Bhanu Chaddha
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Agents in Production

A series on what actually goes into production agentic systems, past the demo and past the tutorial.

11 parts · read in order

  1. Most "AI Agents" in Production Are Demos With Extra Steps

    Part 1 of a series on what actually goes into production agentic systems.

  2. Most "Agents" Are Workflows That Got Ideas

    Part 2 of a series on what actually goes into production agentic systems.

  3. Most Agentic Projects Can't Defend Their Own ROI

    Part 3 of a series on what actually goes into production agentic systems.

  4. Most Multi-Agent Systems Shouldn't Be

    Part 4 of a series on what actually goes into production agentic systems.

  5. Part-5: ReAct Is Where You Start, Not Where You Ship

    Part 5 of a series on what actually goes into production agentic systems.

  6. Most Production RAG Is Quietly Wrong

    Part 6 of a series on what actually goes into production agentic systems.

  7. The Pipeline That Feeds Your Agent Is Broken

    Production AI data pipelines have four jobs: extraction, normalization, enrichment, indexing. Most teams build only the last one and ship stale answers.

  8. Your Tool Schema Is a Prompt, Not an API

    A tool schema is read by the model on every call, not once by an engineer. Design the contract, the errors, and the catalog size, because MCP will not.

  9. Model Routing Is an Optimisation, Not an Architecture

    Routing is a cost optimisation, not day-one architecture. Build it once you have production data showing which requests a cheaper model actually handles well.

  10. A Prompt Edit Is a Deploy. Treat It Like One.

    A prompt edit changes what your agent does for every user, just like code. Give it review, tests, staged rollout, and a rollback button, before it needs one.

  11. Your LLM Judge Is Not Neutral. It Has Opinions You Never Asked For.

    LLM judges have measured biases toward length and confident wrong answers. Check yours against real human labels before it gates anything you actually ship.

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