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).

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).

Product work with coding agents note

When implementation gets cheap, do not delete product discipline. Plans should use prototypes to explore, but still preserve the product loop:

• PRD/spec captures why, users, constraints, and success criteria;
• prototype makes options concrete and testable;
• human taste and systems thinking review whether the result fits the whole product;
• scheduled/background agents may gather context, but promotion still needs receipts and review;
• role boundaries can flex, but ownership, decision rights, and phase state must stay explicit.

Reference: Lenny's Podcast, "OpenAI Codex lead on the new shape of product work" with Andrew Ambrosino (YouTube P3KDebPTUrw).

External research anchors

Ground agent plans against external production guidance, not just video notes:

• NVIDIA Secure Agent Workspace: always-on agents need a managed workspace with credential proxy, enterprise tool access, governance, and runtime controls.
• Infisical Agent Vault: secrets stay in the vault and requests receive credentials through a proxy; agents must not hold raw secrets.
• Anthropic agent evals: judge trajectories and environment outcomes, not just final answers.
• OWASP AI Agent Security Cheat Sheet: treat prompt injection, memory poisoning, tool abuse, sandboxing, and access control as first-class risks.

These anchors support TAC's local rule: deploy remains signed-action-only through git-proxy:8099/deploy; do not reintroduce raw command deploy paths.

References: NVIDIA Secure Agent Workspace (docs.nvidia.com/enterprise-reference-architectures/secure-agent-workspace-reference-design), Infisical Agent Vault (infisical.com/blog/agent-vault-the-open-source-credential-proxy-and-vault-for-agents), Anthropic "Demystifying evals for AI agents" (anthropic.com/engineering/demystifying-evals-for-ai-agents), OWASP AI Agent Security Cheat Sheet (cheatsheetseries.owasp.org/cheatsheets/AI_Agent_Security_Cheat_Sheet.html).

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".