Top 5 Reasons Agentic Developers Will Work Around Your MCP Gateway — Especially for AI Agents

MCP gateways are being deployed with good intentions.

Security teams want guardrails before autonomous systems touch real data and real infrastructure. Platform teams want a single place to enforce policy. Leadership wants confidence that agentic systems won’t surprise them in production. All reasonable goals.

And yet, in practice, many teams discover the same outcome: the gateway is there — but developers quietly route around it to get their job done.

This isn’t a failure of discipline. It’s not a culture problem. And it’s not because teams don’t care about the risk. It’s because AI agents change where risk actually lives.

Gateways were built to govern requests. Agents introduce risk during execution.

That execution gap is why workarounds appear — and why simply tightening gateway policy rarely helps.

1. Gateways Slow Learning by Breaking the Agent Feedback Loop

Agentic development is not a linear build–test–deploy cycle.  It’s an iterative control problem. Teams are constantly:

• adjusting goals and reward signals

• changing tool availability and constraints

• testing how agents behave under partial context

• observing how decisions compound across steps

Progress depends on tight feedback loops between agent intent, agent behavior, and real-world effects.  MCP Gateways disrupt that loop. When a gateway blocks, modifies, or rewrites an agent’s action without exposing:

• how the agent reasoned its way to that action,

• which intermediate state or context triggered the policy,

• or what execution path would have followed,

the team loses the ability to distinguish between:

• flawed agent logic

• missing or misleading context

• overly broad policy

• or legitimate unsafe behavior
  
  

From a developer’s perspective, the system stops being debuggable.

At that point, the gateway isn’t acting as a safety mechanism — it’s acting as an information sink. It removes visibility at precisely the moment teams need more context, not less.

So teams do what experienced engineers always do when a system becomes opaque:
they move closer to execution.

They bypass the gateway in development. They run agents locally or against real systems. They instrument tool calls directly. Not to avoid controls — but to recover observability.

It’s a rational response to a broken learning loop.  When visibility is missing, enforcement gets treated as an obstacle — not a guardrail.  And once learning happens outside the gateway, control inevitably follows it.

2. Gateways Answer the Wrong Question

Gateways are good at answering:

“Was this request allowed?”

But agentic systems force a different question:

“What actions did the agent take — and why?”

Most of the risk in AI agents doesn’t come from a single prompt or tool call. It comes from how decisions evolve across steps, how tools are chained, and how state changes propagate across systems.

That execution path rarely passes cleanly through a gateway.

Developers feel this immediately. Security teams feel it later — usually during an incident, when the gateway logs look fine but the outcome wasn’t.

When a control can’t explain behavior, it stops being trusted as authoritative.

3. Gateways Force Old Team Boundaries in a New Execution Model

From a security perspective, a gateway represents governance and risk reduction.
From a developer perspective, it often represents friction without insight.

That tension isn’t accidental — it’s a signal that teams are applying old mental models to a fundamentally new execution layer.

AI agents don’t fit cleanly into “build vs. protect” roles. Their behavior unfolds across prompts, tools, data, and runtime systems, often owned by different teams. When no one has a shared, end-to-end view of that behavior, enforcement decisions inevitably feel arbitrary — even when they’re correct.

Over time, this changes how teams operate:

• Security assumes risk is managed because controls exist.

• Developers assume progress requires working around those controls.

• Platform teams are left stitching together partial views after incidents.

The organization isn’t aligned around outcomes — it’s fragmented around tools. What’s missing isn’t policy. It is shared execution context.

When teams can see the same agent behavior, in the same timeline, with the same causal story, the conversation changes. Governance stops being something “applied” to development and becomes something co-owned during execution.

That shift is more than cultural. It creates the conditions for new operational practices to emerge:

• Security defining guardrails in terms of behavioral outcomes, not static rules

• Developers iterating faster because they understand how agents actually behave

• Platform teams operating agents as first-class production workloads

This isn’t just better collaboration — it’s how organizations unlock the business value of autonomous systems without slowing down or increasing risk.

Gateways expose the fracture.
Execution-level visibility is what allows teams to evolve past it.

4. Agent Execution Doesn’t Stay at the Boundary

AI agents don’t behave like traditional services. Once invoked, they can:

• chain multiple tool calls,

• operate over time,

• modify internal and external state,

• trigger processes that never re-enter the request path.

Much of this activity happens after the gateway has already made its decision. Developers see this clearly. Security tools generally don’t.

That’s when teams stop believing the gateway represents “the system” — because the system’s most important behavior is happening elsewhere.

5. Gateways Break Down Under Agent Scale and Fleet Behavior

Gateways are designed to reason about individual requests.
AI agents operate as fleets of autonomous actors.

As organizations scale from a handful of agents to dozens — or hundreds — a new class of problems emerges:

• Agents interacting with the same systems in overlapping ways

• Subtle behavior drift across versions and environments

• Cumulative side effects that only appear at system scale
  
  

Gateways treat each action in isolation. They have no notion of:

• historical behavior,

• cross-agent interaction,

• or emerging patterns over time.
  
  

Developers and platform teams quickly learn that gateway policies don’t help them understand or manage fleet-level behavior. So they instrument agents directly, bypass centralized controls, and build local tooling just to keep systems stable.

This isn’t about policy looseness.
It’s about the gateway model not scaling to autonomous execution.

At fleet scale, control requires understanding behavior over time — something gateways were never built to do.

This Isn’t a Gateway Problem. It’s an Execution Problem.

MCP gateways aren’t useless. They matter at the boundary. The mistake is assuming boundary control equals behavioral control.

Consider a simple, real scenario:
An agent passes cleanly through the gateway, calls an approved tool, and updates a CRM record. That update triggers a downstream workflow, which modifies pricing data, which syncs to billing, which emails a customer—automatically.

Nothing violated gateway policy. But the outcome is wrong, irreversible, and no one can explain why it happened.

That’s the gap.

AI agents introduce a new execution layer—where decisions compound over time, across tools, data, and systems. Existing tools weren’t built to observe, explain, or govern behavior at that layer. When execution is invisible, developers can’t debug, security can’t define meaningful guardrails, and ownership collapses.

So teams route around the gateway—not to avoid safety, but to regain understanding.

The real question organizations face isn’t:

“How do we enforce gateways more strictly?”

It’s:

“How do we make agent execution observable, understandable, and governable—without slowing teams down?”

Because control that stops at the request boundary doesn’t control outcomes.
And in agentic systems, outcomes are the only thing that actually matters.