Boring AI vs Make

Make's canvas is its strength. Every module, router, filter, and error handler sits where a teammate can see it, and the modules you draw are the ones that run. Make AI Agent (New), released in February 2026 and still in open beta, places an agent inside that canvas: it works from instructions and knowledge files, calls modules, scenarios, MCP tools, and sub-agents as tools, and records each decision in a Reasoning tab. Maia, in public beta on paid plans, builds scenarios and agents from chat, modifies existing scenarios, and sees run history and logs to help fix errors. If your team thinks in diagrams, that is a real advantage.

The diagram is also the thing you maintain: in a scenario, a new exception is handled by drawing it, as another route, filter, or handler. Boring keeps the order without the drawing. You write the procedure the way you'd brief a colleague, and numbered instructions run as an enforced plan: one step at a time, no skipping ahead, each step marked complete. A step can require evidence before the next one begins, such as a saved artifact or a successful tool receipt for an exact target. The definition is versioned, so every change is a diff you can roll back, and the workspace assistant (⌘J) can prepare a fix as a card that changes nothing until someone confirms it.

Make's AI Agents page says you can "set clear rules, add manual approvals, or stop the Agent at specific points". Its help center describes how. To approve a tool's output before the agent continues, add a tool that sends the output to you for review, such as a Slack message you reply to, and explain that extra step in the agent's instructions. The same guide cautions that agents "could still behave unpredictably and ignore or misinterpret your explicit guardrails". For scenarios, Make's Human in the Loop app, on Enterprise and in closed beta for invited customers, creates review requests that come back approved, adjusted, or canceled.

Boring takes the gate out of the prompt. New agents start with every connected action waiting for approval in a shared Tasks inbox, and the runtime holds the call until a person decides, whatever the instructions say. Owners loosen that to writes only or auto-approve eligible actions they trust, such as reads; an action Boring can't classify counts as a write. Each request leads with the exact tool call and its scrubbed arguments. An assigned reviewer can approve, modify the call, or reject it; the agent's owner or an admin can reassign it; and a review that goes unanswered resolves the way the agent is set up: reject, the default, or skip that action and continue.

Make's own guidance is to "choose tasks for your agent that you trust an intern to handle". Boring is built around the moment an intern would check with someone first: the customer email, the account change, the record update a team has to answer for.

Make's error handling is mature and visible. Retry, Resume, Skip, Commit, and Rollback handlers are drawn on the canvas next to the modules they protect. With incomplete executions turned on (they are off by default), Make stores the failed run and retries rate-limit, connection, and timeout errors on a backoff schedule, starting from the module that caused the error. Replay, on every plan, runs the current version with a past run's trigger data, and that data passes through all modules, including those that already succeeded.

Boring's recovery is built around the write you can't be sure of. Eligible failed runs resume from the last completed step. In an agent that runs numbered steps, when a call errors after it was sent and may have taken effect, Boring blocks automatic repetition and asks you to check the external system instead of guessing. Make's docs treat a timeout as something to retry: it reruns after a timeout on its backoff schedule, and its fix for a timed-out module is a Retry handler. Whether a timed-out write already took effect is not addressed in Make's error-handling docs. Eligible Boring runs also wait out model-provider throttling and outages within a bounded window and, if the provider stays down, end with their saved progress and a clear reason. Failure reasons are classified (billing, rate limit, context size, safety stop) with the provider's reference ID, and Settings → Logs shows what didn't run at all, and why.

If your workflow depends on a long-tail app, Make is the safer choice. It lists 3,000+ apps, its HTTP app reaches services without an integration, and its MCP server lets Claude and ChatGPT run your on-demand scenarios. Boring connects 1,500+ tools and can import any API with an OpenAPI 3.x spec or a token-authenticated MCP server, but its long-tail coverage lags Make's. Make also holds SOC 2 Type II and SOC 3 reports, lets each organization choose a US or EU data center, and includes SSO, audit logs kept 12 months, custom roles, and an on-prem agent on Enterprise. Make Grid maps how scenarios, apps, and AI components depend on each other. Boring's SOC 2 Type I is in progress.

Make publishes its pricing: a Free plan with up to 1,000 credits a month, paid plans priced by credit volume, one credit for most module actions, and token-based credits on top when an agent uses Make's AI Provider. Boring is in early access on design-partner terms, so this page makes no cost comparison. What Boring offers instead is white glove: the team that builds the product scopes the workflow, builds the agent, and operates it with you.