Gate refunds and DB writes behind deterministic checks, not prompt hopes
At a glance
An Ask HN on controlling agents that take real actions converges on: LLM proposes → deterministic validator (rules, spend ceilings, replay checks) → human approve—especially for refunds, payments, and database writes where prompt 'never do X' fails. This workflow is listed under Guardrails, Refunds, Approvals, Safety, Architecture.
In r/AI_Agents, builders say the lasting 2026 pattern is collect→draft→show evidence→wait for approval: inbox triage, GitHub/release monitoring, support handoff drafts, and invoice extraction that formats billing payloads without silent sends.
An Indie Hackers builder runs event-driven agents: when backend changes land, webhooks update TypeScript/Python SDKs, run tests, draft changelogs/PRs, and only deploy if checks pass—plus a morning schedule that drafts Agent Store listings for review.
Beta plugin sits between the model and your app: write hard-deny/soft-deny/allow rules in prose; Armor evaluates every tool call (powered by a decision model) and blocks policy violations before bash/git/exfil runs—Claude Code auto mode works via Ori on any OpenRouter model.
Indie Hackers founder Sean Prentice used Claude Code to build an orchestrator + marketing director + specialist agents (content/ads/research) for his SaaS; outputs land in Slack and nothing posts or spends without his explicit say (~$10/mo self-use).
