The demo landed exactly the way the vendor promised. The agent scanned twelve projects, flagged the epics it thought were stale, and closed 400 of them in three minutes.
About 30 were actually stale. The other 370 had live sprint work, assigned engineers, and stakeholder dependencies. The engineering manager watched it happen, then opened a support ticket.
Speed is a demo. Precision is a product. When an AI coding agent (software that writes, reviews, or operates on code with minimal human input) misfires at agent pace, a full-instance restore is the wrong tool. What’s the right one? Item-level restore.
The gap between agent pace and review pace
DevOps leads piloting AI coding agents keep hitting the same shape of problem. The agent is fast and the review gates are human, but the gap between those two speeds is where the bad outcomes land.
Picture a typical misfire: A Copilot suggestion rewrites a function and quietly merges changes from the wrong branch. An AI-generated migration script drops a column that three downstream services depend on. An agent tagged with “clean up stale issues” decides stale means anything untouched in 30 days, closes 400 tickets, and moves on.
None of those are malicious. They are small judgment errors a human engineer makes on a bad Tuesday. The difference is volume: A human makes one at a time. An agent makes 400 before you’ve had a sip of coffee.
Most breaches (68%) involve a non-malicious human element, a person making an error or falling for social engineering, according to the Verizon 2024 Data Breach Investigations Report. Now add an operator that moves two orders of magnitude faster than a person.
87% of IT professionals reported experiencing SaaS data loss in 2024, with malicious deletions as the leading cause (Kaseya, 2025 State of Backup and Recovery Report). Agents add a new category underneath that: well-intentioned deletions, at scale, inside the permission scope you granted on purpose.
Three dimensions change at agent pace
Speed changes first. 400 issues in three minutes is slower than an agent at its ceiling. A review cycle built for one engineer cannot catch the outputs of a system that moves that much faster.
Scale changes next. A single agent with a broad permission scope can touch many repos, projects, files, and integrations in one execution. A human on the same day would touch a fraction of that surface.
Not to mention the permission scope: Agents are often deployed with broader permissions than a single engineer carries, because the product manager wanted the agent to “be able to do what it needs to do.” That design choice creates a blast radius bigger than any single human’s authority boundary.
The CISO conversation arrives the same day the AI Council approves the pilot. The governance lead wants to know two things: What is the undo button? And who owns it?
Prevent, detect, contain, restore
Rewind is a SaaS resilience platform built on independent architecture, a platform (not a plugin) that ensures data is accessible even if the SaaS vendor is compromised. Rewind does not have AI products. Rewind’s backup and restore product protects your SaaS data from errors introduced by your own AI agents, automations, and AI-assisted workflows. The in-house engineering team is based in Canada, and integrations are built and maintained in-house, not by outsourced third-party connectors.
Four stages hold up under agent-scale incidents across Jira, GitHub, and the rest of the DevOps stack:
- Prevent. Scope the agent’s permissions to the minimum its workflow requires. If the agent needs to close issues in two projects, it should not have org-level admin in Jira. If it needs to comment on pull requests in five repos, it should not hold repo admin on all of them. GitHub Actions Secrets are a useful mental model: write-only from the API perspective, creatable and updatable via LibSodium, never retrievable as decrypted values, and not captured by a
git clone. Apply the same principle to agents. Give them what they need. Audit the gap between what they have and what they need. - Detect. Instrument the operations an agent performs against the operations a human in the same role would perform. An agent that closes 400 issues in a three-minute burst is a detectable signal. The detection layer does not need to block. It needs to surface.
- Contain. Define the containment pattern before the incident. What is the maximum issues closed per hour before human review kicks in? What is the maximum pull requests merged in a day without a second approval? Those are policy decisions a platform team can make in advance, once, and then stop relitigating.
- Restore. Recover individual items, a single ticket, a single page, a file, a configuration, without affecting the rest of the system. That’s non-destructive recovery.
The fourth stage is where the shape of your recovery surface starts to matter. Full-instance restore is the wrong tool for 370 bad closures in a 12-project Jira instance. The other 11 projects were fine. Item-level restore rolls back what needs rolling back and leaves the rest alone.
Five minutes instead of five weeks
If the response to a 400-issue misfire is “restore the entire Jira instance to last Tuesday,” you are undoing a week of correct work to undo three minutes of incorrect work. Rebuilding from memory, commit history, and Slack threads takes weeks. Item-level restore takes minutes.
The governance patterns that hold up are simple. Scope permissions tightly. Add review gates with latency that matches risk tier. Keep audit trails that attribute agent operations back to the human operator behind the credential. The goal is not to slow the agent down. The goal is to match the recovery surface to the agent’s blast radius.
Walk the 400-issue scenario through to the recovery end. The agent is scoped to three Jira projects and closes 400 issues. 30 were genuinely stale. 370 were active. With item-level restore, identify the 370 active issues by timestamp and restore them to their pre-closure state. No other issues touched. Sprint cadence preserved.
“Rewind doesn’t just give us a full backup of the codebase with just a few clicks; it also gives us a business continuity plan in the event of the worst-case scenario,” says Uttej Badwane, Senior Security Engineer at Carta. The business continuity posture is the shape of the recovery surface, not the shape of the backup.
Three moves for this month
A DevOps lead or CISO running an agent pilot can do three things in the next thirty days:
- Map the agents in your environment by permission scope. Rank them by maximum blast radius in issues per minute, pull requests per minute, and records per minute. Publish the list internally.
- Define the containment policy for the top three. What is the alert threshold? Who is on the escalation path? What is the rollback SLA?
- Run a tabletop on the 400-issue scenario against your Jira and GitHub instances. Ask one question. Can we roll this back without touching the other good work? If the answer is no, the restore surface needs a layer.
Rewind is trusted by more than 25,000 organizations worldwide to protect the SaaS systems where agent work actually lands.
See how Rewind’s item-level restore protects Jira and GitHub from agent-scale incidents at rewind.com/ai-resilience.
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