agentic-ai-engineering/plans/meta-prompts/loop_engineering.md

Loop Engineering Meta-Prompt

Use this meta-prompt for every TAC Product Factory / Plan F3 plan that touches agents, deploy, RSI, or self-improvement.

Required loop sections

Every plan must explicitly map the work to four loops:

1. Agent loop — what agent/tool loop runs until done.
2. Verification loop — what deterministic checks, reviewers, receipts, or rubrics gate success.
3. Event-driven loop — what webhook, cron, queue, file event, or dashboard trigger runs the work without manual prompting.
4. Hill-climbing loop — what trace/evidence can safely modify prompts, skills, tools, or config later.

Required safety fields

Every plan/receipt must include:

• Canonical deploy route: git-proxy:8099/deploy.
• Legacy note: deploy-webhook:8098 is internal/stale for guarded product deploy.
• auto_patch_proven: false unless a degraded-skill recovery receipt exists.
• rsi_canary_recovery_evidence: true only when linking the canary receipt.
• Evidence receipt paths for any RSI/self-improvement claim.

Required validation

At minimum, plans must name the checks that prove:

• plan schema/sections validate;
• deploy route is signed-action-only;
• tests pass;
• claims do not exceed receipts.

Taste rule

Prefer the boring loop that compounds over the clever prompt that works once. If the plan needs a new framework, first prove a markdown file, JSON receipt, and pytest assertion cannot hold the invariant.

Agent harness note

A loop is not just repeated model calls. Every agent plan must name the harness around the model:

• context and working memory;
• durable semantic memory / RAG sources;
• episodic memory / traces from prior runs;
• tool allowlist and signed deploy boundaries;
• end-loop guardrails that define when to stop;
• tracing for retrievals, tool calls, latency, token use, and errors;
• evals/receipts that decide whether prompt or config changes can feed back into the next run.

LLMOps/hill-climbing only promotes a changed prompt, retrieval config, tool config, or model parameter when traces plus evals show the new loop is safer or better.

Reference: Sean's AI Stories, "You Can Learn AI Agent Harness & Loop Engineering In 19 Min" (YouTube GrNbuWWJYiI).

Open-source model portability note

A strong harness should make the task portable across model tiers, including open-source or lower-cost models. Plans must spell out enough context, phases, and quality gates that a non-frontier model can follow the work without relying on hidden taste.

For outbound/productized work, require:

• explicit prospect/input target;
• crawler or source gathering step;
• report generation step;
• cover letter or executive summary when the output is meant to sell or persuade;
• design/report review phase;
• quality gate checklist that loops back to the builder when review fails;
• model/cost notes when choosing GLM/open-source/local models over frontier models.

Reference: Jordan Urbs, "GLM 5.2 Proves Open Source AI Can Match Fable 5 (AI Harness Engineering)" (YouTube dJI2GRG1GEE).

Model Workspace Protocol note

For sequential workflows with human review between stages, prefer folder-structured orchestration before multi-agent framework code. The Model Workspace Protocol pattern treats numbered folders as stages, markdown files as role/context carriers, and local scripts as the boring mechanical layer.

Use this when it fits:

• 00-intake/ — raw request, sources, constraints.
• 10-plan/ — Plan F3 artifact and assumptions.
• 20-build/ — implementation notes and changed files.
• 30-verify/ — test output, verifier notes, receipts.
• 40-ship/ — signed deploy request, outcome, rollback notes.

Reference: arXiv 2603.16021, "Interpretable Context Methodology: Folder Structure as Agentic Architecture".