Medium Risk

chat_with_agent

Send a message to an agent. Defaults to the active workspace assistant (assistant@...) when no id or agent_slug is provided. Use list_agents first when you want to choose a different agent. The agent's instructions are used as the system prompt. Supports multi-turn conversations via session_id.

Part of the Agentled server.

chat_with_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 chat_with_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 chat_with_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": {
    "chat_with_agent": {
      "limits": [
        {
          "counter": "chat_with_agent_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Agentled policy for all 119 tools.

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

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so chat_with_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 chat_with_agent tool do? +

Send a message to an agent. Defaults to the active workspace assistant (assistant@...) when no id or agent_slug is provided. Use list_agents first when you want to choose a different agent. The agent's instructions are used as the system prompt. Supports multi-turn conversations via session_id.. 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 chat_with_agent? +

Register the Agentled MCP server in PolicyLayer and add a rule for chat_with_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 chat_with_agent? +

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

Can I rate-limit chat_with_agent? +

Yes. Add a rate_limit block to the chat_with_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 chat_with_agent completely? +

Set action: deny in the PolicyLayer policy for chat_with_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 chat_with_agent? +

chat_with_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.

Enforce policy on every Agentled tool call.

Deterministic rules across all 119 Agentled tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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