Building AI agents is an engineering challenge. Making them interoperable, reusable, secure, and scalable is an enterprise architecture challenge.
In part 1, we argued that every agent needs a harness: the control layer that decides whether, when, and under what conditions it may act. This part asks what happens when you have fifty of them.
Enterprise AI is moving beyond individual copilots and isolated AI applications. We are beginning to explore systems where specialized agents collaborate across business domains, access enterprise capabilities and contribute to larger workflows.
Consider a business process that requires policy validation, risk assessment and transaction execution. Rather than one application handling everything, multiple specialized agents could contribute to the outcome.
But this introduces a fundamental architecture question:
The answer requires three complementary layers: protocols for interaction, a platform for shared enterprise services, and governance that defines identity, ownership, access, and accountability.
An agent needs to interact with enterprise capabilities such as APIs, business applications, tools, and data. It may also need to collaborate with other agents.
This is where the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol come in. Anand Dosapati ‘s post covered how they differ in How the Model Context Protocol Went from Experiment to Industry Standard. In short:
Neither defines the complete enterprise architecture. They address different interaction patterns within it.
Return to the incident from Part 1. Latency spikes after a deployment. The incident agent reads logs and metrics through MCP and traces the spike to database connection-pool exhaustion. It recommends a rollback, but rollbacks belong to the change-management agent, which another team built and owns. So, the incident agent asks it, through A2A, whether a rollback is allowed in the current change window. The change agent checks the release calendar and approves.
Neither protocol decides whether the rollback actually runs. That decision belongs to the harness: whose identity the request carries, whether a production change needs a human sign-off, and how the whole exchange is logged so it can be reconstructed later.
Together, these protocols can standardize how interactions occur, but they do not determine which agents should exist, who may invoke them, how trust is established, what service levels apply or how failures are handled. Those responsibilities belong to the surrounding enterprise architecture and operating model.
The architectural opportunity is to combine these interaction patterns without creating another collection of tightly coupled integrations.
Consider a change-management agent. Incident response, release pipelines and infrastructure teams all want to ask it the same question: is this change allowed right now?
A shared agent could reduce duplicated logic and make change-control logic reusable across teams. But it also becomes a shared dependency, requiring clear ownership, access controls, versioning and resilience.
Likewise, adding agents to every workflow may increase complexity without improving outcomes. A deterministic operation may be better served by a traditional API, while a reasoning-intensive task may justify an agent.
The key design decision is not how many agents we can build. It is where autonomy and collaboration create measurable business value.
This is where architecture must balance several competing concerns:
These are architectural trade-offs, not protocol configuration choices.
As agent ecosystems grow, the harness controls from Part 1 (identity, authorization, observability and recovery) stop being something each agent builds for itself. They become a shared platform, joined by two needs a single agent never had: discovery and lifecycle management.
An enterprise must be able to establish which agents exist, who owns them, what they can access, how they delegate work and how their actions can be traced.
The objective is not to centralize every decision. It is to provide common guardrails while allowing specialized agents to evolve independently.
Protocols enable interaction. The platform, a shared harness, enables the enterprise to manage that interaction at scale.
At Vericence, we see agentic AI as an opportunity to rethink how enterprise capabilities are designed, composed and governed.
The starting point should be the business outcome — not the technology.
Architecture should determine what remains a service, what becomes an agent, which capabilities should be reusable, and when collaboration genuinely adds value.
Interoperability standards, security, observability and governance should be part of the design from the beginning, with platform capabilities evolving alongside adoption.
The goal is not to deploy more agents. It is to build reliable, reusable and economically viable intelligent capabilities that solve real business problems.
We have spent decades designing systems where applications communicate through APIs and services.
We are now exploring systems where agents can reason, act, delegate and collaborate.
That evolution requires us to think beyond connectivity toward agent identity, contracts, ownership, trust, and ecosystem scalability.
MCP and A2A are important building blocks, but they are not the architecture itself.
The larger challenge is designing an enterprise where intelligent capabilities can be composed and reused without compromising security, reliability, or control.
Perhaps the future of enterprise AI is not one super-agent that does everything.
Perhaps it is an ecosystem of specialized agents — supported by an architecture that allows them to work together responsibly and at scale.