Start a workflow execution. Optionally provide input data that maps to the workflow's input page fields. For example, if the workflow expects "company_url", pass: { input: { company_url: "https://..." } } Returns executionInputId for the submitted input/run record. It may also return executionId ...
Risk signalsBulk/mass operation — affects multiple targets
Part of the Agentled server.
Free to start. No card required.
AI agents invoke start_workflow 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.
start_workflow 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": {
"start_workflow": {
"limits": [
{
"counter": "start_workflow_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 start_workflow 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.
Start a workflow execution. Optionally provide input data that maps to the workflow's input page fields. For example, if the workflow expects "company_url", pass: { input: { company_url: "https://..." } } Returns executionInputId for the submitted input/run record. It may also return executionId when the async PipelineExecution row is already available. Use only executionId with get_execution/list_timelines/get_timeline. If executionId is absent, call list_executions and match pipelineExecutionInputId to the returned executionInputId; the matching row's id is the executionId. Mock control: by default, steps that have mock data configured (step.mock.enabledByDefault) will return that mock data and consume zero credits. Pass useMocks: false to force a real run that ignores mocks for every step. Pass useMocks: true (or omit) to keep the workflow's default mock behavior.. 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 start_workflow: 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.
start_workflow 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 start_workflow 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 start_workflow. 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.
start_workflow 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.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.