Parth G

Posted on Jan 16

Best Practices for Coding With AI Agents (Without Losing Control)

#ai #programming #tooling #productivity

If you’ve tried using a coding agent and thought:

“This is impressive… but it keeps doing the wrong thing.”
“It changed 20 files and now I’m scared to merge.”
“It almost works, but I don’t know what it actually did.”

You’re not alone.

Coding agents (like Cursor’s Agent) are genuinely changing how software gets built: they can work for long stretches, perform multi-file changes, and even iterate until tests pass. But you don’t get great results by “just prompting harder.” You get great results by working with agents using the right patterns.

In this guide, you’ll learn practical, beginner-friendly best practices to:

✅ Give agents the right kind of instructions
✅ Keep changes safe and reviewable
✅ Avoid context confusion
✅ Use tests, linting, and plans as “guardrails”
✅ Build a workflow that scales from small tasks to big refactors

What “coding with agents” actually means (quick mental model)

A coding agent is more than autocomplete.

Unlike a normal chat assistant, an agent can:

read and search your codebase
edit multiple files
run terminal commands
iterate until it reaches a goal (like passing tests)

To work well, an agent needs 3 things aligned:

Cursor calls this the agent harness—the system that orchestrates how the agent receives context and uses tools.

Why you should care:
Different models behave differently, and good “harness + workflow” matters more than fancy prompts.

1) Start with a plan (seriously… this changes everything)

The #1 mistake beginners make with agents is this:

“Here’s the feature. Now code it.”

That often leads to:

messy commits
half-correct solutions
surprise changes in unrelated areas

Cursor recommends starting with planning first, because it forces clarity and gives the agent a target to work toward.

✅ What a good agent plan should include

A solid plan is basically:

What files will be touched?
What new behavior are we adding?
How will we verify correctness?
What should NOT change?

Cursor’s Plan Mode (Shift + Tab) explicitly encourages this workflow: the agent explores your codebase, asks clarifying questions, and writes a plan you can approve before implementation.

Example: Prompt the agent for a plan (not code)

You are a coding agent.

Goal: Add "Forgot Password" flow.

Before coding:
1) Find the existing auth flow files.
2) Propose a step-by-step plan with file paths.
3) List the tests we should add or update.
4) WAIT for my approval before making edits.

Why this works:
You’re reducing randomness. The agent knows your rules: plan first, then build.

2) Give the agent “verifiable goals” (tests > vibes)

Agents are fast. That’s both a gift and a risk.

Cursor explicitly recommends giving agents verifiable goals like:

tests
typed languages
linters Because without a verification signal, the agent often “stops” when output looks right… not when it’s actually correct.

✅ What “verifiable goal” means in practice

Instead of:

❌ “Implement the feature and make sure it works.”

Say:

✅ “Implement the feature AND make sure npm test passes.”

✅ “Don’t finish until TypeScript typecheck succeeds.”

✅ “Fix lint errors until the linter is clean.”

Example: Add a verification loop prompt

Implement the change, then:
1) Run `npm test`
2) If any tests fail, fix the issue
3) Repeat until all tests pass
4) Summarize what changed and why

This matches Cursor’s recommendation: agents perform best when they can iterate toward a clear target (like tests).

3) Don’t dump context—let the agent find it

A common beginner move:

“Here are 9 files, 3 screenshots, and my entire repo history. Now do the task.”

That can actually make things worse because too much context adds noise.

Cursor recommends a simpler approach:

If you know the exact file: mention it.
If you don’t: let the agent search.

✅ Better prompting pattern

Where is the tenant onboarding logic implemented?
Please locate the relevant files and explain the flow.

This works because the agent can use codebase search tools like grep/semantic search to locate the correct areas.

4) Know when to continue vs start a fresh conversation

This is a BIG one.

Long agent chats can degrade over time because:

the context grows
noise accumulates
the agent loses focus

Cursor recommends starting a new conversation when:

✅ You’re switching to a new feature
✅ The agent seems confused or repeats mistakes
✅ You finished a clean unit of work

But continue the same thread if:

✅ You’re iterating on the same feature
✅ You’re debugging something it just built

Quick rule of thumb

If you’re thinking:

“Wait… why are we talking about this now?”

Start fresh.

5) If the agent builds the wrong thing, go back to the plan

Here’s a trap:

You keep prompting the agent to “fix” a wrong solution, and the codebase slowly turns into spaghetti.

Cursor suggests a cleaner method:

✅ Revert changes
✅ Improve the plan
✅ Run again

That is often faster and produces cleaner output than trying to patch a messy direction mid-flight.

6) Create lightweight “rules” so the agent stops making repeat mistakes

If an agent keeps messing up the same things (folder structure, formatting, conventions), don’t keep repeating yourself.

Cursor supports Rules as persistent project instructions (stored as markdown files, like in .cursor/rules/).

What rules should contain (keep them short)

Rules work best for:

commands to run
preferred file patterns
style expectations
where to put new code

Example: A simple rule file

# Project Rules

## Commands
- `npm run test`
- `npm run lint`
- `npm run typecheck`

## Code style
- Prefer named exports
- Keep components small and reusable

## Workflow
- Run typecheck after big changes
- Do not modify unrelated files

Tip: Cursor recommends keeping rules focused and avoiding huge style guides (use linters instead).

7) Use “skills” for repeat workflows (like PR creation or issue fixing)

Rules are always-on.

But Cursor also supports Agent Skills, which are dynamic workflows the agent can load when needed. Skills can include commands, hooks, and domain workflows.

Think of this as:

✅ “One prompt → full workflow handled”

Example workflows Cursor mentions:

generating tests
updating dependencies
running review commands

8) Review AI-generated code like it’s “confident but junior”

This is the mindset shift that saves you.

Cursor highlights that AI-generated code can look right while being subtly wrong—so careful review matters even more when the agent is fast.

✅ Your review checklist (simple but powerful)

Before you merge:

Did it change only what it should?
Did it add tests?
Did it follow existing patterns?
Did it introduce unnecessary abstraction?
Did it break security rules?
Did it touch config/build files unexpectedly?

Rule:
If the diff is too big to understand → it’s too big to merge.

Split it into smaller tasks.

9) Use agent-friendly workflows (TDD, debugging, codebase learning)

Cursor lists several workflows where agents shine.

Here are three you can start using today:

A) Test-driven development (TDD)

A safe way to use agents is:

Ask agent to write tests
Confirm tests fail
Ask agent to implement
Iterate until tests pass

Example prompt:

We are doing TDD.

1) Write tests for the password reset flow.
2) Run the tests and confirm they fail.
3) Do NOT implement the feature yet.

B) Codebase understanding

Agents are great for onboarding.

Try questions like:

“How does logging work here?”
“Where is the auth flow handled?”
“Why do we call X instead of Y?”

C) Debugging tricky issues

Cursor also supports a Debug Mode approach (hypotheses → instrumentation → evidence → fix).

Even without special tooling, you can prompt like this:

Bug: checkout fails sometimes.

1) List 3 possible causes.
2) Add logs to prove/disprove each.
3) Tell me how to reproduce.
4) Fix based on evidence, not guesses.

10) Run agents in parallel when the problem is hard

This is a pro move.

Cursor mentions that running multiple agents/models on the same task and picking the best output can improve results—especially on harder problems.

This is useful for:

refactor approaches
architecture decisions
tricky edge cases

Think of it like:

“Get 3 candidate solutions, then choose the best.”

Common mistakes (and how to avoid them)

Here’s a quick table you can keep near your desk:

A simple “agent workflow” you can copy-paste

Here’s a safe, repeatable workflow prompt you can use in Cursor (or any agent tool):

You are a coding agent.

Task: <describe feature>

Rules:
- Keep changes minimal
- Do not modify unrelated files
- Follow existing patterns in the repo

Process:
1) Find relevant files and explain what you found
2) Create a step-by-step plan with file paths
3) WAIT for my approval
4) Implement in small commits
5) Run tests + lint
6) Summarize what changed and why

This combines the best practices Cursor recommends:
planning, context discipline, verification, and reviewability.