Infrastructure is the Missing Link for AI Agents – AiThority

Picture a team of enterprise AI agents, ready to escalate incidents, pull data, and trigger automations. But they’re sidelined. Not because the models are wrong, but because the systems can’t support real-time decision-making or execution. That’s the missing link.

Enterprises are investing heavily in agentic AI, with recent projections putting the market at $140 billion by 2032. Yet Gartner warns that over 40% of agentic AI projects will be canceled by 2027 – a signal that while adoption accelerates, infrastructure isn’t keeping pace.

This isn’t a GPU problem. It’s a foundational one.

While GenAI tools operate in simple request/response loops, autonomous agents need persistent memory, real-time data, and system-level execution. Traditional cloud infrastructure wasn’t built for that. It was designed for stateless apps and APIs, not intelligent systems that observe, reason, and act with context.

The result? A growing number of promising agent deployments that hit a wall the moment they try to scale.

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The pilot plateau problem

The bottleneck isn’t the model. It’s the execution environment.

Agentic AI deployments often plateau once it’s time to scale enterprise-wide. According to recent S&P Global research, 58% of organizations say that scaling AI applications in production is very or extremely challenging, a key friction point as teams try to move beyond pilot projects.

The root causes are architectural:

• Rigid foundations that were never designed for agents operating continuously, adapting in real time, and interacting autonomously with internal systems.
• Fragmented systems that are siloed across different platforms and vendors, restricting agents’ access to enterprise systems and creating latency and complexity.
• Execution gaps where agents can’t invoke tools, retain context, or complete multi-step tasks across systems.

Getting past this requires an underlying AI-first infrastructure built for continuity and coordination, where agents access tools, retain context, and interact across systems without friction.

Take a procurement agent, for example: it might check real-time inventory, consult pricing models, and trigger supplier workflows. But to operate effectively, it needs secure access to data, tool invocation at runtime, and the ability to delegate actions to other agents, such as invoicing or legal review.

The agentic infrastructure blueprint

Five architectural elements are emerging as essential for production-ready enterprise agent systems. Each one addresses a critical shortfall in traditional cloud infrastructure – whether around access, execution, or coordination – and together they form the foundation for real-world deployment.

1. Architect for real-time intelligence, not static reporting

Agents can’t act meaningfully without timely, trustworthy context. Traditional data architectures optimized for human consumption and batch processing don’t meet the requirements of systems that need continuous, low-latency access to enterprise state.

That requires a data access layer designed for both speed and control:

• Hybrid access paths that support both streaming telemetry (e.g. sensor data, event logs) and structured queries (e.g. ERP, CRM systems).
• Private connectivity using VPC peering, VPNs, or direct interconnects to avoid public internet exposure.
• Fine-grained RBAC to define what each agent can access and under what conditions.
• Comprehensive auditability, so every data interaction is logged, traceable, and reviewable for compliance.

Unlike traditional applications, agents rely on continuous, low-latency access to system state, such as current inventory, user activity, or workflow status, to make relevant decisions. Without this real-time data foundation, agents are limited to static inputs and cannot adapt to changing conditions or business requirements.

2. Localize agent memory to meet compliance at scale

Compliance, not convenience, increasingly shapes how and where retrieval workflows are designed. Vector databases used in retrieval-augmented generation (RAG) must stay within approved jurisdictions, support low-latency access, and provide the auditability required to meet regional compliance standards.

This necessitates:

• Jurisdiction-aware routing so agents query only region-appropriate vector stores based on data residency requirements.
• Geographic deployment flexibility with support for edge locations or in-country hosting to meet sovereignty requirements.
• Embedded governance controls, including audit trails and access controls, are especially critical in financial, healthcare, or public sector deployments.

RAG cannot be treated as an abstract architecture layer. It’s the operational memory of agent systems – and if that memory isn’t governed appropriately, agents risk acting on data they shouldn’t access or making decisions they cannot justify to regulators.

3. Self-host models to control execution and protect context

While model APIs from providers like OpenAI or Anthropic work for simple tasks, agentic systems require deeper control over model execution. Privacy, latency, and compliance concerns make it necessary to bring model execution in-house, especially when dealing with sensitive data or mission-critical operations.

Self-hosting containerized large language models (LLMs) or specialized small language models (SLMs) provides:

• Isolated environments to securely run models with clear boundaries between workloads and systems.
• Task-specific inference policies that define which agents can use which models based on role or compliance requirements.
• Dynamic model routing that allows agents to switch between different models (e.g., planners, summarizers, validators) based on the task at hand.
• Cost and performance optimization through the use of smaller, domain-tuned models that deliver better performance within enterprise boundaries

This approach enables teams to maintain control over their most sensitive reasoning processes while optimizing for specific use cases and compliance requirements.

4. Use MCP to expose tools as secure, callable functions

For agents to act effectively, they need structured access to enterprise tools and services. Today, that access is often implemented through brittle, manually-configured APIs or custom scripts that create maintenance overhead and security risks. MCP replaces these ad hoc patterns with a discoverable, machine-readable interface that enables agents to securely invoke internal capabilities at runtime.

In practice, that means:

• A standardized schema for exposing internal tools and system functions in a consistent, discoverable format.
• Granular permission systems that control what each agent can access and execute based on role, context, and business policies
• Deployable MCP servers that sit close to the agent, reducing latency and enabling secure, localized execution.

MCP turns system access from a hardcoded integration challenge into a runtime coordination layer that scales as agent behavior grows more complex.

5. Build coordination into the system – not the agent

In real enterprise deployments, agents increasingly need to collaborate – sharing context, dividing complex tasks, or working across different domains and systems.

Agent-to-agent coordination introduces the protocols needed to maintain trust, identity, and session continuity across distributed agent systems.

That requires:

• Shared session context so agents can build on one another’s actions across steps, domains, or time periods.
• Delegation frameworks that define what authority one agent can pass to another, with clear audit trails.
• Event-driven communication patterns that allow agents to react and adapt without central orchestration.

A2A protocols are an architectural requirement for scaling agent systems safely and predictably. As agents take on distributed, interdependent roles, coordination must be built into the system itself: authenticated, policy-governed, and observable by design.

Fill in the infrastructure gap

Scaling agentic AI successfully depends on the systems that support and enable it. This demands an architectural mindset where composability, observability, and trust are treated as core infrastructure capabilities.

The elements outlined here are not theoretical constructs; they’re being implemented today by mature AI and platform engineering teams that understand the stakes of moving beyond copilots and API wrappers. Whether starting small or scaling fast, these elements offer a clear foundation for operationalizing agents in production.

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