VIBA enters the scene as a shared-session platform that lets you run several AI models—ChatGPT, Claude, Gemini, and others—side by side, assign each a role, and keep a human in the loop for key decisions. For teams that want to explore multi-agent AI without committing to a single provider, this approach offers a practical way to compare how different models handle the same coding problem, while retaining oversight at critical junctures.

Assigning specialised roles in a single session

A typical coding task can be broken into distinct sub‑problems: understanding the requirements, designing the architecture, writing the implementation, and testing the result. In VIBA, each of these can be handed to a different model, with its own system prompt and role label. One model might act as an architect, another as an implementer, and a third as a reviewer. Because they operate within the same session, their outputs are visible to one another and to you, the human operator, in one consolidated view. This setup makes it straightforward to observe how a ChatGPT‑based agent frames a solution versus how Claude or Gemini approach the same sub‑task, all without leaving the platform.

From generation to the approval checkpoint

Once the models produce their initial outputs, the workflow pauses for a human approval step. Imagine you’re adding a new data‑validation layer to an existing service. The architect agent proposes a design, the implementer agent generates a pull‑request‑ready code block, and the reviewer agent drafts a set of test cases. Before anything is merged, you review the combined output, check for edge cases, and either sign off or ask for revisions. This approval checkpoint is where the “human-in-the-loop” aspect becomes concrete: you’re not just passively consuming output, you’re actively deciding what moves forward. The platform records which agent contributed which piece, making the review process traceable and accountable.

Cost visibility as a design choice

One of the more practical benefits of VIBA is per‑agent cost tracking. Each model’s usage is logged separately, so you can see, for example, that the Claude‑based reviewer consumed fewer tokens than the Gemini‑based architect for the same class of work. This visibility turns cost from an afterthought into a deliberate design choice: if you notice one model consistently generating verbose scaffolding while another stays concise, you can adjust role assignments or system prompts accordingly. Over a series of tasks, this data helps a small team calibrate their multi-agent AI usage without guesswork.

A concrete config snippet

While the exact interface may evolve, a typical VIBA session might start with a configuration that labels each model and maps them to roles. Here’s a hypothetical example of how such a setup could be expressed:

session: coding-task-2025-05-20
agents:
  architect:
    model: gpt-4o
    role: design
    system_prompt: "You are a senior software architect. Produce a concise design brief for the given requirement, including data flow, component responsibilities, and potential risk points."
  implementer:
    model: claude-3-5-sonnet
    role: code
    system_prompt: "You are a pragmatic full‑stack developer. Write production‑ready code snippets that follow the design brief, adhering to the project’s existing style guide and avoiding deprecated patterns."
  reviewer:
    model: gemini-1.5-pro
    role: test
    system_prompt: "You are a QA engineer. Based on the implementation, generate a minimal set of unit tests that cover the critical paths and edge cases. Highlight any assumptions you make."
human_approval: true

In this example, the human_approval flag signals that no output should be finalized without your sign‑off—a safeguard for teams that can’t afford unchecked auto‑merge behavior.

Moving forward

The combination of role‑specific models, a shared session, and an explicit approval step gives developers and small teams a way to harness multi‑agent AI that feels both powerful and controlled. You get the comparative insight of running different LLMs on the same problem, the transparency of seeing who contributed what, and the safety net of a human decision point before anything ships. If you’re curious about how this might look in practice, the platform is set up for exactly that kind of experimentation.

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