Multi-agent workflows you can see and govern

The honest starting point is that one well-scoped agent with the right tools solves most problems. Several agents cost more, take longer and are harder to debug, because a mistake in one handoff surfaces three steps later.

They are worth it for specific reasons, and the strongest reasons are about control rather than cleverness.

• Different permissions. A research agent that reads the web should not hold the credentials of an agent that writes to your finance system. Separating them limits what a single mistake or a malicious input can reach.
• Different tools or context. An agent with forty tools in its prompt chooses badly. Smaller specialists choose better.
• An independent check. A second agent reviewing the first one’s output catches some errors that the first would repeat. It is a useful control, not a guarantee.
• Parallel work. Independent sub-tasks can run at the same time and be merged.
• Different owners. When two teams own two parts of a process, two agents with two sets of limits match how responsibility is actually divided.

In Studio an agent is one component in a visual workflow. You place fixed steps where the logic is known and agents where the work needs reading and judgement. Because they are in the same graph, the whole process has one version, one release and one trace, and a person’s approval is a step like any other.

Studio’s Pro tier lists multi-agent orchestration. Each run records traces, logs and token-cost telemetry per step, so a handoff between agents shows up as a step you can open. Workflow and chatflow versions can be rolled back and chatflow versions can be A/B tested. Golden sets, regression runs, release channels and gradual rollouts are in development.

Each agent in a governed workflow keeps its own identity, permissions and limits. One agent’s authority does not leak into another’s. Nexus can apply policy while the workflow runs, blocking an action outside an agent’s scope and requiring approval where you have said it is needed.

Ask any vendor, including us, to show each of these.

• A separate identity for each agent, and a way to see what each one is allowed to do.
• A trace that shows every handoff, with what was passed and what came back.
• A limit on how many steps, calls and tokens a single run may use, so a loop cannot run up a bill.
• A rule that an agent cannot approve its own consequential action, and a named approver for those that need one.
• A defined stop. Someone must be able to pause or stop a workflow, and the action is recorded.
• A record of the full chain: data, model, agent, decision, action and outcome.

A router agent, a policy agent and a drafting agent cooperate, and a person approves the money.

• Router: reads the request and classifies it as a refund, a complaint or something else, and passes refunds on.
• Policy agent: reads the order and the refund policy and recommends eligible, ineligible or unsure. It can read orders. It cannot issue money.
• Drafting agent: prepares the reply from approved wording. It cannot send it.
• Person: approves any refund above the agreed threshold and any case the policy agent marked unsure, then the reply goes.
• Record: each agent’s input, output and model, the approver and the outcome.

Multi-agent systems are an active area, and the failure modes are still being learned. Agents can pass an error from one to the next, and a reviewer built on the same model may share the author’s blind spots. Test on your own cases, keep people in the loop for consequential steps and raise autonomy only as the record supports it.

Studio integrates 100+ tools. If a process depends on a very wide connector catalogue, check coverage first. Single sign-on through SAML and SCIM provisioning are in development and not generally available.