agent deployment, using root cause analysis and semantic clustering to diagnose failures at scale and achieve 95% agent reliability.]]></description>
      <author>hello@interpretai.tech (Gabriele Sorrento)</author>
      <category>Case Study</category>
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      <title>Beyond Keywords: Accelerating eDiscovery</title>
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      <pubDate>Sat, 06 Dec 2025 02:41:00 GMT</pubDate>
      <description><![CDATA[Legal teams waste billions on manual document review while missing 80% of relevant evidence. AI-powered multimodal semantic search eliminates the bottleneck, finding critical documents instantly without seed set bias or training phases.]]></description>
      <author>hello@interpretai.tech (Gabriele Sorrento)</author>
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      <title>Agentic Annotations</title>
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      <pubDate>Fri, 22 Aug 2025 18:51:58 GMT</pubDate>
      <description><![CDATA[Manual annotations don't scale. Agentic Annotations automate data labeling after just a few examples, helping AI teams move from prototype to production in days instead of months, without the human bottleneck.]]></description>
      <author>hello@interpretai.tech (Interpret AI)</author>
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      <title>Data scale is NOT all you need</title>
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      <pubDate>Mon, 21 Jul 2025 21:21:41 GMT</pubDate>
      <description><![CDATA[Blindly scaling datasets puts AI companies at risk. Learn why intelligent data curation—not just more data—is the key to building safer, more effective AI models and how to properly select training data.]]></description>
      <author>hello@interpretai.tech (ilian Herzi)</author>
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