The operating platform for enterprise Agentic AI
Agentic AI built to
operate at enterprise scale.
PIE™ governs how AI agents reason, decide, call tools, collaborate, and act across enterprise systems—with controls, approvals, and evidence embedded at runtime.
Operational Agentic AI, governed by PIE.Governed · Risk-Tiered · Explainable · Auditable · Traceable
PIE operating model
01Why PIE
Enterprise AI earns trust when the system that acts can be understood, controlled, and evolved.
PIE makes that operational.
Every agent, decision, human approval, tool call, and system action executes inside the same governed product architecture—creating the evidence needed to explain, trace, and audit the outcome.
02Govern the enterprise system
The enterprise does not merely need to govern a model.
It needs to govern the full system in which models, agents, humans, decisions, tools, data, and enterprise applications act together.
01Business intentWhat the business needs to happen
PIEGoverned processPolicies, permissions, controls, lineage
Models
Agents
Humans
Decisions
Tools
Data
Enterprise apps
Business outcome + execution evidence
03The architecture gap
Enterprise AI is caught between two failure modes.
Traditional enterprise operations
Manual workflows, disconnected systems, tribal knowledge, and slow change create fragmented execution.
Ungoverned agentic AI
Isolated pilots and agents can leave tool access, decisions, and evidence fragmented across the enterprise.
PIE
One governed execution system brings deterministic, agentic, and human work together with controlled capabilities and evidence.
04The PIE lifecycle
From business intent to defensible enterprise action.
PIE moves through a disciplined lifecycle: describe the intent, design the system, govern the conditions, execute the work, and prove what happened. Prove retains the decisions, approvals, and evidence behind every run—so an outcome can be reconstructed and defended after the fact.
Explore the platform lifecycle05Evidence follows execution
Every consequential action needs an account of what happened.
PIE is designed to preserve the decisions, approvals, and execution context needed to explain and defend an outcome after the fact.
Explore the control plane
06The GREAT standard
Enterprise AI should be GREAT.
Governance should not merely describe how AI ought to behave. The system should be able to demonstrate that it did.
Governed
Every agent, process, capability, and action operates within defined controls.
Risk-Tiered
Risk determines the level of controls, validation, and approval required.
Explainable
Context is preserved around what happened, which rules applied, and how an outcome was reached.
Auditable
The conceptual architecture is designed for execution, decision, capability, approval, and policy evidence.
Traceable
Lineage connects intent to process, process to capability, action, and outcome.
Governance isn’t something PIE adds to AI.
It’s how AI runs on PIE.
07Inside the platform
One platform connects the work of agents, rules, people, tools, and systems.
PIE keeps the design environment, controlled deployment, execution runtime, capability providers, and governance control plane connected—but the technical model belongs in the Architecture view.
Explore the full architecturePIE architectureDesign → deploy → execute → prove
Five connected layers. One governed operating model.
08Illustrative conceptual workflow
Trade-finance invoice reconciliation
Not every decision needs an agent.
PIE is designed for heterogeneous execution: agentic reasoning where interpretation matters, deterministic logic where rules must execute predictably, and authorised human judgment where accountability demands it.
01
System
Receive documents
Invoices and supporting trade documents enter through an approved channel.02
Agent
Extract and interpret
An approved agent extracts relevant fields and context from the submitted documents.03
Connector
Retrieve records
A governed connector obtains authorised lending, transaction, purchase-order, or ledger data.04
Decision
Reconcile deterministic fields
Explicit rules compare amounts, currencies, dates, counterparties, identifiers, and tolerances.05
Agent
Investigate ambiguity
An agent assembles an evidence-backed explanation for non-deterministic discrepancies.Agentic + deterministic + human
See the conceptual walkthrough Evaluate the platform
See how PIE governs Agentic AI from
capability to enterprise outcome.
Explore the product architecture, control model, and execution evidence that place intelligent action on a disciplined enterprise foundation.
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