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AI & Software Development

Best Podcasts and Videos About Loop Engineering

Loop engineering is the practice of designing repeatable, verifiable workflows around AI agents. In practice, a loop gives an agent a goal, tools and context, observes and verifies the result, then feeds evidence into the next attempt until an explicit stopping condition is met.

For two useful reference points, read Addy Osmani’s coding-agent framing of Loop Engineering and IBM’s overview of the act, observe, decide and iterate cycle.

Why these resources are worth your time

Each selection does at least one useful job: define the loop clearly, show a complete workflow, explain verification or harness design, or test the pattern’s limits. Together they help you move from a quick mental model to a working system without spending time on generic AI news or resources that use the term without showing how a loop is built, checked or stopped.

Curated by Readpodcast AI · Last updated · Titles are preserved in their original language.

Start here

The shortest useful path into Loop Engineering

Follow this four-step path from a quick definition to the engineering model, the harness around an agent and a critical look at what still requires human review.

Podcast guide

Best Loop Engineering podcasts and episodes

Audio is especially useful for the concepts behind a loop: goals, context, harness design and the division of responsibility between people and agents. Original-language industry discussions sit alongside practitioner interviews and explainers.

YouTube guide

More Loop Engineering videos worth watching

Continue with complete workflows, improving agents, expert disagreement and applications beyond coding. Demonstrations and case studies keep their original titles and languages, and the four Start Here selections are not repeated below.

Explore the AI engineering landscape

These concepts overlap, but each focuses on a different design problem. As this collection grows, the cards will connect guides about related AI engineering practices.

  1. 01

    Prompt Engineering

    Shapes the instruction, constraints and response for one model interaction.

  2. 02

    Context Engineering

    Chooses and maintains the information, memory and examples available during a task.

  3. 03

    Graph Engineering

    Represents knowledge or workflows as connected nodes and edges for retrieval, routing and reasoning.

  4. 04

    Harness Engineering

    Builds the runtime around an agent: tools, permissions, sandboxes, recovery and observability.

  5. 05

    Agentic Engineering

    Designs autonomous and multi-agent systems, including roles, coordination and human control.

  6. 06

    Current topic

    Loop Engineering

    Turns agent work into repeatable cycles with triggers, state, verification, feedback and stopping rules.

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