Middle layer makes or breaks enterprise AI agents

The hype surrounding AI agents is warranted, but their real-world value hinges less on the models themselves and more on the middleware connecting them to enterprise systems. Agents represent a shift from simply generating information to executing actions, making the "middle layer" of APIs, data streams, tools, and identity management critical for moving beyond sandbox environments.

The model is becoming the commodity layer

After decades of leading technology organizations, I have observed that setting unrealistic expectations for how AI models operate within existing enterprise workflows is a common mistake. Financial services provide a clear example: when an investing agent needs to calculate precise metrics like cash burn ratios, it relies on deterministic analysis in languages such as Python or Node.js rather than asking the Large Language Model (LLM) to perform the math directly.

As models improve, the differentiator lies not in the model itself but in what engineers build around them to turn reasoning into trustworthy action. Organizations must recognize that they cannot simply layer agents onto existing workflows and expect reliable decision-making or consistent execution without significant adaptation.

The middle layer is where agents become enterprise software

For AI agents to deliver value outside a sandbox, they require robust connectivity to retrieve information from systems, pass data between applications, and execute actions within business-critical tools like CRMs or ERPs. This "middle layer" acts as the connective tissue between reasoning and execution; the difference between an agent answering questions versus one retrieving customer data and executing next steps in a system is fundamental.

Integration maturity varies widely across enterprises today:

• Some organizations suffer from "point-to-point" technical debt.
• Others have mature AI integration infrastructure, investing in platforms such as Workato, MuleSoft or Apigee while building dedicated engineering practices and teams to operate these connections. Organizations with the latter approach find adding agents exponentially easier.

Another factor is the rising prominence of Model Context Protocols (MCPs), which are becoming a new standard for agent communication. While hundreds of MCPs exist within enterprise tools like Claude, Gemini or ChatGPT, this does not replace the need for an API/MCP management tool. Regardless of whether B2B transactions are performed via MCP or custom apps expose capabilities, the technical requirement to manage these connections remains unchanged.

Agents will expose the technical debt we’ve learned to work around

Modernizing system connections to prepare for agent integration can reveal critical gaps in existing environments where humans previously picked up the slack through manual processes like data unification or handoffs. Since agents cannot compensate for inconsistencies in the same way humans do, inaccessible data, unreliable APIs, undocumented business logic, and non-communicating systems will hinder their capabilities.

The danger lies in trying to recreate human workarounds for individual agents rather than fixing the underlying architectural problems. As organizations deploy more agents, one-off fixes can snowball into fragmented layers of novel integrations and workflows that IT teams must maintain, potentially increasing complexity instead of reducing it.

Enterprise-grade agents require a different engineering discipline

Agentic AI introduces probabilistic behavior into enterprise environments, contrasting with the predictable inputs and deterministic logic found in traditional enterprise software. This means the same request can produce different outputs depending on context, making workflows unreliable if connections are faulty or prompts drift over time.

Even simple agentic workflows can become derailed by inconsistent schedules or connection issues, requiring significant engineering oversight to operate reliably. If a low-stakes workflow is easily disrupted, consider the immense engineering required for an agent taking consequential action across financial systems. Enterprises cannot simply buy an agent and expect stable automation; they must integrate it as part of the whole system with ongoing testing, monitoring, and maintenance by dedicated teams.

CIOs need to build the organization around AI Agents

Preparing your organization to scale agents requires looking beyond technology alone. While mature integration practices and dedicated software engineering teams are essential, these capabilities must be matched by strong technical leadership and a culture that understands software, data, and calculated risk. Leaders must recognize where AI reasoning creates value versus where conventional automation is superior, while simultaneously fostering an environment comfortable with experimenting on probabilistic systems without sacrificing engineering discipline.

To move beyond individual experiments and scale agents across the enterprise, CIOs need four specific capabilities:

• Mature integration practices
• Dedicated software engineering teams
• Strong technical leadership
• A culture that understands software, data and calculated risk

Enterprise connectivity has a new purpose

Historically, enterprise connectivity enabled information to move between systems. In the agentic era, it increasingly enables software to reason and act across them. With proper infrastructure supporting these connections, agents can eliminate manual handoffs, accelerate decision-making, and scale expertise without adding layers to existing operations.

As models become more capable and widely available, access to the technology will cease to be a primary differentiator. What separates organizations is their ability to build systems around Agentic AI that work dependably across business-critical applications. Ultimately, the greatest advantage from Agentic AI belongs not to those with the most agents or best models, but to those invested in the supporting infrastructure, integration practices, and engineering capabilities.