Matthew Berman Explains AI Coding Agent Loops

Matthew Berman16m 12s
Matthew Berman beside the headline Agent Loops.
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    Matthew Berman defines an agent loop as a repeated workflow driven by a trigger and a goalWorkflow automation uses software or AI to complete a connected sequence of routine steps with less manual effort.. He distinguishes measurable conditionsAn acceptance criterion is a specific condition that must be satisfied before a software change or task can be considered complete. from goals that depend on a model’s judgment.

    Berman demonstrates examples for page performance, documentation, architecture, logging, error review, search visibility and product evaluationEvaluation is the systematic assessment of a system's behaviour or results against explicit criteria.. He describes ways to start these tasks manually, on a schedule or in response to an event.

    He cautions that subjective goals can be brittle, new feature work is difficult to constrain and long-running loops can consume substantial tokens. Sponsored and consulting promotions are omitted.

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    Video topic coverage

    • Agents & workflows100
    • Coding & tools100
    • Models & learning25
    • Infrastructure & compute30
    • Research & evaluation70
    • Safety & security0
    • Business & productivity75
    • Creative & media0
    • Robotics & perception0
    • Society & policy0

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    Matthew Berman beside the headline Agent Loops. With WWW.ARTIFICIAL-INTELLIGENCE.VIDEO, 19 June 2026 and duration 16m 12s.

    Matthew Berman explains how triggers and goals structure coding agent loops, with examples and cautions about judgment and cost.