AI

Real IT landscapes are wild gardens: three logging stacks from two acquisitions, Argo CD next to Flux, strict segmentation everywhere. AI agent demos assume the opposite. Hyground 2.0 closes that gap with one IT operations agent across your entire landscape: 20,000+ workloads, 40+ clusters, and hundreds of connections into observability stacks, databases, cloud accounts, and ticket systems, all in one conversation. A thin outpost per environment keeps credentials local, access stays read-only by default, and no data leaves your infrastructure. Plus: why we built a multi-agent system first, and why we tore it out.

Twenty OWASP risks, but for an AI SRE agent they can be collapsed into five hard hurdles between a working demo and production you can trust.

An AI SRE is an autonomous, LLM-powered agent that triages alerts, investigates incidents, and finds root causes across production systems without step-by-step human direction. The role is emerging just as AI-generated code pushes operational toil to its first rise in five years. What AI SREs do, where they run, and how to evaluate one.

OWASP's Top 10 for Agentic Applications is the threat model for AI agents in production systems. Here is what each of the ten risks means for an AI SRE agent.

OWASP's LLM Top 10 turns from an abstract risk list into a concrete architecture spec the moment you put an AI agent inside your operations loop.

Ten leading AI SRE tools in 2026, scored on what procurement actually asks: where data lives, what the agent can touch, who owns the LLM.

Testers lose hours chasing bugs that turn out to be mismatched deploys, conflicting integrations or broken infrastructure. Hyground's SRE Agent gives you the environmental clarity to know whether your next test session will actually produce trustworthy findings, before you start.

AI is great at observing production systems but can't replace SREs because root cause analysis requires system history, institutional knowledge, and human judgment that models lack.

Effective operations require specialized, secure, and centralized agent architectures rather than risky local execution.

Effective steering relies on high-signal structure and hierarchical clarity rather than aggressive, loud wording.

Successful investigation requires autonomous agents that reason and adapt through iterative loops.

Multi-step AI workflows often waste tokens by passing large intermediate tool results through the model's context.

Loading every available tool definition upfront causes significant performance degradation and wastes the model's limited attention budget.