Fragmented model integration
Every provider has its own endpoint, auth scheme and request shape. Each application ends up carrying its own integration code, and changing a model means changing all of them.
AI INFERENCE INFRASTRUCTURE
Infrastructure for AI that needs to run reliably.
AI Agent Inference is one layer between your applications, your agents and the models that power them, so your team can manage inference requests and understand what happens at runtime.
Built for teams integrating AI models into applications, workflows and agent-based systems.
THE CHALLENGE
AI applications need more than a model endpoint.
Integrating and operating inference brings its own problems. Not every deployment has all of them, but they tend to arrive together as usage grows.
Limited runtime visibility
When a request is slow or fails, teams need to see the outcome, the latency and the usage to investigate. Without a shared layer, that telemetry is scattered or missing.
Reliability under real load
Timeouts, an unavailable provider, an invalid response, a transient error. Any of these can break an AI workflow that assumed the model would always answer.
Operational complexity that grows
As usage climbs, so does the need for clear configuration, request monitoring, usage visibility and controls that a single hard-coded API key never gave you.
HOW IT WORKS
A clear path from request to response.
The gateway sits in the request path on purpose. That is what lets it route, retry and record, and it is the core difference from a governance layer that stays to one side.
Application or agent
sends an inference request
Inference gateway
authenticates and validates
Routing
to the configured model
Model endpoint
hosted or self-hosted
Response + telemetry
returned and recorded
The application sends a request; the gateway authenticates and validates it, routes it to the configured model, handles the response or error, and records usage, latency and outcome. Request and response content is not retained.
CORE CAPABILITIES
What the platform handles for you.
Model Connectivity
Connect your applications to the inference endpoints you use through one interface, and manage model and provider configuration in one place instead of in every service.
Inference Request Handling
Accept requests through a documented API, validate them, handle responses, and return useful errors when a request fails rather than an opaque one.
Provider & Model Configuration
Configure the set of providers and models your team relies on, and change what an application targets without rewriting the application.
Runtime Observability
See request outcomes, latency and errors where instrumentation is available, so production behaviour is something you can investigate rather than guess at.
Reliability & Error Handling
Configure timeouts and handle provider errors, with retry or fallback strategies where they are implemented, so one provider incident is not automatically your outage.
Usage Visibility
Track request volume and token usage where the provider reports it, so consumption is attributable per application, per model and per agent.
OPERATIONAL VISIBILITYIllustrative
See what happens after your request goes out.
An example of the kind of view the platform provides. The figures below are sample data for illustration, not measurements from a production system.
48,210
Requests today
99.2%
Success rate
840 ms
p95 latency
12.4M
Tokens today
Traffic by model
gpt-4o-mini
46%
Healthy
claude-sonnet
33%
Healthy
llama-3-70b (self-hosted)
21%
Degraded
DEVELOPER EXPERIENCE
Integrate inference into your existing applications.
One documented interface, one auth model, one place to change what a route points at. The example below shows the shape of an integration; the exact schema is confirmed against your setup.
example requestIllustrative
# Illustrative request. Not a live endpoint or a production schema.
curl https://inference.your-org.example/v1/generate \
-H "Authorization: Bearer $INFERENCE_KEY" \
-H "Content-Type: application/json" \
-d '{
"route": "assistant-default", # a model target you configure
"input": "Summarise this ticket thread.",
"max_tokens": 512,
"timeout_ms": 8000,
"fallback": "assistant-backup" # used if the primary route fails
}'
# Response (shape shown for illustration)
{
"output": "...",
"model": "gpt-4o-mini",
"usage": { "input_tokens": 734, "output_tokens": 128 },
"latency_ms": 612,
"route": "assistant-default"
}This is a conceptual example to show the integration model, not a live endpoint or a production API contract. We share the real interface and documentation during a demo.
RELIABILITY AND OBSERVABILITY
Understand and withstand what production throws at you.
The telemetry a shared layer collects is what turns an incident from a guess into an investigation, and the error handling is what keeps one provider's bad day from becoming yours.
Investigate failures
See failed requests with the outcome and the model involved, so a spike has an explanation rather than a shrug.
Watch latency
Track latency where instrumentation allows, so a slow model shows up as data instead of a support ticket.
Handle provider errors
Timeouts and provider failures are handled, with retry or fallback where implemented for your routes.
Attribute usage
Request volume and token usage, where the provider reports it, tied to the application and route that spent it.
We describe automatic failover, retries and cost controls only for the routes where they are configured and implemented, rather than as blanket guarantees.
USE CASES
Where the inference layer earns its place.
LLM-powered applications
A consistent model layer behind a product feature, so the application code does not carry provider-specific integration.
AI assistants and agent platforms
A shared path for the many inference calls an agent makes, with visibility into which calls cost what.
Apps using several model endpoints
One integration point in front of multiple providers, with routing decided by configuration rather than branching code.
Internal enterprise AI services
A managed inference layer other teams build against, with usage and errors visible centrally.
Teams that need inference observability
Latency, failures and usage in one place, so an incident review has data to work from.
Products consolidating model logic
Move scattered, duplicated model-integration code into one layer everyone shares.
FAQ
Questions engineering teams ask.
What is AI Agent Inference?
An infrastructure layer between your applications or agents and the models they use. Your code calls one endpoint; routing, request handling, error handling and telemetry sit behind it, across the providers and models you configure.
How does it differ from Bulwark?
They solve different problems and sit at different points. Bulwark governs what an agent is allowed to do and records the decision, deliberately out of the execution path. AI Agent Inference is in the request path by design: it carries the inference call to the model and returns the response. One asks "should this action happen?"; the other asks "how does this request reach a model reliably?" Many teams want both, at different layers.
Which model providers are supported?
It is built to work with the providers and self-hosted models you configure rather than a fixed list. Which specific providers fit your setup is something we confirm on a call, so we describe it as configurable rather than publishing a compatibility claim we would have to keep true.
Can it work with self-hosted models?
The design targets both hosted provider endpoints and self-hosted models you run yourself. We will walk through your specific deployment and what it takes to connect it.
How are failed requests handled?
Requests are validated before they are sent, and errors are returned in a usable form rather than swallowed. Timeouts and provider failures are handled, with retry or fallback strategies where they are implemented for your configuration.
Can we monitor latency and usage?
Yes, where the instrumentation and the provider’s reporting allow it. Request outcomes, latency and token usage can be surfaced so you can investigate behaviour and attribute consumption.
What do you retain?
Operational metadata about requests, not their content. The layer is built so request and response bodies are not retained.
How do we get started?
AI Agent Inference is sales-led. Book a demo and we will look at your inference architecture, the providers you use and what integration would involve.
Build your AI applications on infrastructure you can understand.
Talk to our team about your inference architecture, integration requirements and operational needs.