IT admin monitoring agent activity across the organization
See what every AI employee is doing in real time. Intervene, pause, or redirect without waiting for a report.
Self-Managing Agents
Your employee operates the platform itself, changing settings, managing tools, and configuring the system on your behalf.
Your AI employee does not just work inside external tools. It operates the AI Employee platform itself. Ask it to hire a new team member, adjust a skill configuration, connect a tool, or update a duty, and it navigates the platform interface and makes the changes for you. You give instructions. It handles the clicks.
This turns every employee into a self-service administrator. Instead of opening settings panels and toggling options yourself, describe what you want in chat. "Add the SEO writing skill." "Connect my Google Calendar." "Change the approval mode to always ask before sending emails." Your employee configures itself and the platform around it.
Platform control is especially powerful for team leaders. A leader can onboard new hires, assign skills across the team, adjust schedules, and reorganize the org chart, all through conversation. The platform becomes something your AI workforce manages, not something you have to operate manually.
An AI Employee That Manages Itself and Its Teammates
Platform Control gives a designated AI employee, typically a team lead or coordinator role, the ability to operate the Sistava platform directly. It can update its own settings, assign tasks to other AI employees, adjust configurations, and manage the workforce it is part of. Self-administering AI teams without constant human input.
This is not theoretical automation. A coordinator agent can receive a request like "spin up a research assistant for the new client project," handle the configuration, assign skills and tools, and confirm when the new employee is ready. Human oversight remains in place, but the routine administrative work is handled by the agent.
Self-Configuration Based on Context
Platform Control enables agents to self-configure when context changes. If a new project requires a skill the agent does not currently have, it can request or activate it. If a tool is no longer needed for the current sprint, the agent can remove it from its active set. Configuration that adapts to work, not the other way around.
This dynamic reconfiguration is audited. Every platform action an agent takes is logged with timestamp and justification. Managers can review and override any change. The agent operates with authority bounded by your policies.
The Foundation for True Agent Orchestration
Multi-agent orchestration at scale requires agents that can coordinate without a human mediating every interaction. Platform Control is how Sistava enables that. A lead agent can delegate to specialist agents, monitor their progress, reassign blocked work, and compile results, all within the platform.
For enterprises running complex AI workflows, this means a meaningful reduction in the management overhead of operating a large AI workforce. The agents handle more of their own administration, freeing human managers to focus on goals and outcomes rather than configuration and coordination.
Use Cases
Operations lead reviewing and approving agent actions before they execute
Set approval requirements on sensitive actions. The AI agent pauses and waits for a human decision before proceeding.
Security team auditing all agent decisions and tool usage
Full activity logs let you trace every action an AI employee took, why it took it, and what it produced.
Manager adjusting agent behavior without rebuilding it
Change rules, skills, or access controls on a live agent. Updates take effect immediately without downtime.
Comparison
| Before | After |
|---|---|
| Once an AI agent runs, you have no visibility into what it's doing. | Real-time monitoring shows every action, decision, and result. |
| Sensitive actions execute automatically with no human checkpoint. | Configure approval gates that pause the agent and wait for your sign-off. |
| Debugging agent behavior means sifting through unstructured logs. | Structured activity trails make every decision traceable and reviewable. |
| Changing agent behavior requires a full reconfiguration. | Update rules and controls on a live agent without any downtime. |
FAQ
Which AI employees can use Platform Control?
Platform Control is a capability you assign explicitly. Typically it is given to a coordinator or lead agent role. Not every AI employee in your workforce needs or should have the ability to modify platform settings.
Can a Platform Control agent hire new AI employees without approval?
No. Actions that affect billing, like hiring new employees or upgrading plans, always require human approval. Platform Control covers operational management like task delegation and configuration. Billing changes stay with human administrators.
Is there an audit log for actions taken via Platform Control?
Yes. Every platform action taken by an agent is logged with a timestamp, the agent that initiated it, and the justification it provided. Managers can review and reverse any action from the audit log.
How does Platform Control differ from just assigning tasks to multiple agents?
Task assignment is manual. Platform Control lets an agent actively manage other agents: delegating work dynamically, adjusting their configurations, and monitoring their progress, without a human facilitating every step. It is the difference between a human coordinator and an AI coordinator.
Can my AI agent update its own settings without me doing it manually?
Yes. Self-managing agents can adjust their own skills, memory, and configuration through conversation when given the appropriate permissions. This reduces the overhead of manually maintaining each employee.
I told my agent it would be handling investor relations going forward. It added the relevant skills, updated its own persona description, and briefed the rest of the team. I did not touch a single setting.