Gemini CLI: how to review code when Google’s terminal agent searches the web, thinks out loud, and writes for your stack

2026-04-29 · 5 min read · ZenCode

Gemini CLI is Google’s open-source AI coding agent for the terminal. Like Claude Code and OpenAI Codex CLI, it runs in your shell, reads your codebase, executes commands, and writes or modifies files based on the tasks you describe. What distinguishes Gemini CLI is its architecture: it is backed by Gemini 2.5 Pro, a model with native Google Search grounding, built-in chain-of-thought reasoning that it surfaces to the user, and deep familiarity with Google Cloud infrastructure and APIs. These are genuine capabilities. They also produce three review traps that do not appear with other terminal agents.

Each trap follows from a feature that is genuinely useful: grounded search citations, visible reasoning, and GCP service familiarity. In each case, the feature provides a signal that feels like independent verification but is not. The code Gemini CLI writes is real code — the review question is what the signals around it are actually telling you.

The three Gemini CLI attention traps

1. Search-grounded authority

Gemini CLI can perform Google Search as part of its task execution. When solving an unfamiliar API integration, debugging an obscure error, or implementing a pattern it hasn’t seen in your codebase, it searches for supporting information and cites the results in its response. The citations are real — the agent found sources, and those sources informed the approach it took.

The trap is that “found a supporting source” and “applied it correctly to your specific situation” are two different things. A Stack Overflow answer that correctly solves a similar problem can be applied incorrectly to a different context. A documentation page that describes the right API method can still produce wrong code if the agent misread a parameter type, applied a version-specific example to a different version, or used a pattern designed for one authentication model in code that uses another. The citation shows the agent did research; it does not show the research was interpreted correctly.

This is a more persuasive form of authority transfer than an agent simply asserting a correct answer, because there is an external source you could in principle go verify. The problem is that most reviewers stop at “it found a source” rather than following through to “did it interpret that source correctly for this context.” The fix is to treat citations as pointers to check, not as endorsements. When Gemini CLI cites a source for a non-obvious implementation choice, look up the source yourself and verify that the application matches your specific context — version, auth model, error semantics, and all.

2. Thinking-trace trust

Gemini 2.5 Pro includes chain-of-thought reasoning, and Gemini CLI surfaces part of that reasoning in the terminal output. You see the agent working through the problem: identifying constraints, considering alternatives, settling on an approach. The reasoning is specific to your task, it references your actual code and requirements, and it reads as methodical deliberation.

This creates a review shortcut that feels justified: you have already seen the agent’s reasoning, so reviewing the output is just confirming it. The reasoning seemed sound, the approach seemed right, the code is probably fine. The problem is that visible reasoning is not independent verification of the code. The model generates the reasoning and the code together as part of the same inference pass. Reasoning that sounds plausible can precede code that contains errors — the errors in the code are often consistent with the reasoning, which is why the reasoning sounds plausible. A hallucinated API method will be reasoned about as if it exists; a misapplied pattern will be argued for coherently.

More specifically: the thinking trace is optimized to justify the conclusion, not to surface objections to it. A developer thinking through a problem generates alternative framings and considers why they might be wrong. An LLM thinking trace generates the path to the answer it is about to produce. These are structurally different cognitive processes, and conflating them causes reviewers to treat the trace as a peer review that has already happened. It has not. Review the code on its own merits, reading the thinking trace only as context after you have formed your own independent assessment of what the code does and whether it is correct.

3. GCP service familiarity collapse

Gemini CLI has extensive built-in knowledge of Google Cloud Platform services, APIs, and conventions. For teams working on GCP infrastructure — Cloud Functions, Cloud Run, BigQuery, Pub/Sub, Firebase, IAM, and the rest of the Google Cloud surface — this produces code that fits the familiar architecture with unusual precision. Service names are correct, client library patterns match the official documentation, resource naming follows GCP conventions. The code looks like it was written by someone who knows GCP well.

This familiarity creates a subtle form of authority transfer. When code fits neatly into your existing GCP architecture, the architectural fit reads as a quality signal. It should not. An IAM policy that uses the correct resource path format can still grant permissions that are broader than necessary. A Cloud Run service definition that uses the correct container configuration syntax can still have memory limits that will cause it to fail under production load. A BigQuery query that uses the correct table reference format can still be missing a partition filter that will scan the entire table. In each case, the architectural correctness is evidence of GCP familiarity, not evidence of operational correctness.

The fix is to evaluate GCP-specific code at two levels that must be separated: syntactic and operational. Syntactic correctness means the API calls, resource names, and configuration shapes are right. Operational correctness means the permissions, limits, costs, and failure modes are right. Gemini CLI is reliably strong at syntactic correctness because it was trained on extensive GCP documentation. Operational correctness requires knowledge of your specific production context that is not in its training data: what your actual traffic looks like, what your cost tolerance is, what failure modes you have seen before, and what your security posture requires. Do not let syntactic correctness substitute for operational review.

What Gemini CLI shares with other terminal agents — and what it does not

The broad shape of terminal agent review problems is consistent across tools. All of them — Claude Code, Goose, OpenAI Codex CLI, Cline — produce code that looks clean in the diff while the reasoning that generated it is no longer visible. The review problem is always some version of: the diff shows the output, not the process. Gemini CLI shares this with every other agent in the category.

The three traps above are specific to Gemini CLI’s architecture. The search-grounded authority trap requires an agent that can actually search — not all of them can, and those that can do not always surface citations as prominently. The thinking-trace trust trap requires a model that exposes chain-of-thought reasoning in the terminal interface — Gemini 2.5 Pro does this in a way that is more prominent than most. The GCP familiarity trap requires the deep GCP-specific training that comes from being a Google product — it has no equivalent in agents from other providers.

None of these traps disqualify Gemini CLI as a tool. For teams already on GCP, its familiarity with Google Cloud services is a genuine time-saver. Its ability to search the web for supporting information means it handles obscure integration problems that stump agents with no external access. Its chain-of-thought reasoning is useful context even if it is not a correctness guarantee. The review discipline is just the cost of using a capable tool: verify citations at source, form your own assessment before reading the thinking trace, and separate syntactic correctness from operational correctness on GCP code.

Related reading: Claude Code terminal agent review · OpenAI Codex CLI review · Goose by Block review · Cline AI agent review · Gemini Code Assist IDE review · How to review AI-generated code