It’s Feb. 2026, and I wanted to write my first blog post of the year, titled “How to Use AI Coding Agents More Effectively”. This year, I set several personal goals. One of which is to learn more about AI (coding) agents. So far, I have been playing with several AI tools, such as Claude Code and OpenCode.
I’m not gonna lie! I enjoyed using these AI tools, and I have been “vibe-coding ” ideas or projects. In fact, I started writing code long before AI-assisted developer tools were available. If I recall, I published my first open-source project on SourceForge in 2013 while learning JavaScript. So, I have been around, and I know what it’s like to write code with/without AI tools. I can definitely say that AI Agents give you a big boost in productivity. However, these AI agents are considered a “tool” to use for solving problems. Some have already argued that programming languages are tools as well. Regardless, it’s important to use a tool effectively to save time and get more from it.
That said, in this post, I would like to give you two tips on how to better leverage AI Coding Agents to solve your problems or vibe-code projects.
1- Use plan mode: AI Coding Agents, like OpenCode, have two modes of operation. They are called “plan” and “build”. Most of us usually want to give a one-paragraph problem statement (short prompt) to build our project or solve a bug. However, this is not usually an effective way to work with AI Agents. In essence, a prompt is a set of instructions for an LLM to perform a desired task. Then, it’s essential to develop a plan that outlines our project’s requirements and constraints. Even for a small bug fix, one still needs to develop a small plan outlining what needs to change and which tests need to be performed to verify the fix. After developing the plan with the agent, I recommend saving it as a Markdown file. You can implement the plan using the same model or a different one.
2- Break down the plan into tasks: Once you have devised a plan with the help of the agent, you can break it into several small tasks. Depending on how complex your plan is, AI agents may not be able to implement it properly in one go. Even for us, human software engineers, we often prefer to break a (big) problem into several sub-problems. This way, we focus on solving one sub-problem (small task) at a time. Therefore, it becomes easier to solve the larger problem and helps us track our progress. Similarly, an AI agent can work on a small task at a time. Or several AI agents can work on multiple tasks independently, provided that the tasks do not depend on each other. Also, LLMs have a limited context window, so they are better suited to handling small tasks rather than implementing the entire plan. You can ask the AI Agent to break your plan into several small tasks/problems and determine dependencies among tasks. Finally, the agent can write the identified tasks to a Markdown file so they can be solved later by AI agent(s).
To wrap up, AI agents perform more effectively when there is a clear plan with defined tasks. Sure, to do the above, you may need to have multiple iterations with the AI agent, which doesn’t directly translate to code. However, this will pay off later once you launch AI agent(s) to solve the identified tasks, and they will operate more independently and require less guidance down the road.