Skip to content

Product Suite

Platform Runtime

Specialist capability. Shared platform. Agentic execution.

Platform Runtime runs as part of the Advanze Execution Platform, so the application is not isolated software. It shares the same data foundation, workflow engine, security model and agentic AI fabric as the rest of the enterprise suite. Teams get the specialist capability they need without creating another silo.

See it in action Talk to Advanze
Platform and governance team reviewing AI execution, controls and architecture.
Platform Runtime starts with people trying to make the right call. It starts with someone trying to move platform runtime work forward while knowing that the next action may affect customers, colleagues, money, delivery, compliance or downstream systems.

Product Suite / Platform Runtime

From application to execution capability

Advanze is positioned around the idea that enterprise software must evolve from passive systems of record into active systems of execution. In that model, this page is not just a feature description. It explains how Platform Runtime contributes to an operating environment where people define intent, agents execute governed work, and leadership can see progress through unified data.

The value is strongest when the capability is connected to adjacent processes. Records, workflows, controls, communications and analytics should not live in separate tools. They should participate in a shared execution fabric that can coordinate work across departments while preserving human accountability.

Platform and governance team reviewing AI execution, controls and architecture.
Where Platform Runtime becomes real work people can trust. Advanze gives that person a clearer path. Agents gather context, checks happen before action, approvals are routed when judgement matters and the outcome is recorded so the business can trust the work.

Core capabilities

What Platform Runtime enables

Each capability is designed to work as part of the broader execution platform rather than as a disconnected module.

Execution Engine

High-performance runtime that orchestrates workflows, agents, and business logic at scale.

Service Mesh

Connect all platform components through a resilient, load-balanced service architecture.

Security Layer

Enforce authentication, authorization, encryption, and audit logging across all services.

Performance Monitoring

Real-time metrics, tracing, and diagnostics for application performance and health.

Auto-Scaling

Dynamically adjust compute resources based on demand with intelligent workload distribution.

Extension Framework

Load custom plugins, integrations, and business logic without modifying core platform.

The Advanze Control Model - AI Agents Execute Work Inside the Control Model

Agentic operating model

AI agents execute work inside the control model

Advanze treats AI agents as participants in the operating model. Agents can read context, call services, update records, trigger workflows, prepare decisions and escalate exceptions. Human teams remain responsible for judgement, governance and business accountability.

That distinction matters. The goal is not to add another chatbot to existing systems. The goal is to create an execution platform where work can move across functions with consistent permissions, policies, audit trails and data visibility.

Business outcomes

What becomes possible

Fewer silosShared data and workflows reduce duplicated effort.
Faster executionAgents can progress repeatable work across rules, approvals and systems.
Better visibilityLeaders see work, risk and performance in one operating context.
Lower platform sprawlSpecialised capability without another disconnected vendor.
People working through Platform Runtime execution with clarity and confidence.
The outcome is not just automation. It is confidence in what happens next. Advanze gives that person a clearer path. Agents gather context, checks happen before action, approvals are routed when judgement matters and the outcome is recorded so the business can trust the work.

Agentic use case

Where Platform Runtime becomes governed execution.

It starts with someone trying to move platform runtime work forward while knowing that the next action may affect customers, colleagues, money, delivery, compliance or downstream systems.

What makes it harder in the real world: Platform Runtime work can cross teams, systems, customer impact, financial thresholds, policy checks, security boundaries and audit requirements. Without a governed path, people carry too much of that risk manually.

What Advanze changes: Platform Runtime becomes a governed execution path when work is classified, enriched with context, checked by specialist agents, routed through approvals and recorded with evidence.

Platform Runtime Intake AgentClassifies platform runtime work by intent, urgency, risk and required execution path.
Platform Runtime Context AgentGathers relevant records, policies, documents, messages, metrics and prior decisions.
Platform Runtime Processing AgentPrepares the recommended next action, update, workflow step or response.
Guardrail AgentChecks permissions, thresholds, policy exceptions, sensitive data and approval requirements.
Workflow Orchestration AgentRoutes approvals, coordinates handoffs, records evidence and updates systems after approval.
Challenge

Platform Runtime looks like one product area, but real execution depends on surrounding context, approvals, exceptions and system updates.

Orchestration

Advanze treats Platform Runtime as part of the execution fabric: agents classify work, gather evidence, route guardrails and coordinate workflow before action is completed.

Success

Platform Runtime becomes a controlled path from intent to outcome, with people directing judgement and the platform carrying context, evidence and accountability.

Related execution patterns

Complex products usually touch more than one governed workflow.

AI Governance, Finance, Platform Operations

AI Runtime Control
Control AI execution by routing tasks to the right model, setting run budgets, applying permissions, stopping loops and reporting usage by use case.

Explore pattern
DataServices, Data Governance, Operations

Data Quality Exception and Point-in-Time Recovery
Detect bad data, classify impact, identify the source, route approval and restore or correct records with auditable point-in-time recovery.

Explore pattern
GRC, Security, Operations

Incident Response with Governed Escalation
Coordinate incident intake, severity classification, evidence collection, stakeholder notification, escalation and remediation tasks through governed agentic workflow.

Explore pattern
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 24Platform, architecture and governance teams controlling agentic execution, cost and operational risk.
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 25Platform, architecture and governance teams controlling agentic execution, cost and operational risk.
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 26Platform, architecture and governance teams controlling agentic execution, cost and operational risk.

Why AI execution needs architecture

The work needs context, controls and clear permissions before automation can safely act.

Platform Runtime is strongest when it runs inside the Advanze control model: identity, permissions, data, workflow, policy, audit evidence and human judgement move together.

  • The right context has to be assembled before work can safely move.
  • Different actions require different permissions, approvals and audit evidence.
  • Exceptions must be routed to the correct specialist path instead of handled informally.
  • System updates need to be connected to workflow state, policy and human approval boundaries.
View related pricing Explore Multi-Agent Platform

Implementation path

How to move from concept to production

Advanze can be introduced progressively. The recommended path is to start with a visible workflow, prove the operating model, then expand into adjacent capabilities as the platform foundation matures.

  1. 1
    Assess the current operating model

    Map the workflows, systems, data sources and manual coordination points around this capability.

  2. 2
    Design the target execution flow

    Define the data model, human approvals, agent tasks, service calls and governance controls.

  3. 3
    Launch a focused implementation wave

    Start with a bounded use case that proves the operating pattern and creates reusable platform assets.

  4. 4
    Scale across the business

    Extend the pattern to adjacent workflows, more agents, more users and deeper integrations.

Next step

Build this into your execution platform roadmap

Explore how Platform Runtime can be implemented as part of a broader Advanze platform adoption programme, from first pilot to enterprise scale.

Pricing All Products Platform Request demo