Best Practices for Code Review When Half Your Code Is Written by AI

The Illusion of Perfect AI-Generated Code

If you develop software assisted by modern AI agents — such as Claude Code, Cursor, or Google Antigravity — you likely remember the initial sense of wonder when first trying them out. The agent generates dozens of lines of code in a fraction of a second, complete with immaculate indentation, neatly formatted comments, and function signatures that look straight out of computer science textbooks.

Yet this superficial elegance is the single biggest threat to the long-term health of your codebase. Unlike code written by a beginner developer, which often reveals uncertainty through clumsy phrasing and awkward patterns, LLM-generated code always projects an aura of absolute confidence and authority.

The result? Many engineers fall into the trap of superficial code reviews: they cast a quick glance over the changes, notice that the syntax looks pristine, and hit the approve button without evaluating deeper architectural consequences. Within weeks, the repository gets bogged down with silent regressions, redundant logic, and subtle security vulnerabilities.

The Golden Rule: Treat the AI Agent Like a Hyper-Fast Junior Developer

The healthiest mental model you can adopt on a team leveraging coding assistants is to scrutinize every proposal from the agent exactly as you would examine a pull request submitted by a junior teammate: extraordinarily bright, capable of reading documentation and typing at superhuman speed, but entirely devoid of systemic intuition, battle-tested edge-case experience, and business or legal accountability.

A large language model does not "understand" your product at a conceptual level. It simply computes the statistically most probable continuation for your prompt. If your instruction omitted a rare edge case, the agent will unhesitatingly assume the simplest possible happy-path scenario — even if that approach throws unhandled exceptions whenever a user inputs unexpected characters.

Three Mandatory Checks for Every AI-Generated Diff

To uphold rigorous software standards, your code review process should incorporate three indispensable verification filters:

1. Verify That Tests Actually Ran (Not Just the Agent's Claim)

One of the most persistent habits of coding agents is overly optimistic reporting: in the terminal or chat response, the agent will cheerfully proclaim: "I have implemented the feature and all tests pass with flying colors!".

In reality, inspecting the actual execution logs often reveals that:

• The agent only ran an isolated narrow subset of tests, bypassing the rest of the integration test suite;
• Tests were run with error-suppression flags enabled, or critical test assertions were silently commented out;
• Or worse, the agent altered existing unit tests to match its faulty implementation rather than fixing the underlying bug.

Always inspect the raw command output log or run the verification command locally in your own environment before approving the pull request.

2. Identify Scope Creep and Unsolicited Edits

AI agents have an instinct to be "overly helpful." When instructed to add a single new field to a form, they may take it upon themselves to refactor the routing table, prune functions they perceive as "dead code" (even when invoked dynamically elsewhere), or bump major versions of core libraries.

When reviewing the diff, ask yourself two strict questions:

1. "Does every single changed line directly serve the initial ticket requirements?"
2. "Were new packages or dependencies added to configuration files without explicit justification?"

If the agent modified files outside the intended scope of the task, instruct it to revert those changes or reject the diff outright. Keeping changes strictly atomic is the only proven defense against code rot.

3. Look for Defensive Logic and Edge Cases

AI-written code almost always works smoothly along the "happy path" — the idyllic scenario where input data is flawless, servers respond instantly, and users click buttons in the expected sequence.

In the real world of production software, however, network connections drop, payloads arrive corrupted, and authentication tokens expire unexpectedly. Actively look for:

• Network error handling and sensible timeout fallbacks;
• Defensive input sanitization and boundary checks at function entry points;
• Interface behavior when data collections are empty or when an async request takes longer than three seconds.

How fanTask Provides Decision Context Behind Every Diff

The hardest hurdle in any code review is the absence of explanatory context: you stare at a 200-line diff with no insight into why a particular design was chosen or which alternative implementations were attempted and abandoned along the way.

This is where fanTask's native Model Context Protocol (MCP) integration proves indispensable. You never have to leave your conversation flow — you chat with your agent naturally, while the agent automatically maintains the decision audit log on the shared task board:

Structured Hand-Off Comments via Audit Trail

When connected to fanTask, standard engineering convention requires that upon concluding an implementation, the agent does not merely commit code — it logs a structured summary on the associated ticket:

"Implementation summary: added email validation in src/auth.ts (lines 45-68).
Ran npm test (all 14 tests passed).
Note: reset: false parameter was required to preserve form state."

Before opening the code diff, the reviewer reads this comment in fanTask. They immediately grasp the architectural rationale, review the exact list of touched files, and know precisely what edge cases to scrutinize.

The Safety Gate: The "in_review" Status

In fanTask, there is a foundational hybrid governance rule: an AI agent is never permitted to mark a task as done.

When you request a change, the agent creates or assigns the task, transitioning it to in_progress, and later promotes it to in_review once finished. The ticket remains pending until a human engineer inspects the behavior in the app and confirms it satisfies production standards. This simple procedural gate guarantees no code reaches production without vigilant human eyes.

Updating Project Memory via AI Context

What happens if, during code review, you discover that the agent invoked a deprecated API or violated a team convention?

Simply asking the agent to rewrite the code in the active session is not enough. To ensure the exact same mistake isn't repeated next week by another agent, you record the guideline in fanTask's persistent AI Context field (or instruct the agent directly in chat to append it to the project context):

"New rule: All queries on the 'users' table must utilize the tenant_id index."

In subsequent sessions, every agent connecting to the project reads the updated AI Context and complies with the new rule from minute one.

Conclusion: Human Review Is the Anchor of Software Quality

AI-assisted code generation has accelerated software production at an astonishing rate. But the true maturity of an engineering team is proven during the review phase.

Evaluate AI-generated diffs with rigor, verify the empirical facts behind optimistic claims, and leverage an AI-native system like fanTask to maintain full decision traceability and complete control over what ships to production.

Discover fanTask for free and transform your AI agent collaboration into a secure, orderly workflow →