People often say that coding agents are getting better very quickly, which is true. But I think many users still leave a lot of performance on the table. The main limitation is often not the model itself. It is the way people work with the model.

If you want a coding agent to do complex work well, there are three big levers: context, tools, and feedback.

Context

The first lever is context. Large language models are conditional models. Given a fixed model, the output depends heavily on the input. That sounds obvious, but many people still write prompts that are only three or four words long and then expect the model to behave perfectly. In practice, that usually means the agent has to guess what matters, what constraints exist, what tradeoffs you care about, and what “done” should look like.

I do the opposite. I prefer long prompts with complete information. I want the agent to know what I know: the goal, the constraints, the environment, the files that matter, the failure modes I am worried about, and the standard of quality I expect. A longer prompt is not optional if you want good performance. It is required.

A simple example is feature development. A lot of people will say something like, “build this app” or “add this feature,” and assume the model will figure out the rest. Sometimes it can. But usually the human already has a much clearer picture: what the feature should look like, what it should do, what constraints it has, which user groups it applies to, and which user groups it should not affect. If that knowledge stays in your head and never makes it into the prompt, then the model is solving a worse version of the task than the one you actually care about.

When that version fails, I do not think the blame belongs entirely to the coding agent. In many cases, the human created the failure by withholding the real problem statement.

So my rule is simple: tell the model what you know. If you have a design in mind, say it. If there are hard constraints, say them. If there are specific failure modes you are afraid of, say them. The more the task depends on judgment, the more complete the context needs to be.

Tools

The second lever is tools. A strong coding agent should not just generate code. It should be able to inspect the repository, read logs, search for the right files, run tests, inspect traces, query systems, and use external tooling when necessary. That is where tool integrations, MCP servers, and reusable skills matter. They turn the model from a text generator into a worker that can move through a real workflow.

This matters because many software tasks are not blocked by code generation. They are blocked by information access. The agent needs to find the correct data, understand the current state of the system, choose the right pipeline, and use the correct tools in the right order. If you do not give it a path to those capabilities, it will stay weak no matter how smart the base model is.

The first tool I would emphasize is a good logging system. Logging helps both humans and agents understand what is going wrong. If the system writes useful logs to a file or to a predictable location, then the coding agent can read those logs, extract the important signals, and debug itself. A simple Python logging setup is already enough to make a big difference. The point is not sophistication. The point is visibility.

The second tool is visualization. This is often more valuable for humans than for the model itself. Agents are already very good at generating HTML and simple web pages. That means you can ask the model to build a visualization page for results, settings, metrics, or workflow states instead of forcing yourself to understand everything through raw CLI output. A visual dashboard is often the fastest way to understand whether the system is healthy.

The third tool principle is just as important: do not use an LLM for work that should be mechanical. If a task can be handled by deterministic tooling, use deterministic tooling. Unit tests, make targets, repeatable scripts, and validation checks should be owned by mechanical systems whenever possible. Then the model can use those systems as tools instead of trying to imitate them with language. That keeps the agent focused on understanding the target, changing the code, and updating the toolchain only when needed.

Feedback

The third lever is feedback. The most powerful agentic workflows are closed loop. The agent understands the task, makes a plan, edits code, runs something, observes the result, and then decides what to do next. A lot of failures happen at the observation step. The model can write code and execute code, but it cannot fully recover because it cannot see what actually went wrong.

That is why visibility is so important. If the agent can read logs, inspect traces, open the rendered output, or look at the page the same way a user would, then it can often fix the problem by itself. If it cannot see the failure directly, then a human has to keep translating the failure back into text. That slows everything down and breaks the loop.

This is also why I often prefer low-interruption workflows. For coding agents, I usually want the model to move with as little unnecessary approval friction as possible, especially when the changes are local and reversible. The goal is not to remove human judgment. The goal is to remove low-value interruption. I would rather spend my time designing constraints, building good tools, and reviewing meaningful changes than repeatedly approving mechanical actions that I already know are safe.

Browser visibility is a good example. If a coding agent can use browser tooling such as Chrome DevTools MCP, it gains direct access to the final product surface. Instead of being told “the button looks wrong” or “the flow is broken after step three,” it can see the DOM, inspect the network activity, read the browser console, click through the page, and observe the actual result.

That changes the workflow completely. If you already gave the model a clear design and a clear target, it can write the frontend and backend, launch the app, open the page in the browser, test the flow end to end, observe what is broken, and then change the code based on what it saw. It can repeat that loop until the product behaves correctly. At that point, the human is no longer acting as a low-level sub-agent who manually tests everything and writes summaries back to the model.

I also think it helps when the human can observe the same loop. A headful browser is useful for that. You can watch what the model is doing while it debugs. If you want to preserve the process, you can even record it with a tool like FFmpeg and review the session later. That is not required for every task, but it is a useful way to understand how the agent is making decisions when the workflow gets complex.

The same logic applies to logs and traces. If the agent can see the failing command, the stack trace, the structured logs, and the previous steps that led there, then debugging becomes much more mechanical. The system can often iterate on its own until it reaches a valid fix, then open a PR for a human to review. In that workflow, the human is still important, but mainly as a gate at the end rather than as a translator in the middle.

The Practical Point

I think many users underperform with coding agents because they underestimate all three levers at once. They write prompts that are too short, they give the model too few tools, and they leave it blind to the consequences of its own actions.

If you want the best performance from a coding agent, do not just ask it for code. Give it full context. Give it the right tools. Give it direct visibility into the outcome. That is how you move from a model that can answer questions to an agent that can actually finish work.