Forward Future field guide

Loop Library

Copy prompts for engineering, research, evaluation, and operations. Each one includes clear checks and tells the agent when to stop.

Showing 15 loops

Agentic engineering loops
No. Loop Verify / stop Attribution Action
001

The overnight docs sweep

Each night, review the codebase in full and make sure all documentation reflects the latest changes from the previous day. Update the documentation as needed, then open a pull request with those changes.

Verify / stop Documentation matches the current implementation. Finish with a reviewable pull request. Attribution Matthew Berman
002

The architecture satisfaction loop

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.

Verify / stop The architecture is satisfactory and checks pass. Live-test, autoreview, and commit each significant step. Attribution Peter Steinberger
003

The sub-50 ms page-load loop

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.

Verify / stop Every page loads in under 50 ms. Use the same benchmark and confirm there are no regressions. Attribution Matthew Berman
004

The production error sweep

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. Then ping me in Slack with the findings and PR link. If no actionable errors are present, ping me with that result instead.

Verify / stop Actionable production errors are fixed and verified. Finish with a PR and Slack summary, or a clean-log confirmation. Attribution Matthew Berman
005

The 100% test coverage loop

Add tests until we have 100% test coverage.

Verify / stop The full test suite passes at 100% coverage. Use the project's coverage report as the source of truth. Attribution Matthew Berman
006

The SEO/GEO visibility loop

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.

Verify / stop Priority pages are indexable, answer-ready, and technically sound. The repeatable crawl and query benchmark finds no remaining high-impact gaps. Attribution Matthew Berman
007

The logging coverage loop

Review the system's logging and add missing coverage until every important path produces useful, tested logs.

Verify / stop Every important path emits useful, tested logs. Representative success and failure tests prove coverage without exposing sensitive data. Attribution Matthew Berman
008

The nightly changelog loop

Each night, review changes from the previous day and update the changelog with anything users should know.

Verify / stop Every user-relevant change from the previous day is accounted for. The changelog is updated and validated, or the no-change result is recorded. Attribution Matthew Berman
009

The quality streak loop

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.

Verify / stop The latest [N] realistic cases pass in a row. Every earlier failure is documented, fixed, and protected by regression and benchmark coverage. Attribution Matthew Berman
010

The full product evaluation loop

Create [N] realistic scenarios covering every major capability. Before testing, define clear success criteria and choose a consistent evaluation method, such as pass/fail checks or a scoring rubric. Run every scenario under the same conditions and record evidence for each outcome. Fix the underlying cause of anything that does not meet the criteria, rerun the affected scenarios, and then rerun the complete set. Continue until every scenario meets the original quality bar.

Verify / stop Every one of the [N] scenarios meets the defined quality bar. The final evaluated run covers every major capability under the original conditions. Attribution Matthew Berman
011

The test-suite speed loop

Optimize the test suite to run as quickly as possible without reducing coverage or changing behavior.

Verify / stop The suite is faster with no coverage or behavior regression. Repeatable timing, the full passing suite, and the original coverage report prove the result. Attribution Matthew Berman
012

The repository cleanup loop

Inspect local and remote branches, pull requests, commits, and worktrees. Recover valuable work and clean everything stale until the repository is current and organized.

Verify / stop Valuable work is recovered and remaining repository state is intentional. Branches, pull requests, commits, and worktrees are current, owned, or safely removed with evidence. Attribution Matthew Berman
013

The stale-safe batch release loop

Review pending changes and pull requests, exclude stale or unfinished work, combine the valid changes, and release them together.

Verify / stop Only current, complete changes ship in the combined release. The released revision is the latest integrated main that contains every selected change. Attribution Matthew Berman
014

The production data cleanup loop

Review production records, remove anything that does not meet the allowed definition, improve the classification logic, and verify the remaining data.

Verify / stop Every remaining record meets the allowed definition. Representative classification tests and a post-cleanup audit prove the retained data is valid. Attribution Matthew Berman
015

The post-release baseline loop

After current releases finish, run the standard benchmarks and record the results as the new baseline.

Verify / stop The new baseline belongs to the completed release. Revision, environment, benchmark version, conditions, and results are recorded together. Attribution Matthew Berman

A useful loop specifies: trigger, action, proof, memory, and a stopping condition.

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