The production control plane for multi-agent AI workflows
Routing, fallback chains, token budgets, and human-in-the-loop gates — managed declaratively. Not scattered across five microservices.
workflow: customer-support-v3
routing:
policy: cost_then_quality
primary: gpt-4o-mini
fallback: [gpt-4o, claude-3-haiku]
budget:
max_tokens: 8000
enforce: hardMulti-agent AI in production is an orchestration problem, not a model problem
You have fallback logic copy-pasted across eight agent functions
Every agent that touches a model has its own retry handler, its own cost guard, its own notion of "what happens when this fails." It grows from a sensible abstraction into a maintenance nightmare — eight different timeout values, four different error-handling patterns, zero central visibility.
When a model provider goes down, the incident review reveals the same fallback logic written six different ways, each with a slightly different bug.
Experimentation frameworks don't solve production problems
The frameworks that make building multi-agent prototypes fast — chaining calls, defining agents, wiring tools together — are excellent for development. They're not designed for the operational concerns that emerge at production scale.
There's no routing policy engine. There's no enforced budget system. There's no enterprise governance layer. You end up bolting these on yourself, spread across five microservices, and the glue code becomes the infrastructure.
One control plane. Four production primitives.
A single declarative config defines your entire orchestration policy. OrchVynt enforces it at runtime, emits structured telemetry, and stays out of your agent code.
Built for what breaks in production
Every feature exists because a real production failure required it.
Full workflow observability
Trace every invocation, routing decision, fallback activation, and HITL resolution with structured telemetry.
Declarative orchestration config
Define your entire orchestration policy in YAML. No scattered decorator logic. Version-controlled, diff-able, reviewable.
Enterprise audit trail
Immutable log of every routing decision, budget enforcement event, and HITL gate outcome. Compliance-ready export.
< 12ms routing latency
Control plane overhead measured in single-digit milliseconds. Routing decisions don't add perceptible latency to agent calls.
Self-hosted or cloud
Deploy OrchVynt inside your VPC for full data sovereignty. Cloud-hosted option for teams moving fast.
Drift detection
Detect when agent outputs drift from baseline. Automatic escalation to HITL gate when confidence falls below threshold.
From scattered imperative code to declarative control
Define your orchestration policy
Write a single YAML manifest declaring routing rules, fallback priorities, token budgets, and HITL triggers. One file. One place to change.
Deploy the control plane alongside your agents
OrchVynt runs as a sidecar or standalone service. Point your agents at the OrchVynt endpoint instead of directly at model APIs. Zero changes to agent code.
Observe and tune
Every invocation appears in the trace dashboard. Adjust routing weights, tighten budgets, or add HITL triggers without redeploying agent code. Config is the interface.
What engineering teams say after going to production
Ready to put your agent workflows in production?
Join the teams using OrchVynt to move multi-agent AI from prototype to production-grade infrastructure.