How AI agents participate in a value stream

Connect bounded agent goals, human and system handoffs, and observable outcomes across the real flow of work.

Connect AI agents to end-to-end business value. Learn how AIDIVE examines outcomes, workflow handoffs, human accountability and delegated work together.

Trace the work people actually perform

A value stream connects work to an end-to-end outcome. When AI enters that flow, begin with representative cases: the trigger, information, decisions, handoffs, actions and resulting consequences. Include informal coordination, exceptions and the hidden work that keeps the service running.

AIDIVE's Integration test is practical: an AI output reaches an owned decision or action, and its outcome can be observed. Improved task performance alone does not show whether the wider operating chain has changed.

Make room for two operating forms

Some AI-enabled work follows a sequence designed in advance. Other work begins with a bounded goal and permitted actions, while an agent composes its path at runtime. Both can exist in the same value stream. Assess a predefined flow against its intended route; assess goal-directed work through its approved boundary and the trajectory it actually produced.

For either form, preserve case identity, relevant context, decision ownership and authority across human, agent and system handoffs. Observe tool calls, actions, exceptions, interventions and results. A service restart does not by itself reconcile work left partially complete.

Illustrative example: resolving an order delay

A procurement agent investigates a delayed order by checking shipment events, stock and approved alternatives. Its next step depends on what it finds. The organisation defines the goal and allowed actions beforehand, including which commitments require a person and when the agent must stop or escalate.

If another agent or a colleague takes over, the case carries prior decisions and current state. If a dependency fails, the team identifies incomplete or duplicated actions and reconciles them before normal operation resumes. Outcome evidence then returns to the owners who can improve the flow.

• Map the real decision-to-action chain.
• Define authority, context transfer and failure behaviour.
• Use observed outcomes and exceptions to govern the next change.

Take this into your next conversation.

Agentic integration becomes assessable when the goal, authority, actual trajectory and resulting outcome remain connected.

Framework source

Editorial adaptation from the supplied AIDIVE Version 1 master documents.

• AIDIVE Integration Dimension Version-1.docx · I – Integration; I.1 Enterprise Work, Decisions and AI Participation; I.2.6 Agent-to-Agent Delegation and Multi-Agent Handoff; I.3 Context Continuity Across the Flow; I.4.3 Recovery, Reconciliation and Rollback; I.6 Decision-to-Execution and Closed-Loop Learning
• AIDIVE Integration - Practices & Playbook Version-1.docx · AR-I02 Bounded Goal and Trajectory Review; AR-I09 Context Flow and Minimum Context Package; AR-I15 Closed-Loop Decision-to-Execution Review

Go a little deeper.

Put AI transformation
into practice.

Build capability through training, contribute your expertise or explore a connected workspace for applying the holistic AIDIVE framework.

Build the capability.

Develop the skills to connect AI transformation strategy, business outcomes, people and governance to your own decisions and responsibilities.

Grow the practice.

Bring your learning, delivery or research expertise to the holistic AIDIVE approach to enterprise AI transformation.

Bring AIDIVE into your work.

See how the proposed workspace could connect AI transformation learning, maturity evidence and practical artifacts.