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Coordinating Specialized AI Agents with Explicit Human Checkpoints

#multiagent #llm #developertools #aiworkflow

Introduction

When a coding task grows beyond a single prompt, the natural next step is to delegate parts of the work to models that excel in different domains — one for architecture, another for test generation, a third for performance tuning. VIBA makes this practical by letting you run several LLMs in a shared session, assign each a role, and insert mandatory human approval before any change reaches the repository. The result is a transparent pipeline where cost, latency, and quality are visible per agent.

Defining Roles and Hand‑offs

Start by describing the work in a lightweight YAML manifest. Each agent receives a name, a model identifier, and a concise responsibility statement. The manifest also declares approval gates — points where the orchestration pauses until a developer confirms the output.

agents:
  - name: architect
    model: gpt-4o
    role: "Produce high‑level module layout and interface contracts"
  - name: implementer
    model: claude-3.5-sonnet
    role: "Write production‑ready code for the approved contracts"
  - name: tester
    model: gemini-1.5-pro
    role: "Generate unit and integration tests covering the new code"
approvals:
  - after: architect
  - after: implementer
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When the session starts, VIBA spins up the three models, feeds the architect the initial requirements, and presents the resulting design for review. Only after you accept does the implementer receive the approved contracts. The same gate repeats before the tester runs.

Human Approval in Practice

The approval step is not a generic “looks good” button. VIBA surfaces a diff‑style view for each gate: the architect’s diagram, the implementer’s file changes, the tester’s new test files. You can comment inline, request a revision, or approve with a single click. Because each agent’s output is isolated, you can trace exactly which model introduced a regression or a style deviation.

Cost tracking is attached to every agent. After the run you see a breakdown such as:

  • Architect (GPT‑4o): 1,200 tokens, $0.018
  • Implementer (Claude‑3.5‑Sonnet): 4,500 tokens, $0.067
  • Tester (Gemini‑1.5‑Pro): 2,300 tokens, $0.032

This granularity helps small teams decide whether a cheaper model suffices for a given role or whether the higher‑priced option pays off in reduced rework.

Extending the Pipeline

The same pattern scales to additional stages — static analysis, UI snapshot comparison, or beta‑user simulation — each with its own model and optional gate. VIBA also aggregates the results into a ranked repair report, highlighting the most likely failure points across agents. Teams can then prioritize fixes without sifting through raw logs.

By making the hand‑off explicit and the approval mandatory, the workflow turns “multiple LLMs” from an experiment into a repeatable engineering process. You retain control, the models stay focused, and the cost model stays predictable.

Learn more at https://viba.guru

See VIBA: https://viba.guru

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