[Deprecated — use rerun instead] Retry a failed step in a workflow execution. If no timelineId is provided, the most recent failed timeline is automatically detected and retried. This re-runs the failed step and continues the workflow from that point.
Part of the Agentled server.
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AI agents call retry_execution to retrieve information from Agentled without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though retry_execution only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"retry_execution": {}
}
}See the full Agentled policy for all 119 tools.
These attack patterns abuse exactly the kind of access retry_execution gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
[Deprecated — use rerun instead] Retry a failed step in a workflow execution. If no timelineId is provided, the most recent failed timeline is automatically detected and retried. This re-runs the failed step and continues the workflow from that point.. It is categorised as a Read tool in the Agentled MCP Server, which means it retrieves data without modifying state.
Register the Agentled MCP server in PolicyLayer and add a rule for retry_execution: 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.
retry_execution is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the retry_execution 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 retry_execution. 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.
retry_execution 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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