Migrating Code Security Review to an LLM Agent Safely

You have a working code-review process. Maybe it is human security reviewers, maybe a traditional static-analysis tool wired into CI, probably both. Now you want a Claude-powered security agent in that loop. The temptation is to flip a switch and make the agent a blocking gate on day one. Resist it. A botched rollout that floods engineers with false positives, or worse, lets a real vulnerability through with the agent's blessing, will burn the credibility you need for the tool to ever succeed. Migration is a trust problem as much as a technical one, and the way you sequence the rollout determines whether the agent gets adopted or quietly disabled.

The goal of a safe migration is to learn how the agent behaves on your code, with your patterns and your false-positive triggers, before it has any power to block anyone. You earn the right to gate by first proving the agent is right often enough that people believe it. That proof is built in stages.

Start in shadow mode, where mistakes are free

The first stage is shadow mode: the agent runs on every pull request, produces its findings, and shows them to no one except you, the operator. Its output goes to a log or a dashboard, never to the developer and never to the merge gate. This is the safest possible way to gather real data, because the agent's mistakes cost nothing — a false positive in shadow mode annoys no one, and a false negative blocks nothing.

Run shadow mode long enough to see real variety: across many pull requests, across different services, across the kinds of changes your team actually makes. What you are collecting is a precision-and-recall profile on your live codebase, which is far more informative than any synthetic benchmark because it reflects your real distribution of code. Compare the agent's findings against what your existing reviewers and SAST tools catch, and pay special attention to the disagreements — they are where you learn the most.

flowchart TD
  A["Existing review process"] --> B["Stage 1: shadow mode, findings logged only"]
  B --> C{"Precision acceptable on real PRs?"}
  C -->|No| D["Tune prompt, tools, scope; stay in shadow"]
  D --> B
  C -->|Yes| E["Stage 2: advisory comments to devs"]
  E --> F{"Devs find findings useful?"}
  F -->|No| D
  F -->|Yes| G["Stage 3: gate on high-severity only"]
  G --> H["Expand gate scope as trust grows"]

The exit criterion from shadow mode is a number, not a feeling: the agent's precision on real pull requests has to clear a bar you set in advance. If it is flagging too much noise, you stay in shadow and tune — tighten the prompt, narrow the scope, adjust the toolset — and only graduate when the data says it is ready. Letting an agent out of shadow mode on a hunch is how rollouts fail.

Advisory mode: visible, but never blocking

Once precision clears the bar, promote the agent to advisory mode. Now its findings appear as comments on pull requests where developers can see them — but they still cannot block a merge. This stage does two things. It puts the agent's value in front of the people who will ultimately depend on it, building familiarity and trust. And it surfaces a whole new class of feedback, because developers will tell you, loudly, when a finding is wrong or unhelpful in a way no dashboard ever would.

Advisory mode is where you tune for usefulness, not just correctness. A technically-correct finding phrased as an inscrutable wall of text gets ignored; the same finding written as a clear explanation with a concrete remediation gets fixed. Watch how often developers act on the agent's comments — that action rate is your real adoption metric, and it tells you whether the agent has earned the trust required for the final stage.

Gating, introduced narrowly

Only after the agent has proven itself in advisory mode do you let it block merges, and even then you start narrow. Gate on the highest-severity, highest-confidence findings only — the categories where the agent is most reliable and where a false negative would be most costly, like injection flaws or authentication bypasses. A change that trips a high-severity finding stops until a human reviews it; everything else stays advisory. This keeps the false-positive blast radius small while delivering the safety benefit where it matters most.

Expand the gate's scope gradually as confidence grows, always watching the false-positive rate, because the fastest way to get a security gate disabled by an angry engineering org is to block merges over findings that turn out to be wrong. Crucially, always provide an override path — a documented way for a developer to dispute a finding and proceed with senior sign-off — so the gate channels judgment rather than replacing it. A gate with no escape hatch becomes an obstacle people route around, which is worse than no gate at all.

Run alongside, do not rip and replace

Throughout the migration, keep your existing controls running. The LLM agent complements traditional static analysis rather than replacing it — SAST tools are deterministic and excellent at the pattern-matchable classes of bug, while the agent reasons about context, intent, and the subtle logic flaws that defeat pattern matching. Running both gives you broader coverage than either alone, and it gives you a safety net during the transition: if the agent has a bad week, your old process still catches what it always caught. Decommissioning anything should wait until the agent has a long, boring track record of reliability, and even then "complement" usually beats "replace."

Plan for the rollback you hope not to need

A safe migration includes a way back. Before the agent gates anything, decide in advance what triggers a rollback — a false-positive spike, a confirmed missed vulnerability the agent should have caught, a latency regression that slows every merge — and make demoting it from gating to advisory a single, fast configuration change, not a code deploy. Because the rollout was staged, rollback is graceful: you drop one level, not all the way to zero, and the agent keeps providing advisory value while you diagnose. This reversibility is what lets you move forward with confidence; knowing you can step back instantly is exactly what makes it safe to step forward at all.

Frequently asked questions

What is shadow mode in an agent rollout?

Shadow mode is the first migration stage, where the agent runs on every pull request and logs its findings for the operator only — developers never see them and the agent cannot block a merge. It lets you measure the agent's real precision and recall on your live codebase with zero cost to mistakes, before it has any power over the workflow.

Should an LLM agent replace my existing SAST tooling?

Usually not — it complements it. Static-analysis tools are deterministic and strong on pattern-matchable bug classes, while an LLM agent reasons about context and subtle logic flaws that defeat pattern matching. Running both gives broader coverage and a safety net during migration, so decommission existing tools only after a long, proven track record.

When is it safe to let the agent block merges?

Only after it has cleared a precision bar in shadow mode and proven useful in advisory mode, and even then start by gating on high-severity, high-confidence findings alone. Expand the gate's scope gradually while watching the false-positive rate, and always provide a documented override path so the gate channels judgment rather than replacing it.

How do I roll back if the agent misbehaves after going live?

Decide your rollback triggers in advance — a false-positive spike, a confirmed missed vulnerability, a latency regression — and make demoting the agent from gating to advisory a single fast config change rather than a code deploy. Because the rollout was staged, you step down one level and keep advisory value while you diagnose, rather than losing the tool entirely.

Bringing agentic AI to your phone lines

CallSphere rolls out its voice and chat agents the same careful way — shadow, advisory, then live — so they earn trust before they handle real calls and book real work 24/7. See the approach at callsphere.ai.

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Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.