AI systems · Pattern
Maker-checker review
What is Maker-checker review?
Maker-checker review is a control pattern in which one agent or person produces work and a separate one verifies it against a specification before it ships.
Borrowed from banking controls, it is the most portable reliability pattern for AI agents: it works across models because it constrains the process, not the model.
Related entities
- Altitude, depth and rangeAltitude, depth and range are Felix Rieseberg’s three directions beyond speed for AI work: pursue outcomes, use expert instruments and combine disciplinary methods.
- Claude DuetClaude Duet is Anthropic’s proactive personal AI agent for delegated work in connected apps, introduced as an invitation-only research preview in October 2026.
- Editorial operationsEditorial operations is the governed process connecting audience research, content, site architecture, verification, publication, and ongoing maintenance.
- Claude CodeClaude Code is Anthropic’s agentic development tool for inspecting projects, editing files, using tools, and verifying work under configurable permissions.
- AI evaluationsAI evaluations are repeatable tests of a model or agent workflow against defined tasks, acceptance criteria, and observable failure categories.
- Model Context ProtocolThe Model Context Protocol (MCP), introduced by Anthropic in November 2024, is an open standard for connecting AI applications to external tools and data sources.
- Synthetic usersSynthetic users are AI-simulated personas used to generate and rank hypotheses about how real customers might react, before real-user testing confirms them.
Field notes covering Maker-checker review
- From Content Writer to Website OperatorClaude Code changes the boundary of a writing job: from handing over a document to owning a governed website pipeline. The opportunity, the workflow, and the limits.
- AI Development: Evidence Before AutonomyA convincing demo is not a release gate. How to evaluate agent-built software, contain failure, and measure the whole delivery loop instead of the typing speed.
- AI Pages vs. AI Architecture: Why Volume LostCreating pages with AI multiplies documents. Having AI build entity hubs, internal links and wiki structure multiplies relationships — and that is what answer engines quote.
- The Practice: Spec-Driven, Maker-Checker, Governed ScopeTwo credible trials on AI coding point in opposite directions — 55.8% faster in one, 19% slower in the other. The difference isn't the model. It's the practice. Three patterns, with interactive math and cited data.
- Loop Engineering: The Definitive Field GuideThe leverage point in software has moved from writing prompts to designing the loops that prompt your agents. The complete field guide — Ralph, maker-checker, worktrees, AGENTS.md, memory, and MCP — from someone who runs these loops across a live portfolio.
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