Enterprise AI systems,designed like productsand governed like infrastructure.

Architecture schematic: two separate entry surfaces — agent chat and mesh chat — each with its own orchestration engine. The chat orchestrator handles versions, memory, and quota for single-agent turns; the mesh engine runs a LangGraph state machine with checkpoints and human-in-the-loop interrupts for multi-agent work. Both converge on a shared runtime of payload building, stream event handling, and the tool loop, then the streaming model runtime. Every tool call passes through a full-width governance gate — RBAC, tool policy, approvals, audit — before reaching MCP tools, retrieval packs, document stores, NL-to-SQL, connectors, and memory, all reporting into a telemetry rail.

Request-path schematic. Along the top, discovery: an MCP client reads authorization-server metadata and protected-resource metadata, then registers itself under RFC 7591 against the corporate identity provider; a dashed line returns to the client carrying the 401 that starts the handshake. The client then makes a proxied call into the per-call pipeline, the only element marked in red, where the caller is established, eligibility is settled, and upstream targets and limits are checked before anything is forwarded. Past it sits the proxy executor, which reaches internal MCP servers and REST APIs wrapped through OpenAPI. An audit record falls out of the path into admin metrics and team metrics, the latter aggregate only.

Flow schematic: a customer question in an entitlement-scoped session is rephrased and used to retrieve from knowledge-base articles and product documentation, producing a grounded answer that abstains when unsupported. A satisfaction checkpoint decides the outcome: satisfied ends the session with no case filed — the deflection path — while unsatisfied moves to a drafted case with validated fields, user edits, and case creation.