Make's Maia vs Zapier Agents vs n8n 2.0: The AI Automation Agents Compared

Maia builds automation for you. Zapier Agents take ongoing autonomous actions. n8n AI nodes process data inside self-hosted workflows. Picking the wrong one costs you months of the wrong implementation.

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Three automation platforms launched AI agent features within months of each other in early 2026. Make shipped Maia, its AI scenario builder. Zapier moved Agents to general availability. n8n 2.0 added a native AI Agent node to its workflow builder. All three are being marketed under some variation of "AI agents for automation." All three solve a different problem.

The marketing conflation is understandable but operationally significant. If you pick the wrong one for your use case, you will spend time building something that works technically but does not address what you actually needed. This comparison clarifies what each product actually does, what it costs, and which operator profile each one fits.

Three Different Problems Being Solved

Start with the core distinction before comparing features:

Make's Maia solves the creation problem. Building a Make scenario requires understanding the visual builder, knowing which modules to use, and configuring data mapping between steps. Maia reduces that learning curve by letting you describe the automation in natural language and generating a draft scenario. The AI work happens once, at setup time. After the scenario is built, it runs like any other Make scenario with no ongoing AI decision-making.

Zapier Agents solve the decision problem. You define a goal for an agent that monitors a data source continuously and decides what to do when new data arrives. The AI work happens at every execution. The agent reads content, classifies it, and chooses which actions to take. This is useful when the action depends on interpreting content, not just on the presence of a trigger.

n8n 2.0 AI Agent nodes solve the processing problem. You embed an AI agent as a step inside a larger deterministic workflow. The agent receives data, uses configured tools, and returns a result that the next workflow step uses. The AI is a processing unit, not an autonomous actor. The surrounding workflow is still deterministic; only the AI node's behavior is variable.

Stated plainly: Maia helps you build automation faster. Zapier Agents replace fixed rules with AI judgment. n8n AI nodes add AI-powered data processing to existing workflow steps. These are not competing implementations of the same feature. They are different tools for different jobs.

Make Maia: AI That Builds Automation for You

Maia is accessed through Make's scenario builder by typing a description of the automation you want. Maia parses the description, identifies the apps and modules needed, and generates a draft scenario with modules and basic data mapping. You review the draft, adjust the data mapping, add credentials, and publish.

The technical implementation: Maia understands Make's native module library and generates valid scenario JSON. It handles common automation patterns accurately (new email triggers action in CRM, form submission triggers notification and database entry). It struggles with more custom logic: conditional branching based on specific field values, error handling, or multi-step sequences with dependencies that require understanding your specific data structure.

What Maia is not: it is not an agent that takes ongoing actions. Once the scenario is built, Maia is done. The scenario runs on Make's execution engine, not on any AI runtime. Maia does not observe the scenario's results or suggest improvements over time.

Who benefits most: operators who are evaluating Make but find the builder intimidating, or operators who need to build new scenarios quickly without spending an hour with documentation. If you already build Make scenarios fluently, Maia saves some setup time but is not a fundamental capability change.

Maia is included in all Make plans at no additional cost. Make Core costs $10.59 per month for 10,000 operations, making it the lowest-cost entry point in this comparison for operators who want AI-assisted automation building without per-execution agent charges.

Zapier Agents: AI That Takes Ongoing Actions

Zapier Agents are configured with a goal, a set of tools (Zapier's app integrations), and a monitoring trigger. The agent runs continuously. When the trigger fires (new email, new CRM entry, new form submission), the agent reads the incoming data, decides which actions to take, and executes them.

The technical differentiation from a standard Zap: a Zap executes the same action sequence every time its trigger fires. An Agent reads the trigger data, interprets it, and decides what to do. A Zap that fires on every incoming email and creates an Asana task regardless of email content is a Zap job. An Agent that reads each email, classifies urgency from content, creates Asana tasks only for urgent emails, and routes others to a Notion log is an Agent job.

Agents in Zapier's GA release have persistent memory via Zapier Tables, can invoke existing Zaps as tools, and show error logs with decision reasoning. The platform is stable at seven or fewer consecutive actions per agent run with error rates below 2%.

The cost structure: Agents are included in Zapier Professional at $49.99 per month (2,000 tasks). Each action an Agent takes counts as one task. An Agent that processes 200 items per month with four actions each consumes 800 tasks. At high volumes, this approach favors Zapier's Team plan ($69.99 per month, 50,000 tasks).

Who benefits most: Zapier users who have a routing or classification problem that cannot be solved with simple keyword filters. Customer support inbox routing, lead qualification from form submissions, social mention classification. Use cases where the action depends on reading and interpreting content.

n8n 2.0 AI Agent Node: AI Embedded in Self-Hosted Workflows

n8n 2.0's AI Agent node is a processing node inside a workflow. You place it in a workflow like any other node, configure the model (OpenAI, Anthropic, Google Gemini, or a local Ollama model), define the system prompt, and specify which tools the agent can call. The node receives data from the previous step, runs its tool-calling loop, and passes the result to the next step.

The tools available to an n8n AI Agent node are anything n8n can connect to: HTTP requests to external APIs, Postgres or MySQL queries, file reads and writes, email sends, webhook calls to other services, or other n8n workflows via webhook. The flexibility is significantly wider than Zapier Agents' 7,000-app library because the HTTP request node covers APIs that n8n has no native connector for.

The operational difference: n8n runs on your infrastructure. The AI Agent node calls your configured LLM API (you pay for tokens directly) and uses your server compute. There are no per-completion charges from n8n. Self-hosted n8n costs the server ($12 per month on DigitalOcean) plus LLM API costs (typically $0.002 to $0.008 per 1,000 tokens for GPT-4o and similar models).

Who benefits most: technical operators who need full control over AI processing within workflows, who are already comfortable running infrastructure, and who run high volumes that would make Zapier's per-task pricing expensive. An n8n workflow running 5,000 AI-assisted data classifications per month costs server + LLM API. Zapier at 5,000 tasks (with four actions per item) would require a plan well above Professional.

Side-by-Side Comparison

Which One to Use and When

Use Make with Maia if your problem is building automation faster. You want the productivity gain from describing what you need and getting a working starting point rather than building from scratch. You are comfortable with Make's execution model and just want to reduce setup time. Make is also the right call if you need complex conditional branching that Zapier's Zap editor handles poorly. Maia makes the builder accessible; Make's conditional logic handles the branching depth that Zapier struggles with.

Use Zapier Agents if your problem is routing or acting on content you cannot predict in advance. You need an always-on process that reads incoming data, makes decisions based on meaning rather than keyword matching, and takes different actions based on that interpretation. You are already on Zapier and do not want to migrate infrastructure. The task-count math works for your volume.

Use n8n AI Agent nodes if you are technical, you need AI processing at high volume or with APIs outside Zapier's library, and you want to pay for LLM tokens rather than per-task platform charges. You run n8n for other workflows already and adding AI processing is an extension, not a new platform decision. See the Make vs Zapier vs n8n detailed comparison for cost breakdowns at specific execution volumes.

The choice does not have to be permanent. All three platforms export their automation definitions. If you start with Zapier Agents and hit the per-task pricing ceiling, migrating to n8n is a project, not a sunk cost. The AI Stack Advisor can match your specific use case and volume to the right starting point.