In a previous post, I argued that middleware has become the AI control plane for the enterprise. As large language models move from generating recommendations to taking action, the Model Context Protocol (MCP) marks an important shift in how AI systems interact with real tools and data. By allowing models to invoke tools directly, MCP shortens the path from intent to execution and changes how work gets done inside enterprise systems.
That speed is powerful, but it also exposes a gap. When execution becomes easy, coordination, control, and accountability become harder. MCP addresses the N×M integration problem between models and tools, but it does not define how actions are governed, monitored, or constrained once agents are operating within production systems.
If agent autonomy is going to scale without introducing instability or risk, orchestration and governance cannot be optional. The question this post, part two, explores is simple but critical: what must an AI control plane actually guarantee for autonomous agents to operate safely and reliably in the enterprise?
The first failure mode: onboarding tools instead of capabilities
Most MCP adoption starts the same way. Teams expose what they already own:
- A script becomes a tool
- A query becomes a tool
- A microservice becomes a tool
- A workflow becomes a tool
Locally, this works. It reduces setup time and shows immediate progress. At the enterprise level, it creates a problem that usually surfaces later, during the first incident that crosses team or system boundaries.
A tool is not something the business can reason about. Tools describe mechanics. Enterprises reason about outcomes. What the business actually cares about looks more like this:
- Onboard a customer
- Resolve a billing dispute
- Provision an employee
- Issue a refund
- Close an incident with guardrails
- Create an opportunity and validate it against policy
The gap between those two perspectives is where MCP sprawl begins. An agent does not understand organizational meaning unless you encode it. It does not know that five individually valid tool calls, executed in the wrong order, can produce the same downstream impact as a faulty production change. And because agent decisions are probabilistic, the execution path can vary from run to run.
This leads to the first principle of a real AI control plane: expose fewer things, and expose higher-level things.
Instead of exposing every atomic action, expose capabilities that represent outcomes you are willing to stand behind:
- Not “update customer record”
- Not “create invoice”
- Not “apply discount”
- Not “send email”
Expose “resolve billing issue,” and hide the mechanics behind a governed workflow that enforces ordering, retries, approvals, masking, and logging by default.
What the control plane must guarantee
Once you shift from tools to capabilities, the role of the control plane becomes clearer. It exists to provide a specific set of execution guarantees. These guarantees are what allow agent autonomy to operate safely inside production systems.
1. Capability abstraction
Agents should not dynamically assemble enterprise internals. They should invoke stable, versioned capabilities that reflect business intent. In practice, this means:
- One “onboard customer” capability instead of a web of CRM, billing, and support tools
- Inputs and outputs that describe intent rather than internal schema details
- Explicit versioning, because business rules change even when agents do not
This is where integration platforms have historically played a quiet but critical role, translating many system APIs into a single, governable business process.
2. Policy enforcement at the moment of action
MCP enables connection, not control. A production control plane must decide whether an action should proceed before it happens. That requires answering questions such as:
- Is this agent allowed to perform this action in this environment for this customer segment?
- Is the data scope appropriate, including masking, handling PII, and regulatory constraints?
- Are we within rate limits, change windows, or separation-of-duties rules?
- Does this action require human approval or secondary validation?
As MCP ecosystems grow, identity fragmentation becomes a real risk. Many tools, many credentials, and inconsistent access controls are not theoretical problems. They are how enterprises get breached.
3. State and idempotency
Agents retry. Frameworks retry. Networks fail in partial and confusing ways. Without a durable state, helpful automation becomes duplicated refunds, duplicated tickets, and duplicated account changes. A real control plane makes this explicit through:
- Correlation IDs across steps
- Deduplication keys
- Safe retries and compensating actions
- Clear failure modes instead of narrative explanations
4. Deterministic orchestration around probabilistic decisions
Let the agent decide what to do. Do not let it decide how execution unfolds. The separation is intentional:
- The probabilistic layer interprets intent and selects a capability
- The deterministic layer handles orchestration, ordering, error handling, and rollback
In part one, I warned about execution paths discovered at runtime. This is how that risk is contained without eliminating autonomy.
5. Observability that supports accountability
You do not need more logs. You need answers:
- What did the agent attempt?
- Which capability was invoked?
- What data was accessed, and what was masked?
- Which downstream systems were touched, and in what order?
- What changed, and who owns the outcome?
If your post-incident explanation ends with “the model decided,” the system is not production-ready.
6. Human oversight as part of the workflow
Human-in-the-loop is often framed as a fallback. In mature systems, it is a design feature. Oversight works best when it is structured:
- Approvals above the defined thresholds
- Reviews for sensitive actions like access changes or data exports
- Routing to the correct owner rather than a generic queue
7. Simulation and change management
Capabilities evolve, and they must be treated like software. That means:
- Sandbox execution
- Replay against historical cases
- Drift detection when outcomes change without version updates
- Defined rollback paths
MCP makes it easy to add tools. A control plane makes it safe to evolve capabilities.
Where MCP ends, and enterprise responsibility begins
Viewed end-to-end, the architecture is straightforward:
- Agents and applications interact with users
- MCP provides the standard protocol layer
- The enterprise exposes a governed capability catalog
- An integration and orchestration layer enforces execution guarantees
- Systems of record remain systems of record
MCP functions like a universal connector. It is not an operating system. Enterprises still need centralized control over permissions, policies, processes, and audits, even when execution happens close to data for latency, privacy, or sovereignty reasons.
This is why integration and orchestration quietly reemerge as the control plane for agentic systems. They provide the governance layer that MCP intentionally leaves out.
How to avoid MCP sprawl before it hardens
If teams are already MCP-enabling everything they own, the solution is not a policy memo. It is changing what success looks like.
First, stop onboarding tools and start onboarding capabilities. Make capability creation the work product. Teams can still build tools internally, but what gets published has clear intent, constraints, ownership, and auditability.
Second, centralize enforcement while decentralizing contribution. Let teams contribute capabilities freely, but standardize identity, policy, logging, testing, and deployment. This prevents innovation from turning into operational instability.
Third, treat observability as a product requirement. If you cannot answer “what happened” quickly, autonomy will eventually be scaled back. That arc is predictable, and it is the same one described previously.
What this means in practice
MCP adoption will continue to accelerate. The ecosystem will grow. The number of tools will multiply.
The organizations that succeed will not be the ones with the most MCP-enabled tools. They will be the ones who let agents act without turning execution into chaos.
That requires a control plane that is opinionated about outcomes.
- Agents stay flexible
- Execution stays stable
- Autonomy stays governable
The infrastructure that enables this will not be flashy. It will not be the part that trends. But it is what enterprises will standardize on once agentic execution hits real operational limits.
If you are already thinking about how to move from MCP experimentation to governed, production-grade autonomy, this is the moment to look at how a real integration-led control plane works in practice.
Book a demo to see how agentic execution can be coordinated, governed, and observed without sacrificing speed.
Intelligence is optional. Coordination is not.
