Enterprise Agent Disaster Recovery: What's Actually New

Fifty-six percent of organizations say they’re not well prepared to detect or contain unintended actions by AI agents, according to Cohesity’s Global Cyber Resilience Report — and that’s the number that should frame every conversation about enterprise agent disaster recovery. Not the market projections, not the vendor launches. The majority of companies deploying autonomous agents into production cannot contain what those agents do wrong, and the disaster recovery stack they already own was never designed to help.

Here’s the uncomfortable part: the spending is already massive. The DRaaS market sits at roughly $13.7 billion to $16.1 billion in 2025, on pace for $26.6 billion to $46.1 billion by the early 2030s. The broader disaster recovery solutions market is forecast to reach $90.15 billion by 2030 at a 30.8% CAGR. Money is not the constraint. The constraint is that almost none of that spend addresses the thing that actually breaks when an agent fails.

What I call the converged resilience pattern is playing out in real time: traditional DR vendors, AI governance platforms, and identity players are all announcing agent protection capabilities within weeks of each other — Arpio and Cohesity both shipped agent-specific recovery announcements in September 2026 — and the resulting category has no settled definition, no standard pricing, and no clear winner. This post breaks down why the old model fails, what’s actually being built, what it costs, and how to position your team before the category hardens.

Why can’t traditional DR recover an AI agent?

Traditional disaster recovery restores infrastructure. It can bring back the server, the VM, the database volume. What it can’t do is undo what an autonomous agent already wrote to your shared production systems — and that’s the failure mode that matters.

Cohesity’s chief product officer put it bluntly in the Agent Resilience announcement: detection can tell you an agent went off course, but it can’t undo the changes. An agent with privileged credentials that’s been querying databases and updating workflows for six hours before anyone notices has potentially corrupted every downstream system that consumed its outputs. Restoring last night’s snapshot doesn’t fix that. It just gives you a clean copy of infrastructure pointing at dirty data.

This is the contrarian truth about agent failures: the expensive part isn’t the outage. A ransomware attack now costs businesses an average of $5.08 million once downtime is factored in, with outages running 50 times longer than the ransom demand itself — but at least ransomware is loud. Agent failure is quiet. It’s the cumulative corruption of business data and decisions made without human review, and 58% of organizations aren’t confident they can even verify AI model and data integrity after an incident. You can’t recover what you can’t verify.

We’ve covered adjacent pieces of this problem before. The state recovery problem in agent rollback patterns explains why code reverts don’t fix customer data exposure, and the durable execution gap in agent checkpointing documents how every tested framework fails its own resume contract. Agent disaster recovery is where those two problems meet your DR budget.

Who’s actually building agent resilience right now?

Two vendors moved first, and both landed in the same week. Arpio announced the first automated cloud DR solution with application state recovery for agentic workloads on Amazon Bedrock and Bedrock AgentCore — extending its full-application replication approach to cover the interconnected resources and memory agents depend on. Cohesity Agent Resilience is available now to select customers with general availability targeted for end of 2026, initially supporting Bedrock AgentCore and Bedrock Agents, with Microsoft and Google platforms on the roadmap.

Notice the pattern: both are AWS-only at launch. If your agents run on Azure Foundry or Google Vertex, you’re waiting. Meanwhile the hyperscalers’ native DR services — the ones most enterprises already pay for — protect servers and data, not agent state. Here’s how the field actually lines up:

Tool	Pricing model	Agent state recovery	Platform coverage
Cohesity Agent Resilience	— (no published pricing)	Yes — memory and configuration snapshots	Amazon Bedrock only at launch; Microsoft/Google on roadmap
Arpio	— (no published pricing)	Yes — application state recovery for agentic workloads	Amazon Bedrock and Bedrock AgentCore
AWS Elastic Disaster Recovery	Flat hourly fee per replicating server	No — infrastructure and data only	AWS workloads
Azure Site Recovery	Per protected instance, 31-day grace period	No — infrastructure and data only	Azure and hybrid
Google Cloud Backup and DR	Per gigabyte, no per-server fee	No — infrastructure and data only	Google Cloud

The table tells the real story. The tools that can recover agent memory don’t publish prices. The tools with published pricing models can’t recover agent memory. That gap is the market in miniature.

There’s also a quieter signal worth tracking: five of the seven major backup and DR platforms — Veeam, Cohesity, Commvault, Zerto, and Druva — now publish official MCP servers for AI-agent access. The DR industry isn’t just protecting agents; it’s wiring itself to be operated by them. Whether that’s resilience or recursion is a fair question.

How does enterprise agent disaster recovery pricing work?

Short answer: nobody knows yet, and that’s a problem you should plan around rather than wait out.

The transparency situation in legacy DRaaS is already thin. Per Anavem’s 2026 comparison, only Azure Site Recovery and AWS Elastic Disaster Recovery publish transparent per-unit pricing — every other major option is quote-based. And the three hyperscaler models diverge in ways that matter for agent workloads: AWS bills flat hourly per replicating server regardless of data volume, Azure charges per protected instance, and Google bills by the gigabyte with no per-server fee. None of these units — servers, instances, gigabytes — map to agents, decisions, or memory states.

For the agent-specific tools, the honest answer is that a deployment cost can’t be projected from current research. No per-seat or per-agent pricing is published for Cohesity Agent Resilience or Arpio, and the hyperscaler DR services are documented only as pricing models — flat hourly per server, per protected instance, per GB — without specific rates in the research. Anyone quoting you a 50-agent deployment cost today is extrapolating from nothing.

What you can model is the metering logic agents already live under, because that’s the unit economics resilience pricing will eventually follow. Salesforce Agentforce pricing in 2026 runs from $0 on the free Foundations tier to $550 per user per month on Max, with usage at $2 per conversation, roughly $0.10 per standard action via Flex Credits, or $2 per resolved case outcome-based. Gemini Enterprise starts at $21 per seat per month for Business with a 300-seat ceiling, $30 per seat per month for Standard and Plus, and a $0 seat fee on pay-as-you-go for 20+ seats. Per conversation, per action, per resolution — the industry already meters agents by the decision, not the server.

My read: the vendors that win this category will price resilience at the granularity of agent actions and memory states, because that’s the unit of risk. Organizations still buying DR by server count will systematically under-protect their agent estates while over-paying for infrastructure coverage that can’t address the actual failure mode. And with disaster recovery already consuming 15% to 25% of the annual IT budget, misallocated DR spend isn’t a rounding error.

How big is the exposure gap between deployment and governance?

Deployment is outpacing governance by orders of magnitude, and the delta is where your recovery risk lives.

Gartner predicts up to 40% of enterprise applications will include integrated task-specific agents by 2026, up from less than 5% today, and the average Fortune 500 enterprise will run more than 150,000 agents by 2028. Against that scale: an OutSystems 2026 survey of 1,900 IT leaders found 96% of enterprises run AI agents but only about 12% have a centralized way to manage them. And 80% of enterprises report risky behavior from live agents, per McKinsey citing a SailPoint survey.

Do the arithmetic on those numbers and the picture is stark. Nearly everyone runs agents. Almost nobody can manage them centrally. Four out of five have already seen risky behavior. And the recovery tooling for the specific thing agents break — shared data, memory state, downstream decisions — shipped to select customers last week, on one cloud.

This is why the autonomy tradeoff deserves honest treatment. Fully autonomous agents querying databases and updating workflows around the clock create real value — that’s why deployment is exploding. But every autonomous action is a potential recovery event, and the value of autonomy scales directly with the cost of the resilience infrastructure required to support it. If your AgentOps tooling only replays failures after they happen, you have observability, not recovery. Those aren’t the same purchase.

What should you do before the category settles?

Don’t wait for the market to pick a winner — it won’t, soon. Instead, sequence your work so each step is useful regardless of which vendor prevails.

1. Build the agent inventory first. You can’t protect what you can’t list. Every agent, its owner, its credentials, and every system it can write to. This is the prerequisite for Cohesity-style topology mapping anyway, and it costs you nothing but discipline.
2. Assign ownership for shared-store writes. Restoring agent memory doesn’t undo writes into production databases that other systems consume. Someone — a database owner, not the agent team — has to own the rollback decision for those writes. Make that assignment before the incident, not during it.
3. Set detection-lag-aware recovery points. A prompt-injected agent can look normal while acting on bad input. How far back your recovery points need to reach is a function of how long bad behavior goes undetected, so measure your detection lag before you negotiate RPOs.
4. Keep hyperscaler DR for what it’s good at. Per-server and per-GB replication still protects the infrastructure layer agents run on. Just stop pretending it covers the agent layer.
5. Pilot agent-specific recovery on Bedrock if you’re there. That’s where both first movers live. If you’re on Azure or Google, pressure your account teams on roadmap timing — the announced roadmaps suggest they need the push.

The open question I’d take into 2027 planning: when resilience pricing does arrive, will it be metered per agent, per action, or per protected decision — and does your current inventory even let you count those units? The teams that can answer the counting question will negotiate from strength. The rest will find out what agent disaster recovery costs the same way they found out what agent failure costs: on an invoice, after the fact.