AI Agents in Retool: How to Build Automated AI Workflows

If you're trying to build AI agents in Retool, you're not alone — and you're not too early. Retool's team has been publicly experimenting with agentic patterns inside Retool Workflows, and a dedicated AI Agent product is actively in development. This guide covers what's available today, how to set up a working agent workflow with the shared community example, and what native agentic capabilities are coming soon.

What Are AI Agents in Retool?

An AI agent is an autonomous system that can plan, take actions, evaluate results, and loop — without a human triggering each step. In Retool's context, this means building workflows where an LLM can call tools (like database queries or API requests), store memory across iterations, and optionally pause for a human confirmation step before continuing.

Retool's team introduced an AI Agent block in Retool Workflows designed to make this safer for production use. Key features include a human confirmation step (so the agent doesn't act autonomously without oversight) and eval functions to validate outputs before the workflow proceeds.

How to Set Up the Community AI Agent Workflow

A working example — nicknamed Reagent — was shared directly by Retool's team in the community thread. It consists of two parts: an orchestrator workflow and a front-end Retool app. Here's how to get it running:

• Download the [Reagent] Orchestrator workflow JSON and the Reagent app JSON from the community thread.
• Import both into your Retool instance via the import tool in the Workflow and App editors.
• Create the required database tables in your Retool DB by running the following SQL:

CREATE SEQUENCE reagent_messages_id_seq;
CREATE SEQUENCE reagent_steps_id_seq;
CREATE SEQUENCE reagent_threads_id_seq;

CREATE TABLE reagent_messages (
  id INTEGER NOT NULL DEFAULT nextval('reagent_messages_id_seq'::regclass),
  created TIMESTAMP WITH TIME ZONE DEFAULT now(),
  message_text TEXT,
  message_type TEXT,
  thread_id INTEGER,
  complete BOOLEAN DEFAULT FALSE
);

CREATE TABLE reagent_steps (
  id INTEGER NOT NULL DEFAULT nextval('reagent_steps_id_seq'::regclass),
  message_id INTEGER,
  step JSONB,
  artifact JSONB DEFAULT '{}'::jsonb
);

CREATE TABLE reagent_threads (
  id INTEGER NOT NULL DEFAULT nextval('reagent_threads_id_seq'::regclass),
  uuid UUID NOT NULL DEFAULT gen_random_uuid(),
  owner_email TEXT,
  created TIMESTAMP WITH TIME ZONE DEFAULT now(),
  title TEXT
);

• Update the workflow's trigger to reference your newly created workflow ID after import.
• Connect your preferred LLM resource (e.g. OpenAI) inside the workflow blocks.

How Does the Recursive Workflow Pattern Work?

One of the more interesting design decisions in the Reagent example is that the orchestrator workflow calls itself recursively to loop through agent iterations. If you're wondering whether this causes call stack issues — it doesn't. Retool Workflows are orchestrated with Temporal, which means each recursive call is queued and managed server-side, not on the client or in a JS call stack. There's no browser memory risk, and no stack overflow to worry about.

That said, you could also restructure the workflow using a loop block or a multi-step function to avoid recursion entirely. The recursive approach was a deliberate design choice for this prototype, not a requirement. Multi-step functions in particular are worth exploring — they let you encapsulate reusable logic (like a saveState subflow) and keep your workflow graph clean even as complexity grows.

What's Missing From Retool AI Agents Today?

The current approach works, but it's complex. Building a production-grade agent using raw Retool Workflows requires you to manually wire up memory tables, manage state across iterations, handle recursive triggers, and build your own orchestration logic. Compared to dedicated agent tools, the setup overhead is significant.

The gaps the community has flagged most often include:

• No native memory component — you have to roll your own with database tables.
• No built-in tool-calling UI — connecting workflows as agent tools requires custom plumbing.
• No visual agent orchestration layer — state management across iterations is manual.
• Complexity is high relative to dedicated automation tools that have native agent nodes.

What's Coming: Retool's Native AI Agent Product

According to Retool's team, a purpose-built AI Agent product is in active development. Based on what's been shared publicly, the roadmap includes:

• A workflow-like authoring UI specifically designed for agent logic — not just general workflow blocks.
• Native memory support, either built into Retool or via third-party memory integrations.
• The ability to pass state across agent iterations without manually managing database tables.
• Retool Workflows as callable tools for the agent — so your existing automation work isn't wasted.

This is a significant upgrade from the current DIY approach and is aimed squarely at teams who want to ship reliable, production-safe AI agents without building the orchestration layer from scratch.

Should You Build AI Agents in Retool Right Now?

If you need something in production today and your team is comfortable with Retool Workflows, the Reagent pattern is a solid starting point. It's genuinely functional — just more complex than it needs to be. If you can wait, the native agent capabilities coming soon will dramatically reduce the setup burden.

Either way, now is a good time to get familiar with the patterns: multi-step functions, recursive workflow triggers, and state management in Retool DB. Those foundations will carry over directly into whatever Retool ships as its official agent product. If you want hands-on help adapting this for your specific use case, reach out to our team — this is exactly the kind of internal tooling work we do every day.