Why not just use an LLM to review your pull requests?

Yes, you can paste a pull request diff into a capable LLM and ask for a review. For a one-off second opinion, that can be useful.

The problem starts when a team treats that as a review system. Agentic software delivery needs coverage, independence, context, validation, privacy, and memory across every PR. A chat prompt does not give you that.

An LLM can review a diff. A team needs an independent review system.

What code review actually requires

The modelnecessary, not sufficient

Independence

Coverage

Evidence

Validation

Security & audit

The model is one part. The hard product is the trusted review loop around it.

The honest answer

If you are reviewing a small personal change, using an LLM directly may be enough. Paste the diff, ask what looks risky, and use the answer as another input to your judgment.

That workflow breaks down for teams. It depends on someone remembering to run the prompt. It depends on them choosing the right context. It does not know your repo rules, production behavior, privacy boundaries, prior review feedback, or validation environment. It also leaves no durable evidence trail.

Do not let the author be the only reviewer

Coding agents are excellent authors. Claude Code, Codex, Cursor, and similar tools can inspect files, edit code, run commands, and iterate quickly. But the agent that wrote a change should not be the only reviewer of that change.

The reason is structural. The same agent carries the same task framing, assumptions, context window, and blind spots that produced the implementation. It may catch syntax errors, missing tests, or obvious mistakes. It is less likely to challenge whether the plan drifted, whether the non-goals were violated, or whether production behavior changed in a way the original coding session never saw.

Coding agents are great authors. They should not be the only reviewers of their own work.

Human teams separate author and reviewer for this reason. Agentic development needs the same separation of duties.

Where coding agents help

A coding agent is still valuable in the review loop. It can explain unfamiliar code, run local tests, inspect nearby files, and help the developer fix findings quickly.

The boundary is responsibility. The coding agent can plan, write, and iterate. The review system should independently check whether the result matches intent, follows repo rules, respects production behavior, and has evidence behind the final decision.

What a team review system adds

A real review system has to do work around the model:

Need	Why direct LLM review falls short	What Spinal adds
Coverage	Someone must remember to paste the diff	Local CLI review and automatic PR review
Independence	The coding agent may review its own assumptions	A separate trust layer checks intent, diff, evidence, and validation
Repo context	Rules live in prompts or memory	Persistent repo instructions, knowledge, and tools
Production evidence	The model sees code, not runtime behavior	Logs, metrics, traces, errors, and SLOs shape findings
Validation	The output is advice	Risky findings become PR-specific checks in CI/CD or sandbox
Audit	The review disappears into chat history	Intent, findings, evidence, validation, and decisions are saved

A concrete example

Suppose a coding agent updates SSO callback handling. The diff looks clean. Existing tests pass. The risk is subtle: the change may turn a sliding idle timeout into a fixed session lifetime.

If you paste the diff into an LLM, you may get a useful suggestion:

Consider adding tests around session expiry and refresh behavior.

That is not wrong. It is also not enough for a team deciding whether to merge.

Spinal checks the same change against the captured intent, where the non-goal says session lifetime must not change. It reviews the branch locally before push. At PR time, it adds repo rules, CI state, prior feedback, and production evidence. Then it uses login traces and SLOs to generate a validation that simulates activity before the idle timeout and checks whether the session expiry extends correctly.

Final signal
Status: needs changes
Risk: session timeout changed from sliding to fixed
Evidence: login traces show active sessions near the SLO boundary
Validation: reproduced early re-auth after simulated activity
Action: preserve refresh-based expiry extension before merge

The difference is not that one uses a model and the other does not. The difference is that one produces an opinion, while the other produces an evidence-backed review signal.

Build vs buy

You can build this system yourself. But price the system, not the model call. You need source-control integration, local CLI support, PR automation, repo-specific rules, tool access, observability connectors, validation generation, CI or sandbox execution, redaction, access control, audit history, retries, and a way to preserve review memory.

That is the product surface. The LLM is one component inside it.

When direct LLM review is enough

Use a direct LLM review when the change is small, low-risk, personal, and you mainly want another perspective.

Use a review system when the change is team-owned, production-facing, generated by an agent, subject to repo rules, privacy-sensitive, or risky enough that the final answer needs evidence.

The takeaway

The question is not whether LLMs can review code. They can. The question is whether your team can trust the review process when agents are creating more code, faster, across more repositories.

Spinal exists for that process: independent review around coding agents, grounded in intent, production evidence, validation, and memory.