Agent teams, industry modules and skills on one Enterprise Agentic OS.
Many enterprises face the same structural challenges, and approaches built on "More with More" struggle to solve them.
Systems 10-20+ years old that can't keep pace
Can't hire fast enough. Knowledge walks out daily
Large data assets, hard to turn into decisions
More spend, same outcomes
Six capabilities, designed to work with the data your enterprise already has.
Agents defined with the workflows and terminology of their industry module.
700+ specialized AI agents across 26 industry modules (2 runtime-wired today) — from C-suite strategy to frontline operations.
Pre-built workflows for sprint planning, financial reviews, sales enablement, customer onboarding, and more.
An enterprise knowledge engine designed to connect your data and extract patterns.
Designed to work with existing tools through open standards, connecting to systems of record rather than replacing them.
Designed to turn reviewed work the customer permits into reusable skills and knowledge. The learning loop is not switched on yet.
The architecture, described as design rather than as a record of any deployment: the products behind the strategic pillars first, then the building blocks they share. Where a product definition is still a draft, the entry says so.
The semantic layer: industry ontologies and a knowledge graph that relate business concepts, information and context. IQ 1.0, the discovery and grounding layer, is reported shipped; IQ 2.0, the governed-answer evolution, is a draft definition.
Work is modeled as a case with stages, steps and points where a person decides, built on a durable workflow engine. Industry playbooks organize agent teams around a process rather than a prompt.
The management view over agent work: worker health, work grouped by status, and a rail of active, recently finished and stuck work, built as components shared by the customer and admin portals. The Platform 3.1 definition behind it is still a draft.
Agent work is modeled as a run with one status lifecycle, checkpointed step by step so it can resume in a fresh process, and metered on the server. Execution engines are recorded in a registry, each entry carrying its own conformance-test and placement state.
Designed so a skill is a versioned, composable unit, defined per business function, with an append-only log of its invocations.
The harness is designed to score agent retrieval and answers against frozen, labeled question sets, and to write one append-only, signed result record per scored run.
Autonomy is modeled as levels from observe-only to autonomous, and a per-agent token budget carries warning thresholds and a pause level.
The permission model places each tool at one of four levels — automatic, ask once, ask every time, or never — resolved per tenant and per user against the tool's risk class.
A scoped container for a body of work, holding its own instructions, files, members and activity feed.
Graph ingestion is designed to write lineage records that link extracted knowledge to its source document and extraction run. A reader and writer for the Open Knowledge Format, an externally published specification, is built and not yet enabled.
Designed for single-tenant deployment, with isolation designed into the architecture rather than configured on.
Designed to record each skill's origin, version history and change record.
PII filtering, data minimization and deletion-request handling are designed into the pipeline.
Designed for role-based access control, scoped to what each role permits.
Models are listed in a versioned catalog that records each one's provider and status.
One prioritized workflow, scoped against your own process and written up as a decision brief.