I have been using AI agents Cline/Roo, Claude Code, and GitHub Copilot, and want to share what that experience has been like for me. With their help, I’ve been able to migrate a service domain data access layer from Entity Framework to Dapper, build an operational tool in Next.js that queries multiple databases and provides consolidated dashboards with search and drill down functionality, and experiment with a personal project using Python, SageMaker, and Postgres to process and transform large datasets. Each project had its own challenges, but what tied them together was how AI agents fundamentally changed the way I approached development. They didn’t just save me time, they also made me more ambitious, more experimental, and more confident in tackling things I might have avoided in the past.

How I Used the Tools

GitHub Copilot: My Inline Coding Partner

• Refactoring code: Making it more concise and elegant.
• Adding comments: Extracting common logic into reusable methods.
• Unit testing: Writing tests and fixing compilation errors.
• Explaining code: Providing method-level explanations with runnable examples that deepened my understanding.

What surprised me most was how much Copilot improved my flow. Instead of breaking concentration to look up syntax or boilerplate, I could stay in the zone and let Copilot handle the repetitive parts.

Claude Code: My Terminal Companion

• Wrote scripts: Quickly and cleanly.
• Executed commands: Ran them directly in the terminal.
• Error handling: Managed common errors gracefully, often suggesting fixes before I even thought of them.

This was a game changer for me. Instead of wrestling with obscure error messages, I had an AI that not only spotted the issue but also explained it in plain language and offered a solution.

Cline & Roo: My Architects

• Cline: Felt more informal where I could throw requirements at it, and it would just start working.
• Roo: More structured. Its “plan mode” (or architecture mode) generated detailed documents: implementation overviews, checklists, and summaries. Sometimes this felt like overkill for small tasks, but for larger ones, it was invaluable.

I especially liked using Roo for requirement briefings and brainstorming. It forced me to think through architecture and design decisions before diving into code. Cline was better when I just wanted to get moving quickly.

My Coding Workflow with AI

1. Provide context up front

   I now begin every project with a system prompt that sets the stage. It explains what the project is, the best practices I want followed, the languages and frameworks I’m using, and any constraints or non negotiables. Being explicit here prevents the AI from filling in gaps with its own assumptions.

2. Decompose tasks

   Instead of asking for everything at once, I break the work into smaller, targeted tasks. I use planning or architecture modes to shape the structure before any code is written. Narrowing the scope keeps the AI focused and makes the results more reliable.

3. Start with the skeleton

   The first step is always to create the basic application structure: frontend pages, backend APIs, and a simple data flow with static or mock data. This gives me an end to end path early, so I can see the shape of the project and catch missing pieces quickly.

4. Build module by module

   When I move into functional areas, I provide requirements for both backend and frontend together. For example, in a dashboard module I’ll specify the database schema, the queries to aggregate results and calculate metrics, the paging requirements for APIs, and the exact frontend layout I want to including panels, grids, buttons, and even subtle UI hints like a “go to top” button. With this level of detail, I’ve been able to create surprisingly complex UIs, such as grids with color changing cells and collapsible rows.

5. Keep it modular

   I mirror folder structures between frontend and backend. If I have a dashboard module, there’s a /dashboard folder in both places. This makes it easier for the AI to pick up context when I ask for changes and keeps boundaries clear.

6. Define relationships explicitly

   If a dashboard button should link to a product list page, I spell that out as a cross module requirement. Without this, the AI tends to invent its own connections, which can cause confusion later.

7. Keep context small

   I target specific areas of the application rather than asking for sweeping changes across the whole codebase. It’s faster, more accurate, and easier to refine. Once something works in one place, I can ask the AI to replicate the pattern elsewhere.

8. Review carefully

   AI sometimes takes shortcuts, like writing multiple database queries instead of a single join. When that happens, I correct one instance and then ask it to apply the fix everywhere. Careful review is non negotiable if I want quality results.

9. Test and verify

   I write unit tests, spot check code, and make sure best practices are followed. Testing isn’t just about quality it also helps me understand what the AI produced and why.

10. Refine and iterate

    Over time, I’ve learned how to get the best out of each agent. I keep notes on what works, what doesn’t, and the prompts that get the best results. Each project teaches me something new. What started as frustration has turned into a workflow that feels like a partnership. The agents don’t just save me time they push me to think more clearly, design more carefully, and build with more confidence.

How I Felt

Being able to build faster was a huge shift for me. I didn’t have to spend hours setting up the basics, instead the AI agent handled most of that, which freed me to focus on the interesting parts.
I also became more ambitious about what I could build. In the past, whenever I worked on full stack applications, my user interfaces were usually very plain. I never enjoyed spending time polishing layouts or making them responsive, so I’d settle for something functional. But with Cline and Roo, I ended up creating an interface that looked genuinely impressive and streamlined, responsive across devices, and far more polished than anything I would have attempted before.
Another big change was the sense of independence. I felt like I could complete projects on my own that I once assumed required a team. For example, in one Python project, the agent not only generated optimized, modular code but also gave me detailed instructions on setting up the local environment, deploying, and running it on SageMaker. That kind of support made me feel capable of handling the entire workflow solo.
And maybe the most important part: I had fun. After a long time, I found myself working late into the night and sometimes until 5am, not because I had to, but because I was genuinely excited by the results I was seeing. That sense of flow and momentum reminded me why I love building things in the first place.

Time saved (and why that’s not the point)

Yes, AI saved me 50–80% of the time. But the real win wasn’t the hours saved, it was getting things done.
In the past, I’d start personal projects aiming for 8–10 hours a week, finishing in a month. Life would get in the way, I’d skip a week, lose motivation, and the project would die.
With AI, I could finish the same project in 4–6 hours, meaning in just one or two sittings. That sense of completion and actually shipping something was the real breakthrough.

Weaknesses and Dangers

Code can become non modular and hard to maintain

AI can generate code quickly, but without guidance it often mixes responsibilities and creates tightly coupled components. How to avoid it: Define a clear folder and module structure up front (for example, a /dashboard folder in both frontend and backend). Always ask the AI to place new code inside the correct module, and periodically refactor to enforce boundaries so modules don’t bleed into each other.

AI struggles with large, monolithic codebases due to context limits

When the codebase grows too large, the AI loses track of details and dependencies. How to avoid it: Keep prompts scoped to a single module or feature instead of asking for sweeping changes across the entire app. Use short summaries or architecture notes to reestablish context when needed.

Agents sometimes “lie”, claiming tasks are done when they’re not

There were times when the AI confidently stated that a feature was implemented, but on inspection, parts were missing or incomplete. How to avoid it: Never take the output at face value. Review the code carefully, run it, and write tests to confirm functionality. If something looks suspiciously simple, dig deeper and ask the AI to explain its approach.

They can cheat by taking shortcuts or following happy paths

AI often prefers the simplest solution, which means it may skip over edge cases or error handling. How to avoid it: Explicitly ask for edge cases, error handling, and performance considerations. For example, specify “handle null values,” “add retry logic,” or “optimize for large datasets.” Push the AI beyond the happy path.

They sometimes invent code unrelated to the actual problem

Occasionally, the AI generates functions or structures that don’t belong, which can be confusing if unnoticed. How to avoid it: Make your requirements precise and stop the AI immediately if it drifts. Restating the scope and keeping the context small helps keep it on track.

The Future of AI in Software Development

AI agents are reshaping how we build software, and I see several key shifts ahead:

• Smaller teams: With AI amplifying individual productivity, small teams can achieve what once required large departments.
• From agents to fleets: The evolution will be from single AI assistants to coordinated groups of agents working together.
• Shift from coding to orchestration: Developers will spend less time writing every line of code and more time guiding, reviewing, and orchestrating AI agents.
• Rise of prompt driven architecture: Requirements, prompts, and context management will become first class citizens, much like APIs and design patterns are today.
• Continuous learning systems: AI agents will learn from the codebase itself, spotting patterns, suggesting refactors, and predicting bugs before they happen.
• Blurring of roles: Boundaries between frontend, backend, DevOps, and data engineering will soften as AI enables individuals to span multiple domains.
• Faster prototyping, faster failure: AI can spin up prototypes in hours, encouraging more experimentation and accelerating innovation cycles.
• Ethics and governance: Trust, transparency, and governance will be critical to ensure AI generated code is secure, maintainable, and aligned with business goals.

With AI agents, I can now build complete applications in days that would have taken months in the traditional workflow. I could never, ever go back to the old way of developing software.