AI Agents Moving Decision-Making Up the Stack

Right now, most enterprise systems aren’t feeling the friction. Early agents are still chatbots, copilots, or isolated productivity enhancers.

But as they move into business workflows (you know, things like order resolution, claims processing, triage, fulfillment) they start interacting with real systems. That means:

• Infrastructure needs to interpret agent-carried logic
• Gateways need to parse more than just JWTs and paths
• Load balancers need to stop thinking in terms of “fastest” and start thinking in terms of “fit for this task”

It’s not a crisis yet. But it’s coming. And when it hits, the issue won’t be lack of bandwidth. It’ll be a mismatch between how agents make decisions and how infrastructure handles traffic.

Here’s the real architectural punchline: agents are moving decision-making up the stack.

The request becomes the control plane.

It doesn’t ask the infrastructure, “What should I do?” It tells it: “Here’s what I need. Here’s how I want you to behave. Now get it done.”

If your systems treat that like a normal request, just another GET or POST, then fallback logic will collide, retries will overlap, and agent performance will degrade for reasons that won’t show up in your dashboards.

Not because the infra failed. But because it wasn’t listening.

This shift isn’t theoretical, it’s emerging through real frameworks. Efforts like the Model Context Protocol (MCP), Agent-to-Agent (A2A) communication models, and even early work in policy-wrapped task routing are all pointing in the same direction:

• MCP pushes full context—goals, constraints, fallback options—into the request payload itself.
• A2A protocols enable agents to coordinate tasks and hand off decisions dynamically, without a central orchestrator.
• Runtime task delegation via structured metadata lets the infrastructure interpret—not decide—at the point of execution.

They differ in syntax, structure, and abstraction—but they all land on the same architectural outcome: policy in the payload and purpose in the request.

And once the request becomes the carrier of logic, infrastructure has two choices: adapt or become little more than a pipeline moving payloads.

This isn’t about ripping and replacing. It’s about getting ahead of the shift while it’s still early. Start here:

1. Capture context
   Log X-Intent, X-Task-Profile, and any other metadata that might hint at what the agent is trying to accomplish. If your observability ends at the URI, you're already behind.
2. Think in terms of “task fitness,” not “availability”
   A backend that’s up doesn’t mean it’s good enough. Start experimenting with health models that account for SLA, latency thresholds, and intent.
3. Get smart with your policies
   Existing application delivery and security platforms have scripting layers. Use them. Start parsing intent headers. Modify routing rules based on request goals, not just service path.
4. Plan for adaptive enforcement
   Instead of defining fallback logic in a static config file, begin exploring how infrastructure can enforce fallback instructions embedded in the request itself.
5. Build a feedback loop
   If agents are rerouting traffic based on past performance, you want to be part of that loop. Feed outcome context back into your traffic and health systems.