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Jerry Liu argues evals can replace hand-built agent workflows for most tasks

2 articles2 sourcessince Oct 9Last article 1h ago ·

Overview

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Jerry Liu, in an X post, argues that most tasks can now be solved by defining an eval and hillclimbing on it instead of hand-coding a deterministic or agentic workflow.

He says developers should focus on defining goals and success measures while frontier models do the work, with data providers building evals across economic activity. Liu expects agent interfaces to reduce most tasks to goals and eval instructions, but says the most complex processes will still need explicit workflow builders. This is his prediction and opinion, not a measured result. Harrison Chase later posted a piece titled "eval driven development" that frames evals as a development approach, but the post itself offers no further detail, so it adds no independent evidence for Liu's claim.

Written by AI from the articles below · updated Oct 9, 4:44 AM ET

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Developments

2 developments

  1. Oct 9, 3:50 AM ET · 1 article
    Harrison Chase on eval-driven development for AI agents
    Harrison Chase: Harrison Chase on eval-driven development for AI agents
  2. Oct 9, 1:58 AM ET · 1 article
    Jerry Liu says evals now replace hand-built agent workflows
    Jerry Liu: Jerry Liu says evals now replace hand-built agent workflows

Article timeline

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Oct 9
  1. Harrison Chase
    Harrison Chase on eval-driven development for AI agents

    AIHarrison Chase's post is titled "eval driven development," presenting evals as a development approach. The main post gives no further detail beyond the title. The quoted context from Jerry Liu argues that most tasks can be solved by defining an eval and hillclimbing over it rather than hand-building a deterministic or agentic workflow.

  2. Jerry Liu
    Jerry Liu says evals now replace hand-built agent workflows

    AIJerry Liu argues that most tasks can now be solved by defining an eval and hillclimbing on it, rather than hand-coding a deterministic or agentic workflow. He says data provider companies are building evals across economic activity so frontier models can handle more work, leaving developers to define goals and success measures. He expects agent interfaces to compress most tasks into goals and eval instructions, while the most complex processes will still need explicit workflow builders.

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