Every few decades, infrastructure teams face a reckoning. First it was servers. Then containers. Then APIs sprawling across microservices. Now it’s AI traffic—and the rules are being rewritten again.

Agents are talking to agents. MCP servers are routing requests. LLM calls are flying across your stack faster than your observability tools can track them. This is the new networking. And without the right controls, it’s chaos waiting to happen.

The Traffic You Didn’t Plan For

Here’s the reality: Your infrastructure was built for human-initiated requests. A user clicks a button. An API responds. Logs capture the event. Simple.

AI agents don’t work that way.

A single user prompt can trigger dozens of downstream calls. Agents query knowledge bases. They invoke tools. They call other agents. They hit external APIs, spin up workflows, and make decisions—all before the user sees a response.

This is cross-traffic at scale. And it’s growing exponentially.

The patterns we’re seeing:

• Agent-to-agent communication. Orchestration layers dispatching tasks to specialized agents.
• MCP server routing. Model Context Protocol servers managing tool access, memory retrieval, and execution contexts.
• LLM traffic surges. Multiple model calls per interaction: summarization, classification, generation, validation—stacking latency and cost.

Traditional monitoring wasn’t built for this. Neither were your firewall rules, rate limiters, or access controls.

We’ve Seen This Movie Before

If you’ve been in infrastructure long enough, this feels familiar.

Remember when APIs exploded across the enterprise? Suddenly every team was exposing endpoints. Every service was calling every other service. API gateways became essential—not optional. Rate limiting, authentication, versioning, deprecation policies. The wild west became manageable.

AI is following the same arc. Faster.

The difference? AI systems make autonomous decisions. They chain actions together. They retry, adapt, and escalate. An API is a static contract. An agent is a dynamic actor.

This demands a new layer of control. Call it AI governance. Call it agent orchestration. Call it the DevOps of LLMs. The name matters less than the function: visibility, control, and guardrails for AI traffic.

The Anatomy of AI Cross-Traffic

Let’s break down what’s actually moving through your systems.

Agent-to-Agent Communication

Modern AI architectures don’t rely on a single monolithic model. They use specialized agents: one for research, one for code generation, one for summarization, one for validation. These agents hand off tasks, share context, and coordinate execution.

Without proper controls, you get:

• Circular dependencies (Agent A calls Agent B calls Agent A)
• Unbounded execution loops
• Context leakage between isolated workflows
• Cost explosions from recursive calls

MCP Servers and Tool Access

The Model Context Protocol is emerging as a standard for connecting LLMs to external tools and data sources. MCP servers act as intermediaries: managing what tools an agent can access, what data it can retrieve, and what actions it can execute.

This is powerful. It’s also a new attack surface.

Every MCP server is a potential chokepoint. Every tool invocation is a permission decision. Every memory retrieval is a data access event. Ops teams need to treat MCP servers like they treat API gateways: monitored, rate-limited, and locked down.

LLM Traffic Patterns

LLM calls aren’t cheap. They’re not instant. And they’re rarely singular.

A typical agentic workflow might involve:

1. Initial query classification (LLM call #1)
2. Context retrieval and augmentation (LLM call #2)
3. Primary response generation (LLM call #3)
4. Output validation or fact-checking (LLM call #4)
5. Summarization or formatting (LLM call #5)

Five calls. One user interaction. Multiply by concurrent users. Multiply by agents running background tasks. The math gets uncomfortable fast.

Without traffic shaping, you’re looking at unpredictable costs, latency spikes, and rate limit breaches with your model providers.

Control Is the Product

Here’s the shift in mindset. For developers and ops teams, control is no longer a constraint. It’s the product.

Your job isn’t just to ship features. It’s to ship features that behave predictably in production. That means:

Observability for AI workflows. Not just logging LLM calls: tracing entire agent execution paths. Which agent triggered which tool? What context was passed? Where did the workflow branch? Standard APM tools don’t capture this.

Guardrails that enforce policy. Content filters. Output validators. Cost ceilings. Execution timeouts. These aren’t nice-to-haves. They’re production requirements. An agent without guardrails is a liability.

Access control for agent actions. Not every agent should invoke every tool. Not every workflow should access every data source. Principle of least privilege applies to AI systems just like it applies to human users.

Rate limiting and traffic shaping. Burst protection for LLM APIs. Queue management for agent tasks. Priority lanes for critical workflows. The same patterns we use for API traffic apply here—with agent-specific adaptations.

The Tooling Gap

Here’s the uncomfortable truth: Most organizations are flying blind.

They’ve deployed agents. They’ve connected MCP servers. They’ve integrated LLMs into production workflows. But they haven’t instrumented any of it.

Logs exist—but they’re scattered across services. Costs are tracked—but only at the provider level, not the workflow level. Errors surface—but root cause analysis takes hours because the execution path isn’t traceable.

This is the tooling gap. And it’s where the next generation of DevOps investment needs to focus.

The requirements are clear:

• Unified telemetry across all AI components
• Policy engines that enforce guardrails at runtime
• Cost attribution down to the workflow and agent level
• Anomaly detection tuned for AI-specific patterns
• Audit trails for compliance and debugging

The Path Forward

AI infrastructure is evolving fast. The patterns are still forming. But the direction is clear.

Control is not the enemy of innovation. It’s the enabler. Teams that invest in observability, guardrails, and governance will ship faster—because they’ll spend less time debugging production incidents and explaining unexpected costs.

The new networking is here. Agents are the new services. MCP servers are the new gateways. LLM traffic is the new API sprawl.

DevOps teams built the tooling for the last wave. Now it’s time to build for this one. What’s your strategy to address this?