Loop Library: Repeatable AI Agent Workflows | Forward Future
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Loop Library
Copy practical AI agent prompts with clear checks and stopping<br>conditions.
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Showing 85 loops
Updated Jul 7, 2026
Agentic engineering loops
Loop<br>Action
Engineering<br>By Matthew Berman
The docs sweep
Keeps documentation aligned with the current codebase and opens a reviewable pull request.
Whenever a documentation pass is needed, review the codebase in full and make sure all documentation reflects the current implementation. Update stale documentation, verify the changes, then open a pull request.
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Engineering<br>By Peter Steinberger
The architecture satisfaction loop
Refactors architecture in small, tested, independently reviewed checkpoints.
Refactor until you are happy with the architecture. After each significant step, live-test the system, run autoreview, and commit. Track progress in /tmp/refactor-{projectname}.md.
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Engineering<br>By Matthew Berman
The sub-50 ms page-load loop
Optimizes every page until it consistently loads in under 50 ms.
Continue optimizing the code for speed. After each significant change, measure page-load performance across every page under the same repeatable test conditions. Continue until every page loads in under 50 ms.
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Engineering<br>By Matthew Berman
The production error sweep
Finds, fixes, and verifies actionable errors in production.
Review our production logs for errors. If you find an actionable issue, trace it to its root cause, fix it, verify the fix, and open a pull request. If no actionable errors are present, stop without making changes.
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Engineering<br>By Matthew Berman
The 100% test coverage loop
Adds meaningful tests until the full suite reaches 100% coverage.
Add tests until we have 100% test coverage.
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Content<br>By Matthew Berman
The SEO/GEO visibility loop
Fixes the highest-impact gaps in search and AI answer visibility.
Run an SEO/GEO audit across crawlability, indexation, page intent, titles, internal links, structured data, source citations, and answer-first content. Rank the gaps by expected impact, fix the highest-leverage issue, then rerun the same crawl and target-query benchmark across search engines and AI answer engines. Repeat until no critical technical issues remain, every priority query maps to a clear answer-ready page, and the benchmark shows no high-impact gap left to fix.
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Engineering<br>By Matthew Berman
The logging coverage loop
Adds useful, tested logs to every important system path.
Review the system's logging and add missing coverage until every important path produces useful, tested logs.
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Engineering<br>By Matthew Berman
The nightly changelog loop
Keeps the changelog current with meaningful changes from the previous day.
Each night, review changes from the previous day and update the changelog with anything users should know.
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Evaluation<br>By Matthew Berman
The quality streak loop
Fixes product failures until a defined streak of realistic tests passes.
Test realistic scenarios. When one fails, document it, add regression and benchmark coverage, fix it, and restart the streak. Stop after [N] successful cases in a row.
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Featured<br>Evaluation<br>By Matthew Berman
The full product evaluation loop
Recreates production locally, tests every product surface, and fixes all verified bugs holistically.
Build sanitized, production-scale local data under production-like settings. Inventory every user-facing feature, role, route, button, input, modal, state, and workflow; define documented acceptance criteria and finite risk-based edge cases for each. Test as a real user, logging every bug with reproduction evidence. Review findings for shared causes and dependencies; implement coherent fixes with regression tests, then rerun the full inventory. Stop at a clean pass or blocked handoff. Ask before production, sensitive data, or destructive actions.
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Engineering<br>By Matthew Berman
The test-suite speed loop
Speeds up the test suite without weakening coverage, assertions, or isolation.
Optimize the test suite to run as quickly as possible without reducing coverage or changing behavior.
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Engineering<br>By Matthew Berman
The repository cleanup loop
Recovers valuable repository work and safely removes proven stale state.
Inspect local and remote branches, pull requests, commits, and worktrees. Recover valuable work and clean...