ADAM AI Gateway: Govern Models, MCP, and Agents | Brillio

ADAM AI Gateway

One governed front door for every model, tool, and agent: access, orchestration, safety, and cost on one plane.

Untangle how your
teams reach AI

Many enterprises reach AI through direct model calls, duplicated tool integrations, and siloed agents, with no shared policy, cost visibility, or observability. We replace those point-to-point connections with one governed path. The AI Gateway sits between every app, copilot, agent, and employee and the models, tools, and systems they use, enforcing policy at runtime for every tenant.

Runtime gateways and the controls they share

One model-agnostic endpoint for every cloud and hosted model, with routing, versioning, quotas, and cost controls.

Tool abstraction and governed sharing: a central MCP registry, role-based access and approved-tool enforcement on every call.

Registry, composition and evaluation for agent-to-agent and agent-to-tool traffic, with agent passports and identity governance.

Guardrails, PII redaction, content filtering, and policy checks applied consistently to every request and response.

Evals, scorecards, and quality gates run continuously to keep agents accurate, safe, and production-ready.

Token limits, rate limits, quotas, and cost enforcement by tenant, team, use case or agent, with unified telemetry streamed to the control tower.

What the gateway
enforces at runtime

One secure front door
Every AI request enters through a single governed entry point.
Configured, not coded
Every capability is a toggle on a shared control plane, so you turn on only what you need.
Runtime enforcement
Guardrails, PII controls, evaluations and grounding run inside the request path, not after the fact.

Outcomes that compound

Consolidated

access through one governed path

Portable

AI across models and clouds, with no lock-in

Predictable

costs with quotas by tenant, team, and agent

Auditable

requests, streamed to the Control Tower

FAQs: What platform teams ask first

An API gateway governs HTTP. It does not understand a prompt, a token, a model route, a tool call, or an agent trajectory. The AI Gateway governs those AI-runtime decisions: model access, token budgets, prompt controls, evaluations, and cost attribution.

LiteLLM is strong for model proxy, routing, budgets, and fallback. The AI Gateway governs models, MCP tools, agents, prompts, tenants, safety, and quality from one plane: the AI-specific layer above your model proxy.

No. It is model-agnostic and cloud-aware by design, with swappable open-source components behind stable published APIs, so you adopt best-fit models and switch freely.

No. Capabilities are modular and opt-in. Most enterprises start with two, usually model routing and guardrails, and enable the rest as configuration.

See how Strategy Toolkit fits your enterprise.