How I Use Cursor for Agentic Operations

I am an operator. Most of my work is project-shaped: moving pieces, ambiguous asks, a mix of people and artifacts to coordinate, and a real outcome that has to exist at the end.

Chatbots have been deeply impactful for that work. They help me think faster, draft faster, and move through ambiguity with a lot less friction. But I have been learning that there is a real difference between working with a chatbot (like Claude or ChatGPT) and working with a true agent-oriented approach. Ops work is project-oriented, and that is what makes an agentic workspace like Cursor so powerful.

Cursor is an AI-native IDE, a coding tool, used primarily for software development. If (unlike me) you don’t spend your days surrounded by engineers, ann “IDE” is essentially the editor and workspace where files live, akin to a Google Drive for local files.

But while chat tools can give me a response, and even generate meaningful objects. An agentic workspace persists artifacts for reuse: the plan, the source material, the review notes, the tests, the handoffs, and the run history.

That difference becomes very obvious once the work has real surface area. When a project needs research, writing, review, revision, validation, and packaging all moving at the same time, I do not just need a smart model. I need a contained space for the work to live in.

The clearest example I have is an internal hackathon project at Ascend. We had two years of agentic data engineering material spread across labs, blog posts, docs, and internal notes. The knowledge was there. The path wasn’t. I needed to turn that sprawl into a real certification: three courses, 21 modules, and a sequence that could take someone from foundations to production operations without feeling like they had been dropped into the deep end. In 48 hours, agnets researched, wrote, reviewed, tested, and deployed the project end-to-end.

That kind of scope and velcosity is the difference between chatbot help and agent-oriented work. It isn’t simply having AI spit out an unverified, messy draft. It’s collaborating with agents (like collaborating a team) to run a project.

To be clear, I still love and use other AI tools. They matter a lot to my work. But Cursor is where I go when the work has enough moving pieces that it needs agents, not just an answer.

Here are the lessons I’ve learned as I’ve been working with agentics in Cursor and how it works in practice.

Lesson 1: Start by building the harness

Before I ask an agent for output, I build what technical folks call the agentic harness.

Basically, it’s the context, tools, rules, and file structure around the agent. The agent is only one part of the system. The harness is what keeps the work from drifting.

On the certification project, that meant shared source material, shared quality standards, a defined plan, reusable instructions for reviewers, and clear places for work-in-progress versus final outputs. It also meant deciding what the agents could touch, what had to stay human-reviewed, and what evidence counted as good enough.

If I skip this step, I usually get the same failure mode: polished output that is strangely unhelpful. It sounds right. It is not ready to use.

A simple harness is enough to start. You do not need a giant framework. You need a place for the work to live.

1. Sample folder structure

my-project/
├── README.md
├── context/
│   ├── brief.md
│   └── style-guide.md
├── work/
│   ├── plan.md
│   ├── drafts/
│   ├── reviews/
│   └── runs/
├── .agents/
│   └── skills/
│       └── reviewer.md
└── outputs/
├── next-steps.md
├── decisions.md
├── AGENTS.md

What lives where:

A key thing to call out that will MAKE or BREAK your agentic output is Skills. [Note to agent: let’s beef this up, what are skills, how do people use them, what are some examples from this project. i think we could actually include my skill for creating this file scructure when I start a new project?]

Lesson 2: Use planning mode to decide what “done” means

In Cursor, planning mode is the read-only mode where I work out the job before I let agents start changing files.

I use it as an execution contract.

For the certification build, the planning pass covered the curriculum structure, module outline, subagent roles, prompts for each role, the review process, production-readiness rules, and even how certification would be granted once the work shipped. That planning artifact mattered because it forced me to answer the questions early, while changing direction was still cheap.

Before I leave planning mode, I want the plan to answer these things:

  1. What are we making, exactly?
  2. What files or deliverables should exist when we are done?
  3. What makes something ready for human review?
  4. What review or validation has to happen before that?
  5. What artifacts do I need to save so the next pass has context?
  6. what is the process agents should execute to deliver the output?

That is the part I think operators often underrate. The best work do not come from writing a more complex prompt. Effective agentic output comes from deciding, and communicating, in advance, how the work will move.

Lesson 3: Pilot one test case before you scale

I did not start by fanning 21 modules out to a swarm of agents.

I piloted one module first.

That gave me a controlled place to tune the style guide, the reusable skills, the prompts, and the review bar. It also surfaced the predictable problems early. Some instructions were too loose. Some reviewers were not looking for the right problems. Some drafts sounded competent but thin. I would rather learn that on one module than on twenty-one.

The shared style guide did a lot of work here. Once the agents were all reading the same standards, the reviews got sharper and the drafts got more consistent.

Once I was satisfied with the output I was getting from the first module, then I spun up to parallelize the rest.

This step saved me a lot of cleanup. Scaling bad instructions is a very efficient way to manufacture more bad output.

Lesson 4: Multi-agent & Adversarial-agent models reduce manual review cycles

The certification workflow was not one giant prompt. It was a series of bounded work with explicit handoffs.

For each module, the flow looked like this:

  1. A research agent gathered and organized the relevant source material.
  2. A writing agent turned that material into the module draft.
  3. Eight reviewer agents checked the draft from different angles.
  4. A synthesis agent read those review reports, removed duplicated issues, prioritized the fixes, and issued a verdict on if the module was ready for human review.
  5. A revision agent applied the changes.
  6. The review and revision loop ran again until no hard blockers remained.

The reviewer mix matters. If everyone is asked to do a generic review, you get eight versions of the same vague feedback. These reviewers had different jobs and different lenses. Some were reading like senior data engineers. Some were reading like instructional designers. Some were looking for legal or clarity issues. They were not talking to each other. That independence was part of the value.

A single module could go through 56 to 112 agent review passes before it ever came to me. That sounds excessive until you compare it to the alternative, which is me manually catching every weak instruction, every fuzzy explanation, every missing assumption, and every place a lab step quietly stops matching reality.

ADE 301 is the best example of why the structure mattered. Across six modules, I ran eight reviewer agents per module in a single fan-out, meaning one message that kicked off all 48 reviewers at once. They wrote their reports to files. The synthesis agent polled for those files, deduplicated the findings, and issued verdicts like NEEDS REVISION or ready for human review.

The important detail is that the synthesis agent owned the readiness call, not me. That constraint changed my role in a good way. I was not reading six half-baked modules and trying to mentally merge 48 review perspectives on the fly. I was stepping in after the draft had already been pushed through a serious quality gate.

That is the kind of speed I care about. Faster, yes. But also more thoroughly vetted before the work lands on my desk.

Lesson 5: Use MCP when review needs to touch the real system

MCP stands for Model Context Protocol. The plain version is simpler than the name: it lets an agent use connected tools and systems from inside the workspace instead of only commenting on local files.

You do not need MCP to get started. I would not make it your first step. But once the work has to interact with a real system, it takes AI from a helpful tool to a more less manual one.

On the certification project, I used MCP-connected agents to work with Otto inside the Ascend platform itself. Instead of only reading the lab instructions and saying, “this seems fine,” the agents could test the prompts in the actual environment the learner would use.

That turned up a very real problem. Some of the lab prompts did not match the data and workflow the workspace actually contained. A text-only review would not reliably catch that. The in-platform validation did. The agents generated replacement prompts, and those went back into the module revision cycle.

That is a very different kind of review. It is closer to validation than commentary.

In other parts of my ops work, I use MCP servers in Cursor so agents can draft (but not send) emails directly in my Google Workspace, issue certifications for Bootcamp participants on Certifier, do deep research on prospects in Hubspot, and more.

Connecting your agents to tools dramatically increases what you can develer and the quality of that output. Just ensure that you intentionally set limits to the types of actions agents can take within those tools.

Lesson 6: Save the artifacts (but even if you don’t, agents can help with that)

One of the more annoying moments in the project came entirely from user error. Whoops. I accidentally deleted every module, and all the work of the 301 course of the certification without the ability to recover it.

That could have been a full disaster. It was not.

Because the workflow wrote artifacts to files at every stage, we could reconstruct the work from transcripts, run artifacts, review reports, and synthesis outputs. The system had a memory. The project did not live only inside a chat pane.

That changed how I think about agent outputs.

Review reports are not disposable. Run notes are not disposable. Synthesis verdicts are not disposable. If the work matters, those artifacts are part of the project. They help with recovery, but they also make the process auditable. You can see what changed, why it changed, and which issues kept recurring.

For operators, that matters a lot. I do not just want something that looks impressive when it works. I want something I can recover, inspect, and improve when it does not.

Where this helped me most

Yes, it was fast. With 48 hours, a small host of agents, and couple hundred dollars of tokens, I shipped a full production-ready three-course, 21-module certification program. What, in the past, would have taken a team of 4 or 5 developers weeks to deliver, a single non-engineer could deploy in 2 days.

But what I remember more clearly is how little editing was waiting for me at the end. The work had already been pushed through real scrutiny before it landed back on my desk. That meant better drafts, fewer hidden gaps, and a lot less manual cleanup.

The operator’s job does not disappear in this kind of workflow. I still own the plan. I still own the quality bar. I still own the call on whether something is accurate enough, clear enough, and worth shipping.

What I do not need to own anymore is every intermediate pass. If the system is reading the real source material, saving its review trail, and sending weak drafts back for another round, my job shifts toward judgment. That is where I am most useful. And if every pass still has to come through me, then I have not built much of a system yet.