AI Image Generator

Generate images from text prompts with OpenAI DALL-E. FlowRunner agents produce visuals on demand and pass the hosted URL straight into downstream flow steps.

3 actions available

A new row lands in a content sheet with a prompt column

Agent reads the prompt, target size, and quality from the row

Agent confirms the requested size and quality are supported options

Image generated via DALL-E and returned as a hosted URL

URL written back to the source row for the content pipeline

Design channel notified that a new asset is ready

Brand-sensitive prompts routed to a reviewer before the image publishes

What This Integration Enables

Agents generate images from natural-language prompts without leaving the workflow. The connector returns a hosted URL, so the output feeds directly into a transactional email, a spreadsheet cell, a chat message, or a storage upload. Two helper actions return the supported size and quality options, so a flow can validate a request before spending a generation on it. The connector covers on-demand asset creation, not asset management; the surrounding flow decides where each image goes.

Without FlowRunner

Bottlenecked on design Every routine visual waits in a designer's queue behind higher-priority work

Disconnected assets Generated images live in a chat thread and get copied around by hand

No pipeline Prompt, image, and the system that uses it are three separate manual steps

With FlowRunner

Visuals on demand Routine images are produced inline the moment the flow needs them

URL feeds the next step The hosted image URL passes directly into email, storage, or chat

Reviewer sees brand-sensitive work A human approves the cases that carry brand risk before they go live

Use Case Scenarios

Content Pipeline Image Generation

A content team keeps a planning sheet where each row holds a headline and an image prompt. A Google Sheets new-row trigger fires. The agent reads the prompt, checks the requested size against Get Size Options, and calls Generate Image. It writes the returned URL back into the row's image column and posts a note to the design Slack channel that the asset is ready. The marketing operations lead moves from waiting on a queue to reviewing finished candidates.

Personalized Email Visuals

A customer milestone fires in the e-commerce system. The agent builds a prompt from the customer's product and context, calls Generate Image to produce a personalized visual, and passes the hosted URL to Brevo's Send Transactional Email so the image renders inline. The notification carries a relevant graphic without a designer touching each individual send.

Design Review Gate for Brand Work

A campaign prompt is flagged as brand-sensitive in the request. The agent generates a draft image but does not publish it. It posts the image URL and the originating prompt to the brand reviewer with a single question. The reviewer approves, requests a regeneration, or rejects. Only approved visuals advance to the publishing step.

Human-in-Loop Highlight

DALL-E output is an input to a human decision, not a substitute for one. When a prompt is tagged brand-sensitive, the agent generates the candidate image and then pauses, routing it to the brand reviewer via Slack: "Generated image for the spring campaign hero. Prompt: '[prompt text]'. Approve for publish, regenerate, or reject?" The reviewer sees exactly what the model produced and decides what happens next. Routine assets flow through automatically; the visuals that carry brand risk get a human in the loop before they ship.

Agent processes routinely

Detects exception requiring judgment

Clear match Continues automatically

Ambiguous Routes to human via Slack

Human decides

Agent resumes with decision

Agent Capabilities

3 actions

Image Generation

Generate Image Generates an image from a text prompt using OpenAI DALL-E models and returns a hosted file URL. Used whenever a flow needs a visual on demand, from product mockups to email graphics, with the URL passed straight to the next step.
Get Size Options Returns the available output sizes. Used as a pre-check so a flow can validate or select dimensions before spending a generation.
Get Quality Options Returns the available quality levels. Used as a pre-check so a flow can validate or select quality before generating.

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