How to Get Scope Right in Agentic AI Products

According to McKinsey, agentic AI represents a value potential of $2.6 to $4.4 trillion a year across generative AI use cases. Yet the same McKinsey research finds that only about one percent of organizations consider their AI adoption "mature." Experimentation is accelerating fast; scope and authority discipline aren't keeping pace.

The gap lives in execution. Early AI systems mainly produced predictions — a human made the decision and took the action. Today's agentic systems carry out tasks directly inside business processes, with progressively less human approval in the loop. That splits the "scope" question for agentic AI products in two: what work will the agent do, and what authority and access will it be given?

Scope Isn't Just a "Which Feature" Question

In traditional software products, the scope conversation usually stays limited to a feature list: which screens, which functions ship in this release? For agentic AI products that question alone isn't enough, because the agent doesn't just produce an output — it performs actions directly inside systems: calling an API, updating a record, initiating a transaction.

That's why scope in an agentic product needs to be defined along two axes: what the agent can do (feature scope) and what tools, data and systems it can reach (authority scope). If authority scope isn't defined clearly up front, controls usually end up being built reactively afterward — often only once something has already gone wrong.

The Risks of Leaving Scope Undefined

Agents don't just generate output — they take action inside live systems. As authority grows, so does potential impact. Common risks when scope and authority boundaries aren't defined clearly from the start:

• Loss of execution control. Once given a goal, an agent can carry a multi-step workflow forward on its own; without a clear scope boundary, actions can extend beyond what was intended.
• Unauthorized tool/API calls. When an agent decides at runtime which tool to use, misconfigured integrations can open access paths that were never planned.
• Privilege expansion. An agent may run under a service identity with authority disproportionate to its task; in multi-agent environments, one agent's access can affect another's scope.
• Accountability ambiguity. If responsibility isn't clearly defined among the model provider, the platform operator and the organization, it's unclear who decides when something goes wrong.
• Drift over time. As inputs, tools and business processes change, an agent's behavior can quietly drift away from the boundaries it was originally designed within.

A Practical Framework for Getting Scope Right

1. Define scope and authority up front. The agent's purpose and boundaries should be written down: what work it will do, what data and tools it will access, and which actions are explicitly out of scope.

2. Map identity and access boundaries. The agent operates under a service identity. Apply least privilege: grant only the access its task requires, and review delegated authority periodically.

3. Run an impact assessment before deployment. As an agent's autonomy level and system access grow, so should the depth of assessment and the required approval process.

4. Build runtime controls. Mechanisms that constrain tool calls, validate inputs/outputs against policy, and escalate to a human past a threshold need to stay active not just during development, but while the agent is running.

5. Add logging and traceability. Every action and every tool call should be recorded — needed for both audit and continuous improvement.

6. Clarify human approval thresholds and an intervention plan. Which decisions can proceed autonomously, and which require direct approval? Who can halt execution in case of unexpected behavior should be defined in advance.

7. Set up continuous review and drift tracking. Defining scope isn't a one-time decision — authority should be reviewed periodically as the agent matures.

Seeing Scope in a Real Scenario

Picture a procurement agent operating inside a company's ERP system. Its job is simple: review routine purchase requests for approved suppliers within defined budget limits and generate purchase orders.

Authority is delegated, but bounded. The agent can't change budget rules or add a new supplier — it operates only within the scope it's been assigned. That boundary is also enforced technically through a restricted-privilege service identity: the agent can access purchase records and create purchase orders, but it can't alter financial controls or the approval hierarchy.

Every action is logged; when a purchase order is created, the transaction is traceable down to the agent's identity and timestamp. Transactions above a defined amount are automatically routed to human approval; anomalies go to escalation instead of automatic execution.

Over time, spending patterns are monitored and supplier selections are reviewed; if behavior starts to deviate from what's expected, authority can be narrowed or execution halted. The agent does the work, the human defines the boundary — scope stays bounded at every step.

In Short

• Scope in agentic AI is two-dimensional: what work gets done, and what authority is granted
• Per McKinsey, the potential is large ($2.6–4.4 trillion a year) but maturity is low (only about one percent of organizations)
• Undefined scope grows the risk of lost control, unauthorized access, accountability ambiguity and drift over time
• Least privilege, runtime controls and logging make scope concrete
• Defining scope isn't one-time — it's a discipline that needs continuous review as the agent matures

Source: This article draws on the data, risk categories and implementation framework from Palo Alto Networks' guide, "A Complete Guide to Agentic AI Governance".

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