Governed teammates, not bots
In Nexa Agora, agents are not faceless automations. They are named members of the organization: role-based, configurable, capability-bounded, and accountable inside the same operating model as the humans they support.
What makes an agent a teammate
The important point is not the number of agents. The important point is that every agent has a defined role and visible boundaries. Trace each part on the registry sheet.
The agent roster
Drafting and editorial support — briefs, articles, newsletters — always as drafts that move through human review.
Teams can also create their own specialist agents for the workflows they run.
Three classes of agents
The first-party teammates the workspace ships with — the roster above. Defined and maintained by Nexa, they hold the platform's core roles and cannot be modified or deleted.
Agents your team creates — and can delete — for its own workflows: granted only capabilities the platform allows, tuned to your specific needs.
Outside intelligence admitted through the Nexa Bridge — assistants connected through MCP, your own models and devices — always under a human owner, inheriting that human's permission boundary.
Prompt is not permission
A prompt can describe what an agent is supposed to do — tone, role, expertise, behavior. But it does not grant authority. What an agent can actually do is the intersection of two layers.
Agents Studio turns agent creation into an organizational act
Creating an agent is not just writing a prompt. It means deciding what kind of teammate the organization needs, what work that teammate can support, which model it uses, what memory and tools it may access — and where its authority must stop.
Limits should be readable
Capabilities should not be understandable only to engineers. Trust badges make agent limits readable at a glance — what is enabled, what is blocked, and what requires approval.
Autonomy is granted in measured doses
Nexa Agora avoids two weak extremes: agents too passive to help with real work, and agents acting invisibly beyond human control.
The system records what happened.
Agents work where the work lives
Useful AI work is rarely a single answer. It involves context, coordination, memory, review, and follow-through — so agents participate across the whole workspace, not only in chat.
Design your first governed AI teammate
Start with one role your team already needs: research, review, coordination, onboarding, operations, or delivery support.