Loop cost guardrails pattern

The four-part cost guardrail every agent loop should ship with: a hard iteration cap, a stop-after-N-turns clause in the goal text, a budget limit on SDK loops, and a /cost check inside the loop body.

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Every loop gets: `MAX_ITER=20` hard cap, "or stop after N turns" in the /goal text, `max_budget_usd` on SDK loops, and a /cost check in the loop body.
claude-code

Implementation note

When to use: before running any loop unattended — this is the checklist pattern for the most common loop disaster, discovering the overnight run cost fifty times your estimate. How it works: four guardrails, applied together rather than as alternatives. A hard MAX_ITER=20 cap in the loop wrapper bounds iterations mechanically. An or-stop-after-N-turns clause written into the /goal text bounds the in-session layer. max_budget_usd on SDK-driven loops bounds dollar spend directly. And a /cost check inside the loop body gives running visibility, so drift is observable mid-run instead of at invoice time. Safety: the redundancy is the design — each rail catches what another misses: the iteration cap stops infinite loops, the budget cap stops expensive-per-iteration loops, and the in-prompt clause survives contexts where the wrapper does not apply. Adopt all four as the default template for every loop you ship, not just the risky ones.

Source: Developers Digest ↗graded C · 65/100 — how grades work →

More automation loops

Ship PRD stories via dual-agent loop

Loop/ralphGitHubA

Ralph runs a generator and evaluator in tandem until all user stories pass acceptance criteria and browser tests.

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# Ralph Harness — Agent Instructions ## Overview Ralph Harness is an autonomous AI agent loop that runs AI coding tools (Amp or Claude Code) repeatedly until all PRD items are complete. Each iteration is a fresh instance with clean context. Ralph supports two modes: | Mode | Architecture | When to use | |------|-------------|-------------| | simple | Single agent (self-implement, self-check) | Quick tasks, backend-only stories, well-defined small changes | | harness | Generator + Evaluator (dual-agent with contract) | UI-heavy features, complex stories, when quality is critical | ## Architecture: Harness Mode ralph.sh orchestrator │ ├── Planner (prd.json) │ Defines user stories, acceptance criteria, dependencies │ ├── Generator (generator-prompt.md) │ Drafts sprint contracts → Implements stories → Fixes based on feedback │ └── Evaluator (evaluator-prompt.md) Reviews contracts → Signs/locks → Tests in browser → Scores → Writes feedback ### Per-Story Flow 1. Contract Negotiation : Generator drafts contract.json → Evaluator reviews → Back-and-forth until Evaluator signs → Contract locked (immutable) 2. Build : Generator reads Hard cap: stop after 30 iterations even if PRD items remain.
automationhigh risk

Ship production-grade apps autonomously

Loop/ralphGitHubB

Hand an idea to Claude Code; it authors specs, designs, builds, tests, secures, and ships until enterprise done or budget exhausted.

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# dare-to-be-stupid — Design (v2, refined) > A Claude Code plugin. One command, /dare , hands an idea or PRD to an autonomous > loop that authors specs, designs, builds, tests, secures, ships, fixes, and iterates > until the app passes an enterprise-production definition of done — or the budget dies. > > Named for the Weird Al song. The joke is that it runs the Ralph Loop on purpose , > with --dangerously-skip-permissions , and narrates the whole thing in the voice of an > '80s Junkion. Pre-production only. Never points at anything with users. This is v2. It keeps the strong core of the original spec (external reviewer, ratchet, guard hook, Junkion style) and adds the three phases the original left thin relative to the actual goal: PRD authoring, a design phase, and a real enterprise DoD including security, CI, docs/observability, and design quality (with quality plugins auto-installed). --- ## 0. The premise, in one paragraph The User builds documentation-first: spec → system docs → API contracts → CLAUDE.md → code. dare-to-be-stupid is the deliberate inverse, packaged as comedy that also solves two real engineering problems. It is a real build , not a joke ar

Set and ship autonomous goals

Loop/goalGitHubB

Run a persistent goal autonomously until completion, routing each task to the optimal model for cost and capability.

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/goal <objective · /loop <task | Set a persistent goal · run autonomously until complete (≤25 turns) |
automationlow risk