Orchestrating Multiple Models for a Development Task

Developers often juggle multiple large‑language models to get the best result for different parts of a codebase. One model might excel at generating boilerplate, another at explaining edge cases, and a third at testing. The challenge is coordinating these outputs into a single, coherent workflow without spending hours on manual reconciliation. VIBA addresses this by letting you run several AI models in one shared session, assign each a role, and review key steps yourself before anything moves forward. This article walks through a concrete, hypothetical example of splitting a coding task across specialised agents with a human approval step, and highlights how cost visibility fits into the picture.

Giving Each Model a Clear Role

In a typical multi‑model setup, the first decision is which model handles which sub‑task. You might send a design prompt to Claude for its strength in structured thinking, forward implementation details to ChatGPT for code generation, and ask Gemini to verify syntax or suggest alternatives. The key is that each model receives a focused prompt rather than a vague “write this feature” request. This not only improves output quality but also makes it easier to compare how different models approach the same problem. VIBA’s shared session means you can see each agent’s reasoning, output, and associated cost side by side, without hopping between separate chats or interfaces.

A Hypothetical Feature Split with Approval Steps

Imagine you’re adding a new “dark mode” toggle to a web app. Instead of asking a single model to do it all, you split the work:

1. Model A (ChatGPT) generates the component markup and CSS custom properties for the toggle.
2. Model B (Claude) reviews the markup for accessibility concerns and suggests ARIA labels.
3. Model C (Gemini) writes a small Jest test suite to verify the toggle switches classes correctly.

After each agent finishes, VIBA pauses the workflow and presents a summary screen to you. You can accept the output, ask the agent to refine a specific part, or reject it outright. Only after your approval does the next agent receive the baton. This human‑in‑the‑loop step ensures that no single model’s blind spot propagates downstream, and it keeps you in control of the final code quality.

To make this concrete, here’s a minimal VIBA‑style configuration snippet that defines the agent roles and the approval gate:

agents:
  - name: generator
    model: chatgpt
    role: generate component markup and CSS for dark‑mode toggle
    approval: required
  - name: auditor
    model: claude
    role: check accessibility, suggest ARIA improvements
    approval: required
  - name: tester
    model: gemini
    role: write Jest tests for toggle behavior
    approval: optional
workflow:
  - generator
  - auditor
  - tester
approval_mode: sequential

The approval: required flag on the first two agents means VIBA will stop and show you the generated markup and the auditor’s suggestions before proceeding. The third agent’s optional approval lets you choose whether to review the tests immediately or trust the model’s output given the prior steps were vetted.

Tracking Cost Per Agent, Not Just Output

One of the biggest pain points in multi‑model work is hidden cost. Each model has its own pricing, and without a central view, it’s easy to lose track of what you’re spending. VIBA surfaces a cost breakdown per agent, so you can see at a glance that the generator used 3,000 tokens, the auditor 1,200, and the tester 800. This visibility doesn’t just help with budgeting—it informs future role assignments. If you notice the tester consistently produces tests that need heavy revision, you might shift that responsibility to a different model or adjust the prompt length. The goal is deliberate, informed choices rather than guesswork.

Wrapping Up

Splitting a coding task across specialised AI agents with a human approval step is less about replacing developers and more about extending their capacity. By giving each model a well‑defined role, inserting clear approval gates, and keeping cost in view, teams can move faster while maintaining the quality checks that matter. VIBA provides the orchestration layer that makes this pattern repeatable across features, fixes, and experiments—without locking you into a single vendor or workflow.

If you’re curious about how this looks in practice, head over to https://viba.guru to explore the platform and see how multi‑agent orchestration can fit into your own development cycle.

HASHTAGS: multi-agent-ai, llm-comparison, devtools, orchestration

See VIBA: https://viba.guru