Execute a multi-step AI workflow. Intermediate results stay in the workflow engine (not your context), providing 90%+ context reduction on complex pipelines. Use for any task requiring multiple model calls or tool integrations. Cost tracks your provider bills, not RelayPlane fees - we
AI agents invoke relay_workflow_run to trigger actions in RelayPlane. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool triggers execution of arbitrary multi-step workflows that can chain multiple LLM operations and tool integrations. While the description is incomplete (cuts off mid-sentence), the core function is to run workflows whose effects depend entirely on workflow definition arguments.
From the tool's definition Tool executes multi-step AI workflows with multiple model calls and tool integrations. Description explicitly states 'Execute a multi-step AI workflow' and mentions cost tracking for provider bills, indicating real resource consumption and external operation…
Attacks that exploit this kind of access
Execute a multi-step AI workflow. Intermediate results stay in the workflow engine (not your context), providing 90%+ context reduction on complex pipelines. Use for any task requiring multiple model calls or tool integrations. Cost tracks your provider bills, not RelayPlane fees - we. It is categorised as a Execute tool in the RelayPlane MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the RelayPlane MCP server in PolicyLayer and add a rule for relay_workflow_run: 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 RelayPlane. Nothing to install.
relay_workflow_run 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 relay_workflow_run 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 relay_workflow_run. 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.
relay_workflow_run is provided by the RelayPlane MCP server (relayplane/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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