Dedicated AI agents that can actually take action
Provision a managed OpenClaw container inside Tulsk. Give each agent its own persona, tools, browser, shell, and persistent workspace so it can investigate, execute, and report back in task threads.
What you control in Tulsk
Tulsk adds the operational layer around OpenClaw so autonomous agents are usable in a real team workflow.
Dedicated runtime per agent
Each OpenClaw agent gets its own provisioned container with browser, shell, web search, and a persistent workspace instead of sharing a generic chat session.
Persona files you can actually edit
Customize IDENTITY.md, SOUL.md, USER.md, and TOOLS.md from the agent form, then reprovision when you want those changes applied to the live runtime.
Operational controls, not black boxes
See provisioning and health status, start or stop containers, restart unhealthy runtimes, and verify whether the saved persona version matches the one reported by the instance.
Skills and workspace context
Attach skills to agents and import new ones from URLs or files so OpenClaw can work with the context and workflows your team depends on.
Where OpenClaw makes sense
Use OpenClaw when the job needs tools, runtime state, or more than a single quick model response.
Assigned engineering or research tasks
Hand an agent a real task, let it inspect the codebase or browse the web, and have the result delivered back into the Tulsk thread for review.
Longer-running operational work
Use OpenClaw when a task needs a dedicated container, external tooling, or scheduled execution instead of a short built-in response.
Managed agent operations
Run multiple specialized agents with separate personas, skills, and runtime controls so your team can see which instance is provisioned, stopped, or needs recovery.