Platform
Enterprise AI stalls at the execution boundary. Models can analyze, recommend, and draft — but they can't execute work across your systems without governance, approval rights, audit trails, and human control. NEWWORK closes that gap.
The platform turns AI recommendations into governed, auditable work. Agents prepare tasks, validate against policy, route approvals, coordinate across systems, and maintain evidence. People approve, override, and stay in control. Every action logs. Every decision has a trail.
Work that required email coordination, manual handoffs, and status meetings now runs as traceable execution paths. Onboarding completes without chasing approvers. Service requests route across team boundaries with context intact. Expense approvals enforce policy before submission, not after audit. Revenue operations coordinate sales, legal, finance, and delivery without anyone asking “where are we on this deal?”
What makes execution governed
NEWWORK is the layer where execution happens and governance holds at the same time.
Enterprise Agents get specific roles, limited tool access, policy context, and approval gates. An onboarding agent can build a task plan and route it for review. It cannot provision access or modify employee records without human approval. Decision rights are explicit, not implied.
FLOWs coordinate agents, people, and systems through routed workflows. Every step logs. Every approval records who, when, and why. Every exception documents the override and the authority. Audit evidence generates automatically.
Agents check policy before routing work, not after execution. An expense outside policy gets flagged at submission. A discount exceeding threshold stops at the approval gate. A contract with non-standard terms routes to legal review before signature.
Low-risk work can execute autonomously. Medium-risk work requires human approval. High-risk work demands mandatory human decision. The autonomy model is configurable, auditable, and reversible.
Platform architecture
Each layer has a concrete job. Together, they turn business intent into governed execution.
Agents running across your enterprise systems without policy enforcement make tool calls that bypass approval workflows, execute outside defined authority, and leave no evidence trail when something breaks.
AI Fabric closes that gap. It governs which agents can access which tools, enforces approval gates before execution, grounds every action in policy and context, and captures a complete audit trail from request to completion.
An agent identifies a data discrepancy across connected systems.
Before it can execute a correction, AI Fabric checks the agent's authority against your governance policy, routes the proposed action to the appropriate approver, waits for confirmation, executes the tool call, and archives the full decision chain.
That agent makes the change immediately. You discover the problem during your next audit cycle.
Work that spans teams, systems, and approval boundaries stalls in email threads, status meetings, and manual handoffs. FLOW eliminates that coordination tax by routing tasks through governed execution paths where agents prepare work, people approve decisions, and systems update automatically with full audit trails.
An employee asks a policy question in the workplace chat.
An agent validates the answer, determines it requires a record update in the HRIS, checks that the change falls within policy, routes the proposed update to the employee's manager for approval, waits for confirmation, executes the system update, notifies the employee, and logs the complete decision chain from question to resolution.
That same question generates an email thread, three follow-ups, a manual system update by HR, and no record of who approved what or when.
FLOW converts recurring coordination work into reusable execution paths. Onboarding runs the same way every time. Service requests route to the right resolver without reassignment loops. Expense approvals enforce policy before submission, not during audit cleanup. Contract reviews route through legal, finance, and sales with context intact and no one asking where the deal stands.
Once a FLOW is built, it runs consistently. Exception handling is explicit. Audit evidence generates automatically. The coordination work that consumed management cycles becomes background infrastructure.
When AI agents act on enterprise systems, every action carries the same regulatory weight as a human one. Compliance officers, internal audit, and the board are going to ask the same questions they've always asked: who authorized this, what policy applied, what evidence supports it. Governance and Control gives you those answers by design — not by reconstruction after the fact.
It is the control model inside the execution layer, not a separate security story bolted on. Every agent, every approval, every system update is permissioned, routed, recorded, and reviewable.
An agent updates a customer record in a regulated market.
Identity is bound to the agent. Policy checks fire before the write. The approving authority is captured. The action and its evidence land in the audit log within seconds. When the regulator asks, the answer is one query away.
The change happens. The audit happens months later. The chain of custody gets reconstructed from logs scattered across systems — when it exists at all.
Boards and regulators are asking enterprises to prove governance for AI before it acts in production, not after. Governance and Control gives the answer that holds up to that scrutiny: every AI action is permissioned, routed, recorded, and reviewable. The evidence pack assembles itself.
Most “AI strategies” assume you're going to replace your systems of record. Most CIOs aren't going to. Connect is the alternative: a governed integration layer that gives agents and FLOWs controlled access to the platforms already in production — SAP, Workday, Salesforce, ServiceNow, Oracle, Microsoft 365 — and the documents, policies, and context that live alongside them.
AI gets the reach it needs to actually execute work. Systems of record stay where they are, with no migration timeline, no data duplication, and no platform-replacement project.
A sales agent prepares a renewal proposal that needs data from Salesforce, contract terms from the document repository, billing history from finance, and support history from ServiceNow.
The agent reaches each system through a scoped, audit-logged connector. It reads only what it's authorized to read, assembles the context, and produces the proposal — with the data lineage attached.
Someone copies and pastes across four tools. Half the relevant context never makes it in. The deal moves slower and the data trail dies.
The path to enterprise AI doesn't go through replacing your stack. It goes through making your stack usable to AI — under control. Connect is the layer that makes the existing landscape executable without forcing a big-bang replacement.
AI that acts without a place for people to see it is AI that operates in the dark. Every is the workplace surface where AI-executed work shows up — tasks, approvals, documents, agent activity, and exception decisions — in one control plane for employees, managers, and admins.
Approvals route here. Tasks queue here. Agents answer questions and launch FLOWs here. Override controls and audit visibility live here. People stay in the loop without leaving the systems they already work in.
An expense over the policy threshold lands on a manager's desk.
The agent's reasoning, the policy that was checked, the supporting receipts, and the recommended decision all surface in the same view. The manager approves, overrides, or escalates in two clicks — and the decision is logged.
The agent's recommendation arrives as an email. The policy doc is in a different system. The receipts are attached somewhere else. The manager guesses or asks a clarifying question that takes three days to come back.
Governance isn't only a back-office story. It's also “did the right person see this, in time, with enough context to make a good call?” Every is where that human accountability shows up in the work surface itself — not as a separate review step bolted on after the fact.
Every enterprise has work that doesn't fit a standard pack. A specialized approval chain. A regulated decision tree. A coordination flow that's specific to how this company runs. Create and AI Builder are how that work becomes governed execution on NEWWORK — apps, agents, FLOWs, screens, tools, and Value Pack extensions, all inheriting the same identity, policy, approval, and audit infrastructure.
Create gives technical teams the structured builder surface. AI Builder turns business intent into a proposed app structure, agent team, FLOW design, and governance scaffolding that technical teams review, adjust, and deploy.
A business owner describes a custom workflow: a regulated decision their team makes a hundred times a month with no consistent path.
AI Builder proposes the app surface, the agent responsibilities, the FLOW steps, the policy checks, and the audit trail. The technical team reviews, adjusts the boundaries, and deploys. The work runs under the same controls as everything else on the platform.
The team either lives with the inconsistency, files a custom-build request that gets queued, or stands up a parallel automation outside enterprise governance.
Custom work is the 10% nobody's pack covers. Create and AI Builder is how that 10% becomes governed execution on the same fabric as everything else — not a shadow-IT track that runs alongside the platform with weaker controls.
From idea to governed execution
A business stakeholder describes the work that should become executable: “New hires should have their equipment, access, and onboarding plan ready on day one.”
AI Builder proposes the work surface: manager intake form, HR review screen, employee onboarding dashboard, approval gates.
AI Builder designs the execution path: new hire signal → plan generation → manager approval → IT provisioning → equipment allocation → readiness confirmation.
Technical and compliance teams review the proposed agent boundaries, approval gates, policy checks, and audit trail. Adjustments are made. Controls are locked.
The FLOW deploys. Onboarding runs the same way every time. New edge cases extend the FLOW. The operating wedge becomes a reusable asset.
Buy the platform directly
The platform can be bought as the Enterprise AI Foundation — the full execution infrastructure without pre-built Value Packs. You build your own governed execution wedges using Create and AI Builder.
Or start with a pre-built entry point