AI Agents Are Just CI Pipelines With an LLM Plugged In

In this post, I’ll show you how to think about AI agents through the infrastructure patterns you already use. Think about your CI runner. It spins up an environment. Runs some steps. Reads files. Runs tests. Captures output. Decides what to do next. Knows when to stop.

Now swap out the hardcoded logic for an LLM. That’s it. That’s an AI agent in simpler terms. The fancy demos want you to think it’s magic. Some brand new thing you need to learn from scratch. It’s not. When you take away the hype, an agent is just a controlled automation loop. The LLM handles the reasoning and everything else is infrastructure you’ve built a hundred times.

Here’s what matters, the agent itself isn’t the hard part but The harness is, the execution environment, tooling, guardrails, and observability. It’s all the important stuff that makes automation work in production.

DevOps engineers have been building harnesses forever. CI runners. Deployment pipelines. Infrastructure automation. The patterns are the same. The skills transfer directly.

So if you’re wondering whether AI agents are worth learning, here’s the short answer. You’re already halfway there.

What an Agent Actually Looks Like

Let’s forget the marketing hype around AI agents and understand from a DevOps engineer’s point of view, what an agent actually looks like. An AI agent has six parts.

1. An LLM: Now LLM is the most important part of an agent as this acts as a brain. It reads context and decides what to do next. It doesn’t touch anything directly.

2. A workspace: Think of it as a sandboxed environment. A cloned repo. A container. A temp directory. Same as any CI job.

3. A set of tools: These are the actions it can request. Read a file. Run a command. Call an API. Query logs. The agent doesn’t run these itself. It asks for them.

4. A control loop: This is the core pattern. Observe the current state. Decide an action. Execute it. Check the result. Keep going until you’re done.

5. Policies and limits: Timeouts. Permission boundaries. Rate limits. Cost caps. Without these, agents can spin forever or do things they shouldn’t.

6. A termination condition: The agent needs to know when to stop. Task complete. Error threshold hit. Human review needed. Something has to end the loop.

Now none of this is new as you’ve built systems with all these components. The only difference is the LLM sitting in the decision seat.

The Harness Does the Heavy Lifting

Everyone focuses on the LLM. They miss the important part. The harness is what makes an agent actually work.

The harness is everything around the model. It spins up the environment. Exposes tools. Executes commands on the agent’s behalf. Captures logs and diffs. Enforces limits. Decides when the loop should stop.

Sound familiar? It should. This is what CI runners do.

GitHub Actions. GitLab runners. Jenkins agents. They all follow the same pattern. Spin up an isolated environment. Run steps. Capture output. Handle success and failure. Clean up.

An agent harness does the exact same thing. The only twist is the steps aren’t hardcoded in YAML. They come from the LLM at runtime.

This is why DevOps engineers are perfect for this work. You already think about isolation, execution, logging, and cleanup. You already build systems that run untrusted code safely. Agent harnesses are the same problem with a new input source.

Tool Use Is the Safety Mechanism

Agents don’t touch systems directly. This matters. The LLM never runs a command itself. Never writes a file itself. It requests actions through tools.

The harness gets the request. Validates it. Executes it in a controlled way. Returns a structured result.

This is how you keep agents safe.

Say the agent wants to run a shell command. The harness can check it against an allowlist. Run it in a sandbox. Set a timeout. Capture stderr. The agent never gets raw shell access.

Same thing for file operations. The agent requests a file write. The harness checks the path. Validates the content. Writes the file and returns confirmation.

You control what tools exist. You control how they behave. You control what the agent can even ask for.

This is the same idea behind least privilege. The agent only gets access to what it needs. The harness enforces the boundary.

The Control Loop in Practice

The core of any agent is the control loop. It looks like this.

1. Observe: The agent reads the current state. Test output. Log files. Diffs. Error messages. Whatever context it needs.

2. Decide: The LLM looks at the state and picks an action. Run another test. Edit a file. Ask for more information. Give up.

3. Execute: The harness runs the requested action and returns the result.

4. Verify: The agent checks if the action worked. Did the test pass? Did the error go away? Is the task done?

5. Repeat: If the task isn’t complete, go back to observe.

This loop keeps running until a termination condition hits—success, failure, timeout, max iterations, or human intervention.

You’ve seen this before: build, test, fix, rebuild. CI pipelines do this, deployment rollbacks do this, and health check loops do this.

Agents just make the “decide” step dynamic instead of scripted, and here’s where they actually help in DevOps work.

CI failure analysis. When a test fails, the agent reads the logs, checks the diff, identifies the cause, and suggests a fix—maybe even applying it and rerunning the test.

Terraform drift detection. The agent compares actual state to declared state, flags the drift, and proposes a remediation plan while a human approves before anything changes.

Kubernetes manifest review. The agent checks YAML against best practices (missing resource limits, no liveness probes, exposed secrets) catching the stuff humans miss in review.

Cost anomaly investigation. When spending spikes, the agent queries cost explorer, correlates with recent deployments, and surfaces the likely cause, saving an hour of digging.

Incident log triage. Faced with pages of logs, the agent reads them, extracts the relevant lines, and summarizes what went wrong (not replacing the engineer, but getting them to the answer faster).

Notice the pattern: the agent assists and handles the tedious parts while the human stays in control of decisions that matter.

AI agents sound complicated with their new frameworks, new terminology, and new paradigms.

But look past the hype and you’ll see something familiar.

An agent is an automation loop where the LLM picks the next step, the harness executes it safely, tools provide controlled access to systems, and policies keep things bounded.

This is CI/CD architecture, infrastructure thinking, the stuff you already do.

When you read about agent frameworks or watch demos of coding assistants, you now have a lens to see the harness underneath, spot the control loop, and ask the right questions: what tools does it expose, what limits exist, and how does it handle failure?

You don’t need to become an ML engineer to understand agents—you just need to recognize the infrastructure patterns you’ve been using all along.

The LLM is the new part. Everything else is your domain.