From brief to prototype: OJO brings a team of agents together on one canvas

OJO brings specialized agents into an editable canvas to turn an idea into product strategy, a PRD, a prototype, and code.

Generating an interface from a prompt is no longer the main differentiator among AI design tools. OJO aims to step in earlier, during product definition, and preserve context through the interactive prototype and development handoff.

Presented as a “Design Agent Team Workspace,” the service lets users assemble a team of specialized agents around the same project. The goal is to turn a rough idea into a product strategy, a product requirements document, user flows, an interface, and a prototype. Everything remains on an editable canvas instead of being scattered across multiple conversations and applications. OJO describes a continuous workflow spanning product reasoning, design, code generation, and launch.

The team’s composition depends on the project. Agents can contribute to needs analysis, page structure, visual direction, interactions, or design review. Skills add more targeted methods and constraints covering areas such as typography, motion, information hierarchy, performance, and quality control. These capabilities can be added or removed as the project progresses, without forcing every assignment through the same process.

This structure is mainly designed to prevent a general-purpose agent from simultaneously acting as a product manager, researcher, information architect, visual designer, and reviewer. The different agents share the same objective, references, previous decisions, and user feedback. OJO presents this shared context as the connection between research, structure, design, revisions, and delivery.

The canvas contains the pages and prototypes produced by the team. Users can select and directly edit individual elements, leave localized comments, or request a new version of a specific section. Desktop, tablet, and mobile previews help check how the interface adapts across devices. The result can then be shared, deployed, or exported to Figma and formats including HTML, PDF, PPT, and PNG. OJO also provides a path into Codex, Claude Code, Cursor, and other development tools.

The service offers three generation profiles. Master prioritizes visual quality at the cost of higher credit consumption, Fast is intended for repeated experimentation and batch production, while Pro aims for a more stable balance when moving toward code. Two modes complement these options: Thinking for exploring a problem through conversation and Agile for executing an already well-defined request. The documentation does not specify which models power each profile. The service’s terms state more broadly that the platform uses Gemini and Claude, among other models.

In one use case published by OJO, the starting point is a simple idea for an app that helps people find local activities. Before producing any screens, the agents define the target audience, narrow the MVP, and identify a testable user journey. The resulting prototype includes six connected screens, covering the path from discovering an activity to registering for it. The demonstration illustrates the intended continuity between product definition, structure, and prototyping, but it remains a scenario prepared by the company rather than an independent evaluation.

Agent-powered canvases and Skills are not unique to OJO. Google Stitch already provides an infinite canvas connecting early ideas to working prototypes, while Figma allows agents guided by Skills to work directly inside design files. The claim that OJO is the “first” workspace of its kind should therefore be treated as commercial positioning. Its intended distinction lies more in the orchestration of an agent team and the expansion of the workflow into product strategy and PRDs.

The promise that “taste can be engineered” also deserves some qualification. Skills can formalize brand rules, references, composition principles, and review criteria. They may make these choices more consistent and reusable. They do not, however, turn aesthetic judgment into a measurable property or guarantee that several agents will reach compatible decisions. Orchestration may also introduce contradictions, repetition, and additional validation work.

Professional projects also require careful scrutiny of how data is handled. OJO’s privacy policy states that deidentified prompts, references, and creations may be used to improve its models. Users can opt out, but the choice only applies to future processing. The terms transfer ownership of outputs to users once the relevant credits have been paid, while leaving them responsible for checking potential similarities and third-party rights.

OJO is currently in private beta. Access requires joining a waitlist or using an invitation code, while usage is based on subscriptions and credits. The actual quality of collaboration between agents, exported code, and design-system consistency across complex projects will still need to be tested beyond the launch demonstrations.