Chains break in production
A single LLM call chain is not a system. Multi-agent paths fail mid-route with no recovery story.
AI orchestrating service · MVP
Orchestrate with confidence
Hosted multi-agent workflow runtime for builders who ship. Define graphs, run them reliably, recover failed steps—without inventing orchestration infrastructure.
Built for technical founders and small eng teams. Reliability over demo magic.
Live run · order_fulfillment
v3 · healthy
RouterLLM CallToolGuard
Runs
128
Success
98.7%
P95
312ms
Retrying
1 step
Retrying failed step
Attempt 2 of 3 · exponential backoff
The gap
Multi-agent systems need a runtime, not another prompt chain
State handoff is hand-rolled
Passing context between agents becomes ad hoc glue—brittle, unobserved, and hard to replay.
Ops is an afterthought
Retries, timeouts, and failed-step recovery get bolted on late, usually after an outage.
How it works
Define. Run. Recover.
Start narrow on the multi-agent workflow wedge—own routing, state, and recovery before expanding the surface.
- 01
Define the graph
Compose agents, tools, and routing rules into a workflow you can version and ship.
- 02
Run with control
Execute concurrent paths with clear status, latency, and step-level visibility.
- 03
Recover and adapt
Retry failed steps with policy, resume from state, and keep the system moving.
Runtime
Production control for coordinated agents
lu.computer is the hosted layer for multi-agent execution—not a place to build the agents themselves. Focus on graphs that survive contact with production.
Routing & coordination
Direct work across agents with explicit graph edges—not hidden prompt spaghetti.
State handoff
Carry structured state between steps so agents pick up where the last one left off.
Failure recovery
Exponential backoff, max attempts, and on_error policies that keep workflows alive.
Operational control
See runs, statuses, and bottlenecks without building a custom ops plane first.
Failure is a first-class path
Recover. Adapt. Keep going.
When a step fails, policy decides the next move—retry, recover, or escalate—so your multi-agent graph does not die on the first tool timeout.
- Explicit retry strategy and max attempts per step
- State preserved across handoffs for resume
- Operator-visible alerts while the run stays alive
graph.runBuilder native
graph.run(workflow_id, {
inputs,
retry: {
strategy: "exponential",
max_attempts: 3,
backoff_ms: 500,
},
on_error: "recover",
})Early access
Ship multi-agent systems without the ops tax
We are shaping the runtime with technical adopters. Request access, or open the product shell if you already have credentials.