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A feature flag removal bot should refuse to edit or archive a flag whenever it cannot establish that the flag has one intended, settled behavior across every critical environment, that no dependency will be stranded, and that its code search covers the repositories and references that matter. An inactive or launched status is a reason to investigate—not permission to remove code.
What should make the bot refuse?
| Signal | Why it is not safe to proceed | Bot response |
|---|---|---|
| Different variations or active rollouts across critical environments | Removing a conditional could change behavior in an environment that still matters. Mixed states can also have legitimate explanations, such as pre-release work in staging. | Stop and request review of the environment state and intended behavior. |
| An unresolved prerequisite or dependent flag | Removing a prerequisite can leave dependent flags in an invalid or unintended state. LaunchDarkly’s flag health guidance identifies prerequisites as a hard blocker. | Do not remove the prerequisite until dependents have been handled. |
| Age, inactivity, or low evaluation volume is the only evidence | Those signals do not establish that the guarded code is dead, that a permanent setting is no longer wanted, or that all environments agree. LaunchDarkly cautions: “However, do not archive flags solely because of their status.” | Treat the flag as a review candidate, not an approved cleanup. |
| Incomplete repository access or uncertain search results | The bot may miss references in inaccessible repositories, generated code, wrappers, or dynamically constructed keys. A scan only supports conclusions about the code and commit it actually covered. | Stop or escalate; report the missing repositories, permissions, or search limitations. |
| No authoritative answer about which branch to keep | Environment state, defaults, and targeting can disagree. A code-only guess may preserve the wrong behavior. | Ask a person to resolve the intended value before proposing a patch. |
| The requested action is irreversible or outside policy | Permanent deletion can erase history, and reusing a deleted key while old code still refers to it can cause that code to reference the wrong flag. | Prefer a reviewable code change followed by archive or deprecation where supported and allowed. |
What evidence is enough to move forward?
Settled behavior in the environments that matter
First identify which environments are critical for this service; names such as “production” and “staging” are not universal definitions. Then compare the flag’s evaluations, variations, targeting, and rollout state across those environments. LaunchDarkly’s description of its Vega cleanup behavior says it checks critical environments and proceeds only when the flag serves the same variation across them; otherwise, it stops and reports that the flag is still active. That is a vendor-specific implementation pattern, not a universal safety standard. Its Vega cleanup description is the source for that behavior.
A mixed state is not proof that a flag is obsolete or proof that it must remain forever. It is an unresolved condition. For example, inactive production alongside active staging could reflect upcoming release work or stale staging configuration. The bot should leave the decision to a reviewer unless the reason and intended behavior are established.
No dependency will be left behind
Check both platform metadata and repository relationships for prerequisites and dependents. A provider’s dependency information can reveal relationships that a text search misses; repository inspection can reveal application-level coupling that metadata does not capture. If either check finds an unresolved dependency—or cannot complete—do not remove the flag yet.
#1 Best Overall
The code scan has a stated scope
Search every repository the cleanup policy places in scope, including wrapped references and generated code where applicable. Record the repositories and commit scanned. Dynamic key construction is a particular source of uncertainty: a search for the literal flag key may not find code that assembles it at runtime. The LaunchDarkly cleanup-agent specification hosted by GitHub calls out this risk and describes stopping when references cannot be found or a GitHub permission problem interrupts cleanup.
Reference-scan results are bounded evidence, not proof about unseen code. LaunchDarkly describes an “extinction event” as indicating that references were removed from the codebase as of a specified commit after the scan is rerun. A bot should make that scope and commit visible rather than report simply that “the flag is unused.” See LaunchDarkly’s code-reference documentation.
Rank #2
The value to preserve comes from an authoritative source
Before simplifying a conditional, determine which behavior is intended to remain. The GitHub-hosted cleanup-agent specification says its implementation should preserve current production behavior and use LaunchDarkly as its source of truth. That is a concrete implementation choice, not a vendor-neutral rule: a team may define a different authority in its own policy. If production state, configured defaults, or targeting rules conflict, the bot should explain the conflict and request a human decision rather than choose a branch by inference.
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What should a cautious cleanup workflow do?
- Define scope. Identify the relevant environments, which are critical for this service, and the repositories the bot is authorized and expected to inspect.
- Read the flag configuration. Fetch the complete configuration and lifecycle state from the project’s configured source of truth. If the fetch is partial or fails, stop.
- Check consistency and dependencies. Compare evaluations and variations across critical environments; inspect rollout state, prerequisites, and whether the flag is temporary or intended to remain as a permanent control. Treat mixed or missing evidence as a review condition.
- Scan the code surface. Search the in-scope repositories for direct and wrapped references. Identify dynamic keys, generated code, inaccessible repositories, and any search failures instead of treating them as clean results.
- Choose the behavior to preserve. Use the authority defined by project policy, then make the narrowest code change that preserves that behavior. Explain the value and evidence in the pull request.
- Run project checks and rescan. Run the repository’s own tests and static checks, then repeat reference scanning on the resulting commit. There is no universal test matrix established for every removal bot; use the project’s checks and route unhandled patterns to review.
- Archive or deprecate after code removal. Retain an auditable trail where policy and platform capabilities allow it; do not permanently delete the control merely because cleanup seems complete.
Should the bot leave the flag, archive it, or delete it?
| Action | Appropriate when | Main trade-off |
|---|---|---|
| Leave it in place and request review | Environment behavior, dependencies, intended value, or repository coverage is unresolved. | Cleanup is delayed, but the bot avoids making an unsupported behavior change. |
| Remove the code path, then archive or deprecate | The behavior to preserve is established, dependencies are handled, and the code change has passed project review and checks. | Requires a reviewable change and verification, while retaining an auditable flag history where supported. |
| Permanently delete the flag | Only when project policy specifically permits deletion and the team has addressed history and stale-reference risks. | Deletion can remove history; reusing the key while old code still contains it can lead that code to reference the wrong flag. LaunchDarkly recommends archiving or deprecating rather than deleting for these reasons in its technical-debt guidance. |
Does existing research establish one universally safe policy?
No. The 2019 paper “Software Development with Feature Toggles: Practices used by Practitioners” describes 66 collected artifacts—10 peer-reviewed papers, 41 blog posts and online articles, and 15 videos—and identifies 17 practices across management, initialization, implementation, and clean-up. Its authors explicitly say they lacked enough evidence to label any practice a “best” practice. These counts describe the study’s material and taxonomy; they do not measure the safety of removal bots or validate a particular cleanup policy.
That makes conservative stopping rules a reasoned engineering policy, not a proven cross-platform standard. A bot should make uncertainty legible: name the unresolved environment, dependency, search gap, or policy conflict; preserve the flag; and ask for a specific human decision.
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