Dependency vulnerability patch
Confirm exposure, choose the smallest safe upgrade, update the lockfile, and run focused tests.
What this workflow does
Confirm exposure, choose the smallest safe upgrade, update the lockfile, and run focused tests. It produces a source-linked draft, validates it against policy, and keeps publication or system updates behind an explicit approval.
What you gain
Real dependency risks reach review with evidence and a tested patch.
What the AI agent changes
How this worked before
Templates could merge known fields, but they could not understand source documents or adapt the draft to exceptions.
What the AI agent changes
The agent reads source material, drafts the right version for the case, cites evidence, and leaves the final send or write to a person.
Agent trigger
A dependency alert affects a package used by the repository.
Inputs
- Process context: advisory, dependency graph, runtime usage, changelog, code, and tests
- Approved policies, ownership, and exception rules
Agent flow
- 1
Assemble the current source material, template, and policy version.
- 2
Draft only claims that can be linked to allowed evidence.
- 3
Prepare a minimal patch, residual risk, and review evidence and mark every unresolved exception.
- 4
Publish or write back only the approved version.
Human decisions
After step 3
The accountable process owner approves a minimal patch, residual risk, and review evidence.
Outcome
- Tested dependency-upgrade pull request
- Evidence, exceptions, and audit trail
Guardrails
- Never publish, send, or post without the named approval.
- Keep templates, policies, and source timestamps visible to the reviewer.
Risks and mitigations
Unsupported claims can enter a customer-facing or regulated record.
Attach evidence to material claims and block approval when citations are missing.
Sources and evidence
Sources establish feasibility or impact. Not every metric comes from an identical implementation.
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