Agentic Troubleshooting with Amazon Bedrock: Hype vs. Reality

Agentic troubleshooting—systems that use multiple autonomous software agents to diagnose and resolve complex IT problems—is getting a lot of attention. HPE Zerto, known for backup and disaster recovery, recently built such a system using Amazon Bedrock. The promise: troubleshooting that feels less like poking around in the dark and more like orchestrated diagnostics. Yet, as with any new architecture, it's worth questioning how much of this is fresh paint versus real structural change.

On paper, agentic setups promise faster root cause analysis and hands-off problem resolution. The catch: multi-agent architectures add complexity, increasing the risk of brittle workflows and hard-to-trace errors. Businesses should ask themselves: does this genuinely cut downtime, or just shift troubleshooting from humans to software agents in a maze of logic?

Unlike cloud-only models, Zerto’s system runs inside the customer’s own environment. This on-premises approach addresses concerns over data privacy, latency, and control—three areas where regulated industries push back on pure SaaS deployments. But managing AI toolchains behind the firewall isn’t for the faint-hearted.

Enterprise IT leaders will need to weigh the convenience of managed cloud services against the operational reality of maintaining sophisticated agent systems in their data centers. In our consultancy’s experience, hybrid and on-prem AI deployments tend to create new headaches around patching, monitoring, and cross-system integration.

Zerto’s engineering team faced a genuine challenge: how to make sure troubleshooting agents actually use live, accurate disaster recovery data rather than working off stale logs or canned scenarios. This grounding—tying AI outputs to real, up-to-date data—is critical. Without it, automated decisions risk being irrelevant or outright wrong, especially under pressure during a recovery scenario.

Getting this right means wrestling with data freshness, schema drift, and access controls. The bigger the enterprise, the harder these problems bite. While the architecture is impressive on paper, the devil is always in the operational detail: will these agents flag subtle failures buried under normal activity, or miss emerging threats because their inputs are noisy or outdated?

Strands Agents, the framework underlying Zerto’s approach, lets companies compose chains of AI-driven actions—think of agents as digital coworkers, each with a specialty. Theoretically, this should make troubleshooting more agile. In practice, orchestrating their coordination can turn into an exercise in workflow sprawl and unexpected side effects.

Businesses will want to pilot such systems in controlled environments, track false positives, and regularly audit agent outputs. If the orchestration logic grows tangled, diagnosing why a multi-agent system made a particular call could become harder than the original troubleshooting process it set out to improve.

The move to agentic troubleshooting signals a genuine desire to improve resilience and reduce time to resolution. But results don’t materialize simply by wiring up new architecture. Costs—licensing, infrastructure, and ongoing tuning—could easily offset any gains if the implementation is half-baked or poorly aligned with real operational needs.

For most enterprise IT shops, the real differentiator will be whether these systems demonstrably reduce mean time to repair and free up scarce human expertise for higher-value work. Until then, agentic troubleshooting lives in a gray zone between promising innovation and yet another layer of tech complexity.