Multi-agent systems

Several models or agents working on one task — dividing the work and checking each other.

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What it is

In a multi-agent setup, different models or agents take different roles — planner, specialist, reviewer — and their results are combined. Done well, it can improve quality on hard tasks; done badly, it multiplies cost and confusion.

Example

One model drafts an analysis, a second checks the numbers and a third reviews the conclusions before the report is released.

What can go wrong

Errors and injected instructions can travel from one agent to the next, and agreement between models is not proof of correctness.

How to secure it

Verify results with tests and evidence rather than model agreement, keep each agent's permissions separate, and log every hand-off.

In Obsigen

In Titan mode, Obsigen GRID has several models collaborate on a task, then checks the result with evidence-based verification before it passes the output gate. About Obsigen AI

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