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Commercial Model

Agent Orchestration

A simpler way to plan adoption, consolidation and scale.

Agent Orchestration provides a commercial view of the Advanze platform. Pricing is designed to support consolidation, predictable adoption and a clearer relationship between platform investment and operating outcomes.

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Platform and governance team reviewing AI execution, controls and architecture.
Agent Orchestration starts with people trying to make the right call. It starts with a person trying to get agent orchestration work done without losing context. They need the next action to be clear, but they also need confidence that the platform has checked the risk, evidence, permissions and downstream impact.

Commercial Model / Agent Orchestration

Commercial clarity for platform adoption

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 Agent Orchestration 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 Agent Orchestration becomes real work people can trust. The person is no longer carrying the full burden alone. Agents assemble context, route specialist checks, pause when judgement is required and record what happened so Agent Orchestration work feels calmer, faster and accountable.

Core capabilities

What Agent Orchestration enables

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

Agent Design

Use agent design as part of one operating model, with shared data, governed workflows, auditable actions and AI agents that can execute work while people stay in control.

Task Delegation

Use task delegation as part of one operating model, with shared data, governed workflows, auditable actions and AI agents that can execute work while people stay in control.

Orchestration

Use orchestration as part of one operating model, with shared data, governed workflows, auditable actions and AI agents that can execute work while people stay in control.

Governance

Use governance as part of one operating model, with shared data, governed workflows, auditable actions and AI agents that can execute work while people stay in control.

Monitoring

Use monitoring as part of one operating model, with shared data, governed workflows, auditable actions and AI agents that can execute work while people stay in control.

Human Approval Points

Use human approval points as part of one operating model, with shared data, governed workflows, auditable actions and AI agents that can execute work while people stay in control.

The Advanze Stack - Business Services, Platform Services, Technology Foundation

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

PredictabilityPlan cost by suite, service and adoption pathway.
Consolidation economicsEvaluate savings from retiring overlapping tools.
Value alignmentConnect spend to execution outcomes.
Scale readinessSupport growth across users, agents, records and APIs.
People working through Agent Orchestration execution with clarity and confidence.
The outcome is not just automation. It is confidence in what happens next. The person is no longer carrying the full burden alone. Agents assemble context, route specialist checks, pause when judgement is required and record what happened so Agent Orchestration work feels calmer, faster and accountable.

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
    Scope the suite

    Identify which applications, services and integrations should be included in the first adoption wave.

  2. 2
    Model value

    Estimate consolidation savings, productivity lift and execution capacity using conservative assumptions.

  3. 3
    Choose the path

    Start with a focused pilot or move directly into a platform foundation programme.

  4. 4
    Scale deliberately

    Expand suites, agents and integrations with clear governance and measurement.

Why this pricing path matters

Agent Orchestration pricing should be understood through the work it helps govern.

It starts with a person trying to get agent orchestration work done without losing context. They need the next action to be clear, but they also need confidence that the platform has checked the risk, evidence, permissions and downstream impact.

What makes it harder in the real world: Agent orchestration work looks straightforward until it crosses people, systems, policies, approvals and customer impact. In practice, the work may require the right customer or employee context, policy checks, data quality, approvals, exception routing, integration updates and a clear audit trail.

What Advanze changes: Agent Orchestration turns agent orchestration activity into governed execution by connecting agent, prompt, tool, policy, approval and cost context to agents, workflow, permissions, approvals and audit evidence before work is completed.

Agent Orchestration Intake AgentClassifies new agent orchestration work, identifies intent, urgency, context requirements and the likely execution path.
Agent Orchestration Context AgentGathers related records, history, documents, messages, policies, metrics and system state needed for agent orchestration decisions.
Agent Orchestration Processing AgentPrepares the recommended action, draft update, workflow step or system change for agent orchestration work.
Guardrail AgentChecks permissions, policy thresholds, sensitive data, financial exposure, compliance implications and approval requirements.
Workflow Orchestration AgentRoutes reviews, manages approvals, records evidence and coordinates safe system updates after approval.
Challenge

Agent Orchestration starts as a single app experience, but the real work usually depends on context from other teams, policies, data and systems.

Orchestration

Advanze treats Agent Orchestration as part of a governed execution fabric. The app captures the work, agents gather context, workflow routes approvals and the control model determines what can safely happen next.

Success

Agent Orchestration becomes more than a screen. It becomes a reliable path from intent to controlled action, with people still responsible for judgement and the platform carrying evidence.

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.

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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 2Platform, architecture and governance teams controlling agentic execution, cost and operational risk.
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 3Platform, architecture and governance teams controlling agentic execution, cost and operational risk.
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 4Platform, architecture and governance teams controlling agentic execution, cost and operational risk.

Control model

Useful AI execution needs pricing, permissions and governance to move together.

Agent Orchestration is valuable when it participates in the Advanze control model: identity, permissions, workflow, policy checks, data context, audit evidence and human approval boundaries sit inside the execution path.

  • Agent Orchestration work needs the right agent, prompt, tool, policy, approval and cost context before an agent or user can act with confidence.
  • The process often crosses handoffs, approvals, exception paths, SLAs and downstream system updates.
  • Different actions need different permission levels: read, draft, update, approve, send, pay, create, close or escalate.
  • The business needs evidence of what was requested, what was checked, who approved, what changed and why.
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Next step

Build this into your execution platform roadmap

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

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