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    <title>Ilmora Blog — Engineering Enterprise AI</title>
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    <description>Technical writing from Ilmora&apos;s engineers and researchers: agentic AI, generative AI, MLOps, data engineering, cloud, and enterprise automation.</description>
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      <title>Designing Guardrails for Production Agentic AI Systems</title>
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      <description>A layered architecture for constraining autonomous agents in production: tool permissioning, plan validation, budget enforcement, human checkpoints, and post-hoc audit — with concrete failure modes each layer prevents.</description>
      <pubDate>Tue, 30 Jun 2026 00:00:00 GMT</pubDate>
      <category>Agentic AI</category>
      <dc:creator>Daniel Reyes</dc:creator>
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      <title>RAG vs. Fine-Tuning: An Enterprise Decision Framework</title>
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      <description>When to retrieve, when to fine-tune, and when to do both — a decision framework based on knowledge volatility, behavior specificity, latency budgets, and total cost of ownership, with numbers from production deployments.</description>
      <pubDate>Tue, 09 Jun 2026 00:00:00 GMT</pubDate>
      <category>Generative AI</category>
      <dc:creator>Lena Vogel</dc:creator>
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      <title>LLM Observability: What to Measure Before Your Users Do</title>
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      <description>A practical observability stack for LLM applications: structured traces, online quality signals, drift detection, and evaluation suites that catch regressions before deployment — with the specific metrics that matter.</description>
      <pubDate>Tue, 19 May 2026 00:00:00 GMT</pubDate>
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      <dc:creator>Lena Vogel</dc:creator>
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      <title>From RPA to Agentic Automation: A Migration Path That Doesn&apos;t Break the Back Office</title>
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      <description>How to evolve a brittle RPA estate into agentic automation without a risky rewrite: triaging bots by failure economics, an API-first interception layer, and where LLM agents genuinely outperform deterministic scripts.</description>
      <pubDate>Tue, 28 Apr 2026 00:00:00 GMT</pubDate>
      <category>Automation</category>
      <dc:creator>Sanjay Mehta</dc:creator>
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      <title>Cloud Architecture for AI Workloads: Engineering the Cost Curve</title>
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      <description>Why AI workloads break traditional cloud cost models, and the architecture decisions that fix it: serving-tier design, GPU capacity strategy, caching layers that cut inference spend 40-70%, and a FinOps model for tokens.</description>
      <pubDate>Tue, 31 Mar 2026 00:00:00 GMT</pubDate>
      <category>Cloud</category>
      <dc:creator>Sanjay Mehta</dc:creator>
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      <title>Machine Learning Models That Survive Production: An MLOps Field Guide</title>
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      <description>Why most ML value is lost after deployment, not before — training-serving skew, feature stores, shadow deployment, drift monitoring, and retraining triggers, drawn from operating models in regulated enterprises.</description>
      <pubDate>Tue, 10 Mar 2026 00:00:00 GMT</pubDate>
      <category>Machine Learning</category>
      <dc:creator>Lena Vogel</dc:creator>
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      <title>Data Contracts for Reliable AI Pipelines</title>
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      <description>AI systems fail at the data layer more than the model layer. How data contracts — schema, semantics, SLAs, and enforcement — stop upstream changes from silently corrupting features, RAG corpora, and training sets.</description>
      <pubDate>Tue, 17 Feb 2026 00:00:00 GMT</pubDate>
      <category>Data Engineering</category>
      <dc:creator>Sanjay Mehta</dc:creator>
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      <title>Event-Driven Architecture for Enterprise Integration: Patterns That Scale, Traps That Don&apos;t</title>
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      <description>A field guide to event-driven integration at enterprise scale: event design and versioning, outbox and saga patterns, exactly-once myths, and why your event backbone is becoming the substrate for AI and agentic systems.</description>
      <pubDate>Tue, 27 Jan 2026 00:00:00 GMT</pubDate>
      <category>Software Engineering</category>
      <dc:creator>Daniel Reyes</dc:creator>
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