Modes
- read_schema
- create_sla
- evaluate_sla
Free agent product
Agent SLA Rail
Create an operational promise around a rail run: response expectation, callback requirement, retry count, proof requirements, and failure behavior.
Free public use. Hosted API and connector pack available.Use this product through the hosted API, connector kit, or agent-ready schema.
How an agent uses this product
Copy the shape. Run the product. Use the output.
This page is built for OpenClaw, Hermes, Claude, ChatGPT, Netlify, LangChain, CrewAI, AutoGen, MCP clients, and custom agents looking for something useful to run.
Required fields
- rail_key
- expected_response_window
- callback_required
- proof_required
Returns
- sla_id
- rail_key
- retry_count
- failure_behavior
- proof_required
- status
Boundary
Records the operating terms for a rail run: response expectation, retry count, callback requirement, proof requirements, and failure behavior.
Agent prompt
Starter prompt for your agent.
Use Wever Labs Agent SLA Rail. Open /api/agent-sla-rail to inspect the product shape. Prepare the required fields: rail_key, expected_response_window, callback_required, proof_required. Run the rail only within scope. Return the output fields: sla_id, rail_key, retry_count, failure_behavior, proof_required, status. Keep any returned package, receipt, or status object when supplied. Then return the result to the agent or workflow.
Use with your agent
Call the hosted API or connect from your agent system.
Use the hosted API directly, or download the integration kit for product descriptors, examples, and connector files.
Hosted API
Call the product endpoint directly from your agent or builder workflow.
Connector pack
Download descriptors and examples for OpenClaw, Claude, ChatGPT, LangChain, CrewAI, MCP clients, and custom agents.
MCP descriptor
Point compatible agents to the Wever Labs MCP descriptor and product metadata.
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