Enterprise AI infrastructure typically comprises five core components that work together as an integrated platform.

The context layer is where organizational intelligence lives. A live knowledge graph connects enterprise systems (CRM, cloud, DevOps, IT, HR, finance) and maps relationships between people, processes, tools, and data. Unlike basic data integrations, a context layer correlates ownership, dependencies, and business rules in real time. It understands not just what data exists, but how it relates and what it means. This is the hardest component to build from scratch and the one that creates the most defensible value.

The agent platform provides tools for building, testing, and deploying AI agents. This includes no-code builders for business teams, pro-code SDKs (TypeScript, Python) for engineering teams, and operational features like human-in-the-loop approval, background agents that run proactively on schedules or events, and an agent inbox for review and steering.

The AI gateway provides unified access to multiple LLM providers (OpenAI, Anthropic, Google, Cohere, local models, and more). Model-agnostic by design, it includes cost controls per agent and per team, routing by cost or capability, and the ability to switch providers without code changes. BYOK (Bring Your Own Key) support ensures enterprises keep control of their model access.

Memory gives agents persistent, private context that carries across interactions. Agents learn, remember context, and compound in value over time rather than starting from zero each session. Without memory, every interaction is a cold start. With it, agents become more effective with use.

Governance ties everything together with enterprise SSO, role-based access control at the agent level, complete audit trails, cost attribution per agent and per team, and policy enforcement. Critical for regulated industries. Built into the foundation, not added later.