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Developer's AI agent dependency classifier misses all actual dependencies

By PulseAugur Editorial · [1 sources] ·

A developer tested their own classifier designed to identify dependencies between tool calls in an AI agent, finding that the classifier incorrectly identified 5.9% of pairs as dependent. Upon manual review of 40 pairs, 30 were found to be dependent, meaning the classifier missed 100% of these actual dependencies. The developer highlights that a test can be specific yet blind, and the control group's low error rate did not accurately reflect the classifier's performance. AI

IMPACT Highlights potential pitfalls in evaluating AI agent logic and the need for robust manual auditing.

RANK_REASON Developer's self-built classifier for AI agent tool call dependencies.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Developer's AI agent dependency classifier misses all actual dependencies

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Developer's self-built classifier for AI agent tool call dependencies.
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1 days old
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · The Agent Loop ·

    40 hand labels: my batching test caught 0 of 30 dependencies

    <p>A classifier with a clean control told me that 5.9% of my agent's same-tool call pairs were dependent, which left 94% of them unproven. Before publishing that, I hand-labeled 40 of the pairs. Thirty were dependent. The classifier caught none of them.</p> <p>The classifier was …

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