Medium Risk

create_agent

Create a new agent with name, instructions, tools, workflows, and optional config files. Agents are always 'chat-only' (conversational). For scheduled/autonomous work, create the agent first, then attach routines to it via create_routine (e.g. daily deal-sourcer, weekly digest). Key fields: - nam...

Risk signalsHigh parameter count (19 properties)

Part of the Agentled server.

create_agent can modify Agentled data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use create_agent to create or modify resources in Agentled. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call create_agent repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Agentled.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "create_agent": {
      "limits": [
        {
          "counter": "create_agent_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

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These attack patterns abuse exactly the kind of access create_agent gives an agent. Each links to the full case and the policy that stops it:

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so create_agent only ever does what you allow.

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Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the create_agent tool do? +

Create a new agent with name, instructions, tools, workflows, and optional config files. Agents are always 'chat-only' (conversational). For scheduled/autonomous work, create the agent first, then attach routines to it via create_routine (e.g. daily deal-sourcer, weekly digest). Key fields: - name: Agent display name - agentType: Preset template — 'personal-assistant', 'competitive-researcher', 'social-media-marketer', 'customer-support', 'content-marketer', 'lead-qualifier', 'deal-sourcer', 'custom' (default) - instructions: System prompt / core AGENTS.md content - enabledApps: App IDs this agent can use — get IDs from list_apps (e.g. ['web-scraping', 'kg', 'gmail']) - assignedWorkflowIds: Workflow IDs this agent can trigger — get IDs from list_workflows - goals: Natural-language description of what the agent should achieve - configFiles: Override generated config files — keys are 'SOUL.md' (persona), 'TOOLS.md' (tool routing). If omitted, files are auto-generated from agentType template. - avatar_icon_name: Lucide icon name for the agent avatar (e.g. 'Bot', 'Radar', 'Target', 'Sparkles') - avatar_color: Hex color for the avatar (e.g. '#6366f1', '#7C3AED', '#EA580C') - linkedFileIds: Workspace-level AgentFile IDs to attach as knowledge (from list_agent_files — workspace scope) - chatModel: Override the chat model (e.g. 'anthropic:claude-4-6-sonnet', 'openai:gpt-4o-mini') - activate: Set true to activate immediately (default false = draft) To add scheduled routines after creating the agent, use create_routine.. It is categorised as a Write tool in the Agentled MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on create_agent? +

Register the Agentled MCP server in PolicyLayer and add a rule for create_agent: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Agentled. Nothing to install.

What risk level is create_agent? +

create_agent is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit create_agent? +

Yes. Add a rate_limit block to the create_agent rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block create_agent completely? +

Set action: deny in the PolicyLayer policy for create_agent. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides create_agent? +

create_agent is provided by the Agentled MCP server (@agentled/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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