ROADMAP.md

Roadmap

This roadmap keeps Awesome Loop Engineering focused on useful, verifiable work for an emerging practice.

Near Term

• Collect more direct Loop Engineering sources as the term stabilizes.
• Add real or anonymized gallery entries from practitioners running recurring agent loops.
• Grow the runnable loop directory beyond the test-repair reference loop, including scheduled-trigger variants per runtime.
• Add more translations for the introduction, mental model, Loop Contract, and contribution guide.
• Audit contextual sources in small batches and replace weak summaries or secondary links with stronger canonical evidence.
• Continue replacing weak or unstable links with primary sources, official docs, papers, and implementation-heavy write-ups.

Pattern Library

The library now contains 15 reference patterns: PR babysitting, CI repair, docs drift, deploy verification, feedback clustering, dependency triage, evaluation regression, security review, cost control, bug hunting, enterprise approval, incident response, data quality, release notes, and model routing. Every pattern ships a schema-validated loop contract in examples/.

Next pattern-library work should prioritize variants backed by operational evidence rather than adding names for coverage. Useful additions include runtime-specific implementations, before/after receipts, measured retry and cost budgets, failure cases, and human-escalation outcomes.

Community And Adoption

• Publish a concise monthly Discussions digest with corrected annotations, new primary sources, and open contributor tasks.
• Keep several narrowly scoped good first issue and help wanted tasks available for source audits, translations, runnable examples, and gallery case studies.
• Ask cited authors to review the repository's characterization of their work; request corrections, not promotion or stars.
• Track qualified traffic, forks, watchers, and external contributions after each launch channel while GitHub traffic data is still available.

Gallery

The gallery should grow from reference examples into public or anonymized case studies. Good entries should include:

• the runtime or agent tool used;
• trigger and intake source;
• verification gates;
• durable state artifact;
• budget and escalation rules;
• receipts or anonymized evidence;
• lessons learned after real use.

Quality And Governance

• Keep CI dependency-light and easy for contributors to run locally.
• Keep all resource annotations tied to recurring agent systems, not generic AI-agent interest.
• Keep public claims conservative: this repository is an early curated field guide, not a finished standard.
• Preserve clean owner-only commit identity for main.

Open Questions

• Which loop primitives become common across Codex, Claude Code, GitHub Agentic Workflows, and custom runtimes?
• What is the right schema shape for portable loop contracts?
• Which verification gates are strong enough for unattended or semi-attended loops?
• How should maintainers evaluate submitted real-world loop examples without exposing private data?