How to achieve first-pass quality with AI coding agents

Why does AI-generated code slow down delivery?

AI coding agents produce code faster than any team can review it. Pull requests that used to be 300 lines are now 3,000, and the pace keeps climbing.

That volume looks like progress, but most of the apparent gain reappears further down the pipeline. A pull request that fails CI, bounces back in review, or breaks after merge shifts the cost downstream, where it becomes harder to trace. Whether AI-assisted development actually accelerates delivery depends on how quickly and reliably teams can verify what the agent produces.

What is first-pass quality in software development?

First-pass quality is code that meets your expectations on its first submission for review, without a second lap.

Concretely, code that clears the first pass:

• Works as intended against functional requirements.
• Holds up on maintainability, without excess complexity or duplication.
• Introduces no new security vulnerabilities.
• Carries the tests needed to prove and protect its behavior.

AI agents raise the stakes on this metric. When output volume climbs, every weak first pass multiplies into more review cycles, more failed builds, and more rework. The gap between agent output that becomes engineering capacity and agent output that becomes cleanup runs through first-pass quality.

How does poor AI code quality create rework for engineering teams?

A weak first pass rarely fails loudly. It leaks time across every team the code touches.

• AI agents generate code at volume, often with structural blind spots that compound across the pipeline.
• Engineers lose hours to review: reconstructing intent from a diff, context-switching back to work they thought was finished, rewriting instead of building.
• QA absorbs defects that should never have reached them, and sends work back.
• Platform engineering fields broken builds and unstable pipelines.
• Production inherits whatever slips through.

The signals are observable and worth tracking: rejected pull requests, failed builds, reopened work, production incidents. 

• LinearB's 2026 Software Engineering Benchmarks Report, drawing on 8.1 million pull requests across 4,800+ organizations, found that agent-generated PRs wait 4.6x longer before a reviewer picks them up.
• Faros AI's Productivity Paradox research, based on telemetry from over 10,000 developers across 1,255 teams, reports that AI-heavy teams merge 98% more PRs while PR review time climbs 91%.
• New Relic's 2026 State of AI Coding report, based on a survey of 200 US technology decision-makers, found 78% of organizations seeing production incidents rise and 86% seeing increased senior-engineer "firefighting" and emergency intervention.
• Independent research from Carnegie Mellon, studying 806 open-source projects that adopted Cursor, found a velocity spike that dissipated within two months while the added complexity persisted: +30% code analysis warnings and +41% cognitive complexity. Together, these studies show that increased AI-assisted throughput can coincide with review bottlenecks, production incidents, and persistent maintainability costs.

Why do AI coding agents produce low-quality code?

LLMs generate code that contains bugs and vulnerabilities, a pattern that holds even for the highest-scoring coding models on Sonar's LLM leaderboard. Weak inputs amplify that baseline into predictable, addressable failure modes:

• Incomplete context. The agent does not know your architecture, your patterns, or your boundaries.
• Unclear task boundaries. Without a defined scope, the agent guesses where the work starts and stops.
• Ambiguous requirements. Vague intent produces plausible code that solves the wrong problem.
• Missing constraints. Without explicit guardrails (which files to leave alone, which dependencies are off-limits, which security or compliance rules bind the change), the agent has no signal for what it must not do.
• Weak conventions. No enforced standards means every generation drifts differently.
• No feedback loop. The agent never sees the results of static analysis or prior review outcomes, so it repeats the same mistakes.

Off-by-1 Labs' F.L.A.W.E.D. study measured this directly. Across 6,080 patches for six recent CVEs, fix-success rates hit 65.0% under correct guidance versus 15.2% under incorrect guidance, and climbed to 76.3% at the richest end of correct, information-rich prompting. Input quality strongly affected patch success.

How do I improve AI coding agent inputs to get better code?

The cheapest defect to fix is the one caught at the point of generation, before the agent ever hands you a PR to review. Better inputs are the first lever, and they belong upstream of generation.

Give the agent the rules of the game before it starts:

• Architecture patterns so generated code respects your structure.
• Coding standards so output stays consistent across every run.
• Dependency rules so the agent stops introducing risk through third-party choices.
• Explicit constraints so it knows what it cannot touch.
• Defined completion criteria so "done" means done, not "compiles."

Do this before the agent writes a line, and it produces code that clears the first pass more often, because it finally understands the playing field.

How do I verify AI-generated code before it reaches code review?

Guidance improves the odds, but verification confirms the result. In the agentic era, code verification is mandatory, and it cannot wait until a human opens the pull request.

Move checks earlier and make them automatic:

• Run quality and security analysis and tests before human review, so reviewers spend their time on design and intent, not defects a machine should have caught.
• Enforce quality gates in CI so nothing broken advances by default.
• Route findings back into the agent workflow so the agent corrects issues at the source, before they reach a person.

The code verification methodology matters as much as its placement. Algorithmic, multilayered analysis catches the complex, cross-file mistakes that an LLM checking its own work misses. An agent cannot be its own reviewer; verification has to be independent of generation to be trustworthy. 

SonarQube provides that independent verification layer, applying the same standard to AI-generated and existing code alike inside GitHub, GitLab, Bitbucket, Azure DevOps and more, with a false-positive rate under 3.2%.

How do I set up a feedback loop to improve AI coding agent output?

A single verification pass improves one pull request. A feedback system improves every pull request that follows.

Turn each failure into an input for the next generation:

• Categorize recurring failure modes so you know what your agents get wrong most often.
• Update templates and guardrails to close those gaps at the source.
• Monitor quality trends to confirm first-pass quality is climbing, not eroding.

Issues surfaced in verification feed back into guidance, and each iteration starts with more accurate context than the last.

The Agent Centric Development Cycle

Better inputs, in-loop verification, and a feedback system fit together as a continuous cycle. Sonar's name for it is the Agent Centric Development Cycle (AC/DC): a Guide → Verify → Solve loop wrapped around every AI code generation event.

• Guide sets the context and constraints before the agent writes.
• Verify catches what the guide did not prevent, independently of the model that wrote the code.
• Solve routes each failure into a fix and back into the next iteration's guide, so the system compounds.

How do I roll out first-pass quality improvements for AI coding agents?

You do not fix first-pass quality across every workflow at once. You prove it on one, then extend.

A rollout that holds:

1. Start with a bounded task type: a well-understood, repeatable category of work.
2. Set a baseline. Measure current first-pass rates, rejected pull requests, and failed builds before you change anything.
3. Add high-signal checks that catch the failures that matter most for that task.
4. Iterate on guidance and guardrails as patterns emerge.
5. Extend to the next task type once the numbers move.

Each cycle produces evidence: measurable proof that first-pass quality is rising, and that agent activity is converting into capacity.

Why is first-pass quality the most important metric for AI coding agents?

What matters is how much agent output reaches production without triggering rework, incidents, or review debt. First-pass quality is the metric that measures it.

Guiding the agent before it writes, verifying its work before a human reviews it, and feeding every result back into the next run turns raw agent throughput into code teams can accept, merge, and ship at the same rate the agent produces it.

See how SonarQube verifies AI-generated code inside your existing workflow: request a demo.