# 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`).

## Autonomous work agent note

For business adoption, distinguish two operating modes before designing the harness:

1. **Team manages agents** — an agent factory or admin team creates, updates, and governs shared agents.
2. **Agents assist people** — each human gets a personal work agent that learns their context, skills, preferences, and second-brain material.

Every autonomous work-agent plan must include:

- per-agent isolation boundary, preferably one container or equivalent sandbox per agent;
- credential separation: no raw secrets inside the agent environment;
- vault/proxy credential injection for only the requests the agent is allowed to make;
- explicit access policies for tools, files, APIs, and outbound network;
- skill/instruction iteration loop for tuning the agent's actual output;
- management surface for upgrading, maintaining, and revoking agents after deployment;
- second-brain/context source plan when personal-agent behavior depends on private knowledge.

Reference: Latent Space, "The Blueprint for Autonomous Work Agents" with Gavriel Cohen / NanoClaw (YouTube `hLUGXO5DSpo`).

## 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".