Your Make Scenario's AI Agent Has No Memory. Here Is the Fix.

Every run starts blank. Your scenario fires, the AI module reads its prompt, does its thing, and then forgets everything. The next run? Same blank stare. If you have ever built a Make scenario around an AI agent and felt like you were reintroducing yourself to a coworker every single morning, you already know this pain.

Make scenarios are stateless by design

Make.com is built around isolated executions. A trigger fires, data flows through modules in a straight line, the scenario finishes, and the runtime cleans up. Nothing carries over. That architecture is great for reliability. You never worry about one run corrupting another. But it means state is not the platform's job. State is your job.

An LLM module inside a scenario gets exactly what you feed it: the prompt, the mapped fields, maybe a system instruction. It has no idea what happened in the run before it. It does not remember that the customer asked a follow-up question yesterday. It does not know which leads it already scored or which drafts it already sent. Each execution is a fresh conversation with someone who has never met you.

For one-shot tasks this is fine. Summarize this ticket, classify this email, translate this text. Stateless works. The moment you want continuity, a support agent that remembers the customer's history, a sales assistant that tracks a lead across touchpoints, a research agent that builds on yesterday's findings, the amnesia becomes the bottleneck.

The workarounds people try

The standard answer in the Make community is Data Stores. Make has a built-in key-value store, so you add modules that write the conversation turn after the AI responds and read it back before the next prompt. It works. It is also a second system to design, maintain, and debug.

You decide on keys, serialization, expiry logic. You handle what happens when two runs race each other. You build a retrieval layer that picks which history is relevant instead of dumping a whole log into a token-hungry prompt. And you rebuild this scaffolding in every scenario that needs memory. The Data Store is a database, not a memory system. It stores what you tell it to store, exactly how you tell it to store it.

The other common hack is Google Sheets as memory. Append a row per turn, read the sheet at the start of the next run. It is cheap and visible, which is why people do it. It is also slow, fragile, and painful at any real volume. Row limits, formatting accidents, someone sorting the sheet and breaking your offsets. A spreadsheet is not a memory layer no matter how convenient it looks.

The laziest option is stuffing history into the prompt itself. Pass the last few messages as context every time. This works for about three turns and then eats your token budget alive. Costs scale with every message, latency grows, and you are doing retrieval by hand with no retrieval at all. Just everything, every time.

All three approaches share the same problem. You are building memory infrastructure instead of building your agent. And the memory only exists inside one scenario anyway. The agent in your Make scenario still knows nothing about the conversation you had in ChatGPT last night.

What memory should actually do

Step back and ask what an AI agent needs. It needs to remember what was said, across runs, across tools, without you hand-feeding it context every time. The memory should be write-once and readable everywhere. Your Make scenario, your ChatGPT session, your Claude chat should all be talking to the same brain.

That is what Vilix AI is. It is a cloud-hosted memory layer for AI tools. There is nothing to install and nothing to maintain. No database to provision, no expiry cron jobs, no schema migrations. You connect it and it remembers.

Vilix AI connects over MCP, the open protocol for giving AI tools shared context. Your Make scenario reaches it through an HTTP module, the same way it reaches any API. Your ChatGPT, Claude, Codex, and Cursor sessions connect over MCP directly. One memory, every tool, every device. A conversation your agent has in Make on Tuesday is visible to Claude on Wednesday. No exports, no copy-paste, no plumbing.

It stores full conversation history, not just facts. Most memory products reduce your past to a few bullet-point summaries. Vilix AI keeps the actual conversations, so the real thread can be revisited anytime. Summaries are useful. They are not a replacement for the record.

Getting started

The free plan is free forever. No trial clock, no credit card, no locked features that matter. If you outgrow it, the Pro plan comes with a 7-day trial and still no credit card required. Your data stays portable either way. Export everything or delete it anytime in a portable format. Leave with your data whenever you want.

The pattern for a Make scenario is simple. Before the AI module, pull the relevant memory. After the AI module, save the turn. Two HTTP modules in your scenario, and your agent stops waking up blind. Over time that history compounds. Your agent gets smarter about the customer, the lead, the project, because it has actually been there.

Stateless execution is a good architectural choice for automation. It is a terrible default for agents. Give yours a memory and see how much changes. Start at vilix.ai.