AI Coding Agents Comparison: What Separates Them in Practice

Coding agents differ less in generating code than in understanding a codebase and handling being wrong. This guide covers what to compare, drawing on FISTA Solutions' AI enablement engineering practice.

What should the comparison cover?

Six dimensions that separate tools in real use.

Dimension	What to look for	Why it matters
Codebase comprehension	Uses existing utilities and conventions	The main quality differentiator
Change scope	Does what was asked, no more	Review cost
Test execution	Runs suite, fixes failures	Working versus plausible
Review integration	Normal pull request flow	Fits your process
Recovery	Handles being wrong sensibly	Long tasks depend on it
Context handling	Large repositories	Where tools diverge most

How do you test comprehension?

Ask for a change that should reuse something already in the codebase.

A tool with real comprehension finds the existing utility, follows the established pattern, and matches the error handling style. One without it writes a new implementation that works and duplicates logic.

Test on your actual repository, not a sample project. Large codebases with history and inconsistency are where tools diverge, and that is what you have. See AI code review checklist.

Why does change scope matter?

Because review cost scales with diff size, and review capacity is your constraint.

An agent that fixes the bug and also reformats three files, renames a variable, and updates an unrelated dependency has produced a change that takes far longer to review than the fix warranted.

Discipline here is a genuine differentiator and is easy to test: ask for something small and see what comes back.

What does test execution change?

Whether you receive working code or plausible code.

An agent that runs your test suite, sees failures, and iterates delivers changes that at least pass. One that generates and stops delivers changes whose correctness you discover during review or later.

Check whether it can run your actual suite, how it handles slow tests, and whether it distinguishes a genuine failure from a flaky one. See CI/CD for web apps.

How should it fit your process?

By producing a reviewable change through your normal flow.

A pull request with a clear description of what changed and why, reviewed by a person, merged through your pipeline. Tools that bypass this to be faster are optimising generation when review is the constraint.

Check whether the descriptions it writes are useful to a reviewer. A diff with a generic description costs the reviewer more than one with a clear statement of intent.

What does recovery look like?

Noticing it is wrong and changing approach rather than persisting.

On a longer task an agent will take a wrong turn. The useful behaviour is recognising that the tests still fail or the approach is not working, backing out, and trying differently.

The unhelpful behaviour is repeated variations of the same failing approach, which burns time and tokens. Test this by assigning something genuinely difficult. See agent trace analysis pipeline.

What about large repositories?

It is where tools diverge most, because a big codebase does not fit in any context.

Tools differ in how they search, what they choose to read, and whether they find the relevant code before changing anything. Some handle large repositories well; some effectively require you to point at the right files.

Test on your largest repository. Performance on a small sample project predicts very little. See why context beats prompting.

How do you run your own comparison?

Take five real tasks from your backlog — a bug fix, a small feature, a refactor, a test addition, and something ambiguous — and run each candidate on your actual repository.

Score the results on whether the change fits your conventions, whether the scope was disciplined, whether tests pass, and how long review took. That last number is the one that matters.

What does switching cost later?

Low. These tools sit alongside your existing workflow rather than inside your architecture, so changing is mostly a matter of habit and configuration.

Avoid embedding tool-specific configuration deeply into the repository, and the switch stays cheap.

What do people get wrong here?

Evaluating on sample projects. Measuring generation speed rather than review time. Bypassing review to capture the speed benefit. Ignoring scope discipline. And adding capacity without adding review capacity.

Does this change how teams should be structured?

It shifts effort toward review and specification, which is where the constraint now sits. Teams that add generation capacity without adding review capacity accumulate a problem they cannot see yet.

Protecting review time explicitly, and rebuilding the junior pathway through review apprenticeship, are the two structural responses that appear to work. See the new shape of engineering teams.

Which should you choose?

Compare on codebase comprehension, scope discipline, and test execution using real tasks in your real repository. Measure review time rather than generation speed, because review capacity is the constraint the tool is pressing against.

What should you do first?

Run one real backlog task through each candidate on your actual codebase and time the review. That number decides it.

How FISTA Solutions helps

FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: coding tools evaluated on real backlog tasks in the real repository with review time measured rather than generation speed, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.

To run this comparison against your own workload, message FISTA on WhatsApp, or read AI code review checklist.