So we're doing loops now

2026-06-12 · Source: Matthew Berman · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

Agent loops represent a shift in AI agent interaction, moving from discrete prompt-and-wait workflows to continuous, goal-driven operations. Instead of repeated prompting, an agent given a goal will initiate and persist until that goal is met. A loop fundamentally requires a trigger and a verifiable goal, which can be either deterministic (e.g., all tests pass) or non-deterministic. For instance, in Cursor, an automation can be configured to trigger upon opening a PR in Astro Hub. The agent is then instructed to review the PR, automatically fix potential issues, commit changes back, ensure all tests pass, and verify continuous integration (CI) is green. This demonstrates how loops enable autonomous, self-correcting development tasks.

Key takeaway

For Software Engineers or MLOps teams managing development workflows, adopting agent loops can significantly enhance automation and efficiency. You should explore integrating goal-driven agents to handle repetitive tasks like pull request reviews, automated bug fixes, and continuous integration validation. This approach reduces manual intervention, ensuring code quality and accelerating delivery cycles by allowing agents to autonomously iterate until defined criteria are met.

Key insights

Agent loops enable autonomous, continuous goal-seeking workflows, transforming discrete interactions into persistent operations.

Principles

• Agent loops require a trigger.
• Goals must be verifiable.
• Goals can be deterministic or non-deterministic.

Method

Configure an automation with a specific trigger (e.g., PR open) and a multi-step goal (e.g., review code, fix issues, pass tests, ensure CI green), allowing the agent to self-correct until completion.

In practice

• Automate pull request reviews.
• Automatically fix code issues and commit.
• Ensure continuous integration passes.

Topics

• AI Agents
• Automation
• Software Development
• Continuous Integration
• Pull Request Review
• Workflow Automation

Best for: AI Engineer, Software Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Matthew Berman.