Building an Agent Control Plane (Before the Enterprise ones arrive!

Building an Agent Control Plane (Before the

Enterprise ones arrive!

Most engineers are being introduced to “AI platforms” and “control planes” through polished demos and enterprise tooling that doesn’t exist yet in their day-to-day work.

That’s a problem.

Because control planes are not magic.

They’re architecture patterns — and you can understand (and even build) them today, inside your own repos and clusters, with tools you already control.

This article walks through a practical, developer-owned approach to building a lightweight agent control plane using:

• Git webhooks

• Python microservices

• Large Language Models (LLMs)

• Explicit reasoning flows

No vendor dependency.

No central platform required.

No enterprise permissions needed (for a first pass).

Just solid architectural thinking.

The Mental Shift: From Pipelines to Control Planes

Traditional pipelines work like this:

Code → Pipeline → Result

A control plane works differently:

Event → Reason → Decide → Act

That distinction matters.

Instead of hardcoding behavior into YAML or scripts, you introduce a decision layer that can:

• Inspect context

• Apply policy

• Choose what to do — or not do

That’s what an agent really is.

The Simplest Control Plane You Can Build

At a minimum, a control plane has four responsibilities:

1. Ingest events

2. Route based on policy

3. Execute reasoning

4. Produce a decision or recommendation

Here’s what that looks like in practice.

This entire system can live inside your own AWS cluster or Kubernetes environment.

Step 1: Events Are the Trigger (Not Humans)

Modern Git systems already emit events:

• Pull request opened

• Merge request updated

• Code pushed

• Pipeline failed

You don’t write this infrastructure.

You subscribe to it.

Git platforms send these events via webhooks — HTTP POSTs to an endpoint you control.

That endpoint becomes the front door of your control plane.

Step 2: A Python Web Service Is Your Control Plane

At its simplest, your control plane is just a Python service.

Example using FastAPI:

from fastapi import FastAPI, Request

app = FastAPI()

@app.post("/events/git")

async def handle_event(request: Request):

    payload = await request.json()

    event = normalize_event(payload)

    decision = route_and_reason(event)

    return {"status": "accepted", "decision": decision}

Key rule:

The webhook handler should do almost nothing.

It should:

• Validate the request

• Normalize the event

• Hand it off to internal logic

No business logic here.

Step 3: Normalize Events Early

Raw webhook payloads are noisy.

Your control plane should reduce them to engineering-relevant facts:

def normalize_event(payload):

    return {

        "event_type": payload["object_kind"],

        "repo": payload["project"]["path_with_namespace"],

        "author": payload["user"]["username"],

        "changed_lines": estimate_diff(payload),

        "labels": extract_labels(payload)

    }

This is where control planes differ from pipelines:

• Pipelines react blindly

• Control planes reason over meaning

Step 4: Policy Before Intelligence

Before you invoke any AI model, ask:

• Is this repo even in scope?

• Is this event relevant?

• Are we in advisory or enforcement mode?

def policy_allows(event):

    return (

        event["changed_lines"] > 500

        and "refactor" in event["labels"]

    )

This protects you from:

• Cost explosions

• Noise

• Accidental automation

LLMs should be gated, not default.

Step 5: Agents Are Reasoning Units, Not Bots

An agent is not a chat interface.

An agent is a structured reasoning flow:

1. Gather context

2. Apply constraints

3. Produce a decision

4. Explain the decision

Example agent structure:

class RefactorAgent:

    def init(self, llm):

        self.llm = llm

    def run(self, context):

        prompt = f"""

You are a senior software architect.

Context:

{context}

Rules:

- Do not change business logic

- Prefer small refactors

- Explain every decision

Task:

Provide refactoring recommendations.

"""

        return self.llm.invoke(prompt)

Notice what’s missing:

• No autonomy

• No looping

• No “thinking forever”

Agents serve engineering judgment, not curiosity.

Step 6: LLMs Are a Component, Not the System

The LLM is just one part of the control plane.

It should:

• Receive structured input

• Operate under explicit rules

• Return bounded output

Your system should never depend on:

• Memory inside the model

• Chat history

• Implicit behavior

Everything important lives outside the model.

Step 7: Decisions, Not Actions (At First)

For early implementations, the safest output is advice, not mutation.

Examples:

• PR comments

• Architecture reports

• Lint summaries

• Refactor recommendations

This avoids:

• Enterprise permissions

• Security reviews

• Irreversible changes

And it still delivers real value.

Step 8: Why This Matters Before Enterprise Control Planes Arrive

Enterprise platforms will eventually give you:

• Central orchestration

• GUIs

• Approval workflows

• Governance layers

But those platforms will assume you already understand:

• Event-driven systems

• Policy-based routing

• Agent reasoning flows

• Control-plane separation

This approach gives you that understanding now.

What You Gain as an Architect

By building (or even just understanding) this model, you gain:

• Control-plane literacy

• Better pipeline design

• Cleaner automation boundaries

• Stronger conversations with platform teams

• Real leverage inside your own repos

You’re no longer waiting for tools.

You’re reasoning at the same level they are.

Final Thought

Control planes are not products.

They’re patterns.

If you can:

• Receive events

• Reason over context

• Make explicit decisions

You’re already building one.