Infrastructure for agents that run in production
Reliable routing, long-running tasks, predictable costs. Built for agents that make decisions without a human in the loop.
- Deterministic fallbacks
- Per-agent budgets
- Long-context support
Your agent doesn't care which provider answered
It cares that the answer came back, matched the schema, and stayed under budget. We make sure all three happen.
Deterministic fallbacks
When a provider times out mid-task, your agent shouldn't fail. We route to the next model before your code notices.
Per-agent budgets
Cap spending per agent, per session, per end-customer. Hard cut-off on overrun — no runaway loops burning the monthly cap.
Long-context, long-running
Million-token windows. Multi-hour tool-calling loops. Async job patterns for tasks that outlast a single HTTP request.
Structured outputs guaranteed
Strict JSON schemas. No more regex-parsing model output and no more silent schema drift when you switch models.
Full observability
Every call logged. Trace latency, tokens, cost per agent run. Filter by agent, session, customer. Export to your warehouse.
Model-agnostic by design
Pin a model, or let the router pick. When a new frontier model lands, switch without rewriting your agent.
Agents that survive provider outages
Chain models by preference. If Claude 5xx's, Gemini takes the call. If Gemini rate-limits, GPT does. Your agent stays running — provider downtime becomes our problem, not yours.
- Ordered fallback per API key
- Retry budget per request
- Circuit breakers around failing upstreams
- Post-incident traces
Hard budgets per agent, per customer
Runaway loops don't cost you a month's worth of credits. Set a budget per agent-id or customer-id header; we enforce it at the edge.
- Per-key, per-agent, per-session budgets
- Hard cut-off at cap (no overage)
- Real-time alerts on burn rate
- Per-customer cost breakdowns
Frequently asked questions
How does fallback behavior work for agents?
You define an ordered list of models per API key. On 5xx, timeout, or rate-limit, we retry the next one automatically. The agent sees one successful response — not an error and a retry loop.
Can I pin a model version for determinism?
Yes. Every model has pinned versions (e.g., gpt-5-4-20260210). Use pinned versions in production agents so behavior doesn't drift on model updates.
What's the SLA?
99.9% uptime on paid plans, 99.95% on Enterprise with custom contracts. Fallback chains give you provider-level redundancy on top of ours.
How do I monitor cost per agent?
Tag every request with an agent-id / session-id / customer-id header. The dashboard breaks down usage and cost per tag, with export to CSV or your warehouse.
Can I run long-running async tasks?
Yes. For tasks that outlast a single HTTP request, use the async job API — submit, poll, or webhook on completion.
What about tool calls and MCP?
OpenAI-style tool calls work across every model. Native MCP (Model Context Protocol) server support is in beta.
Put your agent in production
OpenAI-compatible. Fallback chains. Per-agent budgets. Everything production-grade agents need, nothing you don't.