AI pilots are easy to launch. Production raises the stakes. Once agents and models act on live business processes, they touch real customers, sensitive data, and financial transactions, and they have to meet the same budget, security, and compliance requirements as every other enterprise system.
Amplify Platform is an AI and API platform for regulated industries. It brings together four capabilities: AI Control Plane, Integration Fabric, API Management, and Engagement Portal. Integration Fabric connects applications, APIs, data, and events across cloud and on-premises environments. API Management discovers, secures, and governs APIs across distributed environments, and Engagement Portal gives people and agents one governed catalog for finding and using the services the enterprise publishes.
Amplify Platform’s AI Control Plane turns AI experimentation into production-ready business outcomes by governing AI agents, large language models (LLMs), and Model Context Protocol (MCP) servers from one control plane. It extends that governance to the systems and services organizations already run, with centralized policy enforcement and observability across models, agents, and providers.
One platform keeps the AI ecosystem together. AI Control Plane shares a backbone with the rest of Amplify Platform, including API Management and Integration Fabric, so AI governance sits alongside the APIs and integrations the business already manages. The platform covers what production AI requires, without forcing teams to give up tools they prefer elsewhere. AI brings opportunities for business agility and growth. But it carries real risks. With governance built into AI Control Plane, business leaders can weigh the reward of each AI initiative against its risk and scale the ones where the reward comes out ahead, or risks are either manageable or minimal.
Three concerns matter most once AI reaches production: sovereignty over where it runs and which data it sees, control over what it costs, and visibility into what it does. AI Control Plane addresses each one and builds on a proven foundation for connecting critical systems, protecting sensitive information, and governing digital interactions.
Start with the APIs and services you already run
The fastest path to production AI is to build on enterprise services that already work. The APIs exposing core systems already carry security policies, access rules, and lifecycle governance, giving teams a governed foundation for AI adoption.
The business logic behind a service does not need to change; only the access pattern does. An application sends a structured API request and expects a defined response, while an AI agent works through natural language and calls MCP servers, their tools, and other agents.
AI Control Plane turns those existing APIs and services into MCP tools while preserving their established governance. Teams can extend trusted capabilities to agents and concentrate new development on the services AI uniquely requires.
A shared engine keeps application and agent interactions consistent. When an agent calls a tool through AI Control Plane, the call runs as an integration on the engine that powers Integration Fabric. Teams can reuse visual design tools, data transformations, and controls across both paths, avoiding separate AI tools and the work of maintaining connections among them.
Govern AI agents, models, and MCP servers
Governance starts with knowing what exists. AI Control Plane discovers and registers the agents, models, and MCP servers in use across the enterprise, including the ones IT never provisioned. LLM and MCP traffic passes through one control plane under consistent policies, in place of a separate governance layer for each provider or team.
Reaching a service does not grant permission to use it. Each agent should call only the APIs, tools, and other agents the business has approved for it, and AI Control Plane, together with Engagement Portal, defines those rules and enforces them at runtime. Multi-agent workflows follow approved paths. Small inconsistencies compound as one agent hands work to the next, so the control plane validates each step to keep outcomes consistent at every handoff.
Keep AI inside your sovereign boundary
Sovereignty has two dimensions. The first is deployment sovereignty: control over where AI governance runs. AI Control Plane supports on-premises, hybrid, sovereign cloud, and air-gapped deployments, and organizations can combine those options as requirements evolve. This flexibility keeps AI traffic and governance within the locations and jurisdictions the business requires, rather than leaving those decisions to a SaaS provider.
The second is data sovereignty: keeping private data private even when parts of a workflow reach external models or systems. Policies in the live data path govern access, inspect prompts, and determine which information may leave the organization. AI Control Plane can mask or redact sensitive fields before forwarding a request, so external services receive only the data they are permitted to process. Guardrails can also block content that violates the organization’s policies, including malicious, hurtful, or hateful material.
Know what AI costs and what it does
Cost control begins with a ceiling: AI Control Plane can enforce a hard spending limit for each model provider, which caps the organization’s exposure regardless of who uses the service. But a ceiling alone can halt valuable work. When a limit runs out mid-month, every application behind it stops, including the ones that generate revenue.
Better budget decisions depend on knowing who is spending and on what. AI Control Plane knows which person, agent, or application made each request, so it can attribute spend to the owners the business defines, such as an individual, an application, or a line of business. You analyze consumption by team, model, and use case, route each workload to the model that fits its cost and performance needs, and then judge which spending delivers value. The same visibility carries into Engagement Portal, where teams manage how their published services are consumed.
Visibility covers behavior as well as spend. AI Control Plane tracks usage and activity across models and agents, monitors compliance and operational health, and records the authorization, execution, policy, and outcome of each action. When an agent behaves unexpectedly, teams have the record they need to explain what happened and act on it.
What AI Control Plane delivers
Organizations using AI Control Plane put AI solutions into production with confidence, keep spending proportionate to value, and prove how their agents and models behave.
AI reaches production with the guardrails, resilience, and oversight that live business processes demand. Agents and models work with real customers, data, and transactions under the same governance as other enterprise services, built on APIs the business already trusts.
The AI estate stays visible. Registered agents, models, and MCP servers each have an owner and a policy, which leaves far less room for shadow AI to spread through teams and tools. Spending tracks with value. Costs are visible and attributed to owners, and a vendor-neutral control plane lets teams adopt new models and providers as the market moves, without depending on any single one.
Compliance becomes a by-product of daily operations. The audit trail builds automatically at runtime, recording who invoked what and with what result. When regulators and auditors ask for evidence, teams no longer have to reconstruct it after the fact.
AI Control Plane meets organizations where they are. You can begin with the APIs and services already in production, extend the governance you trust to AI, and add agents, models, and providers as adoption grows.
Explore AI Control Plane: One control plane governs AI agents, models, and MCP servers for regulated organizations.