Send a message to the AgentLed AI agent and get a response. The agent can reason, plan, and build workflows through natural language conversation — no need to construct pipeline JSON manually. Use this tool when you want to: - Build a workflow from a high-level description ("Create a lead enrichm...
Part of the Agentled server.
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AI agents invoke chat to trigger processes or run actions in Agentled. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
chat can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"chat": {
"limits": [
{
"counter": "chat_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Agentled policy for all 119 tools.
These attack patterns abuse exactly the kind of access chat gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Send a message to the AgentLed AI agent and get a response. The agent can reason, plan, and build workflows through natural language conversation — no need to construct pipeline JSON manually. Use this tool when you want to: - Build a workflow from a high-level description ("Create a lead enrichment workflow for SaaS companies") - Get recommendations on how to structure a workflow - Ask questions about available integrations or capabilities - Iterate on workflow design through conversation The agent has access to the same planning tools, workflow builder, and workspace context as the in-app chat. For multi-turn conversations, pass the session_id returned from the first message to maintain context across messages. Example: chat("Build me a workflow that takes a LinkedIn company URL, enriches the data, and scores it by ICP fit"). It is categorised as a Execute tool in the Agentled MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Agentled MCP server in PolicyLayer and add a rule for chat: 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.
chat is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the chat 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.
Set action: deny in the PolicyLayer policy for chat. 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.
chat 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.
Deterministic rules across all 119 Agentled tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.