Expanding on McKinsey's Agentic AI Vision: The Critical Role of the AI Control Plane

The recent McKinsey & Company report, “Seizing the agentic AI advantage” has ignited a necessary and forward-looking conversation across boardrooms and technology teams.

It paints a vivid picture of a future where artificial intelligence transcends its current role as a sophisticated chatbot or content generator. In this new paradigm, AI "agents" will function as autonomous, goal oriented systems, capable of reasoning, planning, and executing complex, multi step tasks across an entire organization.

They are envisioned not as tools, but as a new operational backbone, capable of orchestrating entire business processes, unlocking trillions of dollars in economic value.

McKinsey’s analysis is both compelling and directionally correct. The shift from conversational AI to agentic AI is not a matter of if, but when.

These systems promise to redefine productivity, connecting disparate digital tools and automating workflows that are currently fragmented across multiple human teams and software suites.

The report identifies the immense potential, urging companies to act decisively by identifying high value use cases and building the requisite technological foundations.

Building upon this transformative vision, the report advises organizations to “manage the risks” associated with this new level of autonomy.

This recommendation is so crucial that it deserves a deeper, architectural exploration. This is where the grand vision of agentic AI meets the operational challenge of enterprise security, compliance, and control.

Granting autonomy to an AI system with access to your company’s most sensitive data, critical applications, and financial resources is a profound architectural and security challenge.

To help organizations seize the agentic advantage safely, it is essential to define the foundational layer that McKinsey’s high-level framework sets the stage for.

We call this essential layer the AI Control Plane.

This post will build upon McKinsey’s insights, exploring the risks they identify in greater detail and defining the AI Control Plane as the essential architectural component that makes their vision a reality.

It is the layer that provides the security, observability, and governance necessary to turn a promising concept into a trusted, scalable, and truly transformative business asset.

The Agentic Advantage: A New Operating System for Business

To understand the solution, we must first fully appreciate the problem space McKinsey has mapped out. The report defines an AI agent through its core capabilities, which represent a significant leap beyond today’s generative AI. An agent can:

• Reason and Plan: It deconstructs a high level goal, like “Plan a three day marketing offsite in Austin for the product team,” into a logical sequence of sub tasks.
• Use Tools: It can autonomously access and operate a wide array of digital tools, such as calendars, booking websites, procurement software, and customer relationship management systems, to execute its plan.
• Remember and Learn: It maintains context throughout the task, learning from feedback and adapting its approach without needing to be reprompted at every step.
• Act Autonomously: It executes the plan from start to finish with minimal human intervention, making independent decisions to achieve its primary objective.

The "agentic advantage" stems from the ability to automate entire cross functional workflows.

McKinsey offers a potent example in supply chain management.

An agent could detect a shipment delay caused by a storm, autonomously query logistics databases for alternative carriers, contact suppliers via API to check for inventory, renegotiate pricing within predefined parameters, and update all relevant internal systems without a human needing to intervene.

This is not simple automation; it is dynamic, end to end process orchestration.

The vision is clear. Agentic AI offers a new operating system for the enterprise. And like any powerful operating system, it requires a robust framework for security and control to function effectively.

Operationalizing the Vision: Managing Risk at Scale

The report’s call to “manage the risks” provides the perfect starting point for a deeper operational discussion.

To turn this high-level directive into a robust, enterprise-grade reality, we must detail the specific engineering and architectural components required.

The risks associated with autonomous AI agents are a new class of vulnerability, magnified by the agent’s power and autonomy.

Let's explore the specific, high stakes risks that any organization pursuing agentic AI must confront head on.

1. The Exponentially Expanded Threat Surface

Every tool an agent can use, every API it can call, and every database it can access becomes a potential attack vector.

In traditional IT, these access points are managed through human interfaces and role based permissions. An AI agent, however, interacts with these systems programmatically, at machine speed.

A successful prompt injection attack, where a malicious actor tricks the agent into executing unintended commands, is no longer about generating inappropriate text.

It could mean instructing a supply chain agent to reroute a million dollar shipment to an unauthorized address or convincing a finance agent to approve a fraudulent invoice.

The agent becomes a highly privileged insider threat that can be manipulated from the outside.

2. The Autonomy and Alignment Dilemma

The core value of an agent is its autonomy, but this is also its primary source of risk. How do you ensure an agent remains perfectly aligned with its intended goal and constraints, especially when it operates without direct human supervision?

An agent instructed to find cost savings might, through a logical but flawed interpretation of its goal, decide to cancel a critical server contract because it is a significant monthly expense.

The agent would be technically correct; canceling the contract saves money. However, it lacks the broader business context to understand that this action would trigger a catastrophic service outage, costing the company far more in lost revenue and reputational damage.

This is a problem of alignment. Without continuous, context aware guardrails, an autonomous agent can become a powerful but blunt instrument, optimizing for a metric while destroying value.

3. The Unseen Risks of Data Leakage

AI agents, by design, will interact with a rich variety of data sources, from internal financial records and customer databases to third party APIs and public websites.

Each interaction is a potential point of data leakage. An agent tasked with generating a market analysis might inadvertently include sensitive, non public sales figures in a prompt to an external AI model, permanently embedding that proprietary data into a system beyond its control.

Or, it could be tricked into revealing customer personally identifiable information (PII) in a diagnostic log or an email summary.

As agents become more integrated, the pathways for data exfiltration multiply, creating a compliance nightmare, particularly under regulatory frameworks like GDPR and the EU AI Act.

4. The Black Box of Compliance and Auditability

When an agent executes a workflow involving dozens of steps across multiple systems, how do you prove what it did, why it did it, and whether its actions were compliant?

Imagine a regulator asking why a particular loan application was denied or why a specific supplier was chosen over another.

If the decision was part of an autonomous agentic process, a simple "the AI did it" is not a sufficient answer.

Without a detailed, immutable, and easily queryable audit trail of every prompt, every tool interaction, and every decision point, the entire process becomes a "black box."

This lack of transparency makes debugging impossible, accountability meaningless, and regulatory compliance an insurmountable hurdle.

These challenges are not roadblocks, but rather the design requirements for the system that will enable a secure agentic future.

The Solution: Defining the AI Control Plane

To empower AI agents safely, we need to surround them with a persistent layer of security and governance. This is the AI Control Plane.

It is not just another tool; it is a fundamental architectural layer that sits between your AI agents and the digital resources they interact with. It functions as a centralized, policy driven gateway through which all agentic activity must pass.

Think of it as analogous to the control plane in a modern cloud native infrastructure or a telecommunications network.

It is the intelligent fabric that manages, secures, and observes the operational plane where the work actually gets done.

For agentic AI, this means every prompt sent to an agent, every response generated, every API called, and every tool used is inspected and managed in real time, at inference time.

A properly implemented AI Control Plane delivers three foundational capabilities that directly address the risks of autonomy.

Pillar 1: Comprehensive Security Enforcement

The first job of the Control Plane is to act as a dedicated AI firewall. It secures the entire agentic ecosystem from both internal misuse and external threats.

This is not a traditional network firewall; it operates at the application layer with deep awareness of the content and context of Large Language Model interactions.

This security enforcement includes:

• Threat Detection: Actively scanning every incoming prompt and outgoing response for malicious patterns. This includes blocking prompt injection attacks that aim to hijack the agent, detecting attempts to jailbreak the model’s safety constraints, and preventing the agent from interacting with malicious URLs or APIs.
• Tool Use Policy: Enforcing granular permissions on which tools an agent can use, when, and for what purpose. An agent designed for marketing analysis should be blocked from ever accessing the company’s core financial ledgers or human resources systems. These policies are not static; they are context aware and enforced in real time on every transaction.
• Preventing Misuse: The Control Plane can enforce acceptable use policies, ensuring agents are not used to generate harmful, toxic, or off brand content.

Pillar 2: Total Observability and Traceability

The second function of the Control Plane is to render the "black box" of AI operations completely transparent. It serves as the single source of truth for all agentic activity, capturing an immutable, detailed record of every transaction. This is the flight data recorder for your AI fleet.

This observability includes:

• Full Audit Trails: Logging every prompt, response, and intermediate step an agent takes. This includes which tools it used, what data it accessed, and the rationale behind its decisions based on the flow of information.
• Performance Monitoring: Tracking key metrics like latency, cost per transaction, and the frequency of errors or hallucinations. This provides the data needed to optimize agent performance and ensure reliability.
• Debugging and Root Cause Analysis: When an agent produces an unexpected or incorrect outcome, the observability layer provides developers with a complete, step by step trace of the agent’s actions, making it possible to identify the root cause and fix the problem quickly.

Pillar 3: Centralized Governance and Compliance

Finally, the Control Plane acts as the implementation point for all business and regulatory rules. It translates high level corporate governance policies into machine enforceable rules that agents must obey.

This governance layer is responsible for:

• Data Loss Prevention (DLP): Scanning all agent outputs to identify and redact sensitive information before it leaves the trusted environment. This prevents the accidental leakage of customer PII, protected health information (PHI), intellectual property, or financial data.
• Compliance Enforcement: Implementing specific controls required by regulations like GDPR, CCPA, and the EU AI Act. This includes managing data residency, enforcing a user’s right to be forgotten, and providing the documentation required for regulatory audits.
• Human in the Loop Workflows: The Control Plane can programmatically identify situations that require human judgment. It can enforce policies that flag high risk or high value transactions, such as an agent proposing a financial commitment over a certain threshold, and automatically route them to a designated human for review and approval before execution.

NeuralTrust: Building the AI Control Plane for the Enterprise

The concept of an AI Control Plane is not theoretical. It is the architectural principle behind NeuralTrust's suite of products.

McKinsey’s report provides the strategic map to the agentic advantage. NeuralTrust provides the critical infrastructure to navigate there safely. Our platform is designed from the ground up to serve as the AI Control Plane for enterprises that are serious about deploying AI at scale.

Here is how our products map directly to the pillars of a robust Control Plane:

• TrustGate is the Security Enforcement Point: As a dedicated AI Security Gateway, TrustGate sits in front of your AI models and agents, inspecting every single request and response in real time. It is the engine that detects and blocks prompt injections, enforces tool use policies, and prevents agents from being weaponized. It acts as the core policy enforcement point for the entire Control Plane.
• TrustLens is the Observability Engine: TrustLens provides the deep traceability and analytics that make agentic systems manageable. It delivers the full audit trail, showing you exactly what your agents are doing, what data they are accessing, and how they are performing. It is the flight recorder and the control tower, giving you the visibility needed for debugging, compliance reporting, and operational oversight.
• TrustTest is the Proactive Hardening Mechanism: A secure Control Plane must govern secure agents. TrustTest is our automated red teaming product that allows you to continuously test your agents for vulnerabilities before they are deployed.

By simulating thousands of attack scenarios, TrustTest helps you find and fix security flaws, alignment issues, and potential data leakage points during the development lifecycle. This ensures that only hardened, reliable agents are connected to the live Control Plane, dramatically reducing risk in production.

Together, these components form a comprehensive solution for governing agentic AI, turning abstract principles of safety and control into concrete, operational reality.

The Agentic Advantage in Practice: With and Without a Control Plane

Let us revisit McKinsey’s supply chain example. An agent is tasked with resolving a critical shipment delay.

Scenario A: Without an AI Control Plane

The agent queries the public internet for alternative shippers. It is tricked by a sophisticated phishing site masquerading as a legitimate logistics provider.

Through a carefully crafted prompt injection hidden in the site’s data, the attacker instructs the agent to not only reroute the shipment to a new address but also to send a copy of the original invoice, which contains sensitive pricing and customer data.

The agent, optimizing for its goal of "resolving the delay quickly," complies. The company suffers a direct financial loss from the stolen shipment and a data breach from the leaked invoice. There is no clear audit trail, making the investigation a forensic nightmare.

Scenario B: With the NeuralTrust AI Control Plane

The agent begins its task. Its first action, a query to an external logistics API, is intercepted by TrustGate. The policy engine verifies that the API is on the pre-approved vendor list.

The agent then receives data from the provider. TrustGate scans this data for malicious code or prompt injection attempts and finds none.

The agent formulates a plan to reroute the shipment and drafts an instruction for the procurement system. This instruction is again intercepted.

TrustGate recognizes the action as a financial transaction. Because the value of the shipment is over the $50,000 policy threshold, it automatically pauses the agent’s execution and flags the transaction for human review.

A supply chain manager receives an alert, verifies the plan, and provides a single-click approval. The agent then completes the task.

Throughout this entire process, TrustLens has logged every step: the initial alert, the queries made, the data received, the proposed action, the human approval, and the final confirmation.

When the compliance team runs its quarterly audit, the entire incident is fully documented and verifiable. The agent’s power was successfully harnessed, and the risk was effectively managed.

Conclusion: From Vision to Secure Reality

McKinsey’s report serves as an essential call to action. It outlines the "what" and the "why" of the agentic AI revolution.

The next logical step for every enterprise leader is to define the critical "how." How can this level of autonomy be unlocked safely and at scale?

The bridge from the agentic vision to production deployment is built upon a dedicated architecture for security and governance.

The AI Control Plane is that architecture. It is the foundational layer that allows enterprises to move from tentative experimentation to scalable, production grade agentic systems. It provides the guardrails that make autonomy safe, the transparency that makes it manageable, and the governance that makes it compliant.

The companies that truly seize the agentic advantage will be the ones that build on a foundation of trust. By implementing a robust AI Control Plane, organizations can bring McKinsey's transformative vision to life in a secure, scalable, and trustworthy manner.

If you’re deploying LLM agents at scale, start with observability, policy enforcement, and adversarial testing. Start with NeuralTrust.