Definition · AI in finance
Multi-agent system
Multi-agent system is multiple AI agents coordinating, often in parallel, on a complex task. For multi-agent system, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
Also known as MAS, multi-agent AI
Why it matters
Understanding multi-agent system matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo orchestrates multiple agents — planning, analysis, verification — over a shared semantic model, so every team sees the same answer to the same question.
In practice
Governance example
Teams use multi-agent system when they evaluate whether an AI-assisted analysis can be trusted. The useful test is whether the output is tied to approved data, repeatable logic, human review, and an audit trail.
Pluvo example
Pluvo orchestrates multiple agents — planning, analysis, verification — over a shared semantic model, so every team sees the same answer to the same question.
In practice, teams should define multi-agent system with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding multi-agent system matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo orchestrates multiple agents — planning, analysis, verification — over a shared semantic model, so every team sees the same answer to the same question.
A strong workflow for multi-agent system separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
Pluvo orchestrates multiple agents — planning, analysis, verification — over a shared semantic model, so every team sees the same answer to the same question.
FAQ
What is a multi-agent system?
Multi-agent system is multiple AI agents coordinating, often in parallel, on a complex task. For multi-agent system, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
Why use multiple AI agents instead of one?
To use multi-agent system, start with the decision, then confirm the source data, timing, calculation logic, and owner. The analysis is strongest when a reviewer can trace the answer back to the records that produced it.