Generative and agentic AI are all the rage and they have been for the last few years. But in 2026, production use is getting real. According to the 2026 Gartner Hype Cycle for Agentic AI, although 17% of organizations have AI agents in production today, more than 60% expect to do so within the next two years. That is impressive adoption.
Here’s the part that doesn’t get enough attention: disaster recovery planning hasn’t caught up. Most of the conversation around AI agents and risk focuses on what happens if an agent makes a mistake, or worse, gets hijacked by a bad actor. That’s a real concern, and cyber recovery absolutely needs to account for it. But there’s a quieter risk sitting right next to it: what happens to your agents when you have an outage that has nothing to do with AI at all?
At Arpio, we didn’t ignore this problem, we attacked it head on. Looking at Gartner’s adoption forecast, it’s plain to see that your agents aren’t a side project anymore, they’re core to how you interact with customers and how work gets done inside your business. Amazon Bedrock was built to help you deploy those agents fast, which is exactly why we built Arpio to protect them just as seriously as we protect the rest of your application stack. Here’s why that matters, and what’s actually at stake if you don’t.
Agentic Applications Bring New Kinds of Risk
Agentic AI is quickly becoming core to the modern application. Users (both internal and customers) interact with conversational agents to get answers, accomplish tasks, and more. Behind the scenes, agents add a new form of automation to workflows. Development teams are even using agents to help accelerate the delivery of new applications and features.
Amazon Bedrock gives you the foundation to build that quickly. But from a disaster recovery standpoint, an agent isn’t just another service to back up. It introduces new types of data, new integrations, and new orchestration logic that traditional DR was never designed to handle.
Regardless of where, or how, your agents run, protecting them is mandatory. It’s not enough to recover the app around the agent. Without the agent itself, memory and all, you aren’t fully recovered.
Traditional DR Wasn’t Built for This
Adding agentic AI to an application doesn’t just add a new service to protect, it adds an entirely new class of complexity. Agents operate best with discreet tasks to avoid hallucinations. Building an agentic experience often requires multiple models, data sources, integrations, and agents. When you think of this from a DR perspective, it doesn’t really matter whether your agents are automating internal workflows or acting as the front door for your customers, your application isn’t really restored after a disaster until the agents are back up too.
Traditional disaster recovery simply wasn’t designed for what agentic AI brings to the table: agent memory, model configurations, runtimes, integrations, and the connective tissue between all of them. Every one of those pieces adds complexity to your recovery plan, and complexity is exactly what breaks automation when you need it most.
What’s Actually at Stake
Let’s talk numbers for a second. Every minute of downtime costs you customers, reputation, and money. That math doesn’t change just because the thing that went down happens to be an AI agent instead of a database. In order to deliver consistent results for a frontend agent experience or an automated workflow, you need to replicate all of the services and resources of both your application and your agent, not a select few. Plus, agents are often built to store memories of past interactions and results. These memories are integral to how the agent operates as it matures. Lose those memories and you are back to square one.
Then there’s cyber recovery. Because cyber recovery has to account for a bad actor actively inside your systems, you need an air-gapped environment where you can confirm the threat is truly gone before you bring anything back online. Most of the conversation about agents and cyber risk focuses on agents as the bad actor, which is a legitimate concern. But if you have an agentic application, your validation after a cyber event needs to encompass validating that your agents haven’t been used for the attack, then restore them to a steady state, preserving memories and ensuring their identity and access wasn’t corrupted. If your DR plan doesn’t account for backing up your agents and everything you’ve configured around them in Amazon Bedrock including models, integrations, A2A and MCP connections, and more then you can’t actually call your workload restored. You’ve recovered the shell of your application and left the parts that make it work behind.
Arpio provides the first DR solution built for your agents
We built Arpio around a simple idea: disaster recovery strategy should match the way your cloud applications actually work, not a simplified version of them. As cloud applications evolve, Arpio evolves with them. Agentic AI workloads are no different. That’s why we prioritized our support for Amazon Bedrock.
Arpio customers now have access to new features designed to extend our platform to support Amazon Bedrock, including AgentCore, Knowledge Bases, and Guardrails. This means that your enterprise, agentic applications now have a comprehensive disaster recovery solution. We’d love to hear from you so we can show you how it works. Request a demo today.
