Engineers manually scoured dashboards to investigate alerts. Now, they query an AI agent to pinpoint root causes and surface exact fixes.
A global provider of food safety and resource management solutions with over 100 years in operation, running site reliability teams that monitor approximately 550 Azure cloud resources within a single application monolith.
SRE teams fielded 30-40 performance alerts daily, manually navigating multiple dashboards and data sources to diagnose each incident. The reactive...
“Azure SRE Agent changes our overall interaction with the system, allowing us to pose questions and receive precise, targeted answers.”
Water, hygiene, and infection prevention services for global industrial markets.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Ecolab's Incident response is part of this use case:
Related implementations across industries and use cases
Comparing quotes and sorting inquiries manually bottlenecked staff. Now, AI handles both while custom agents proactively flag supply risks.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.
A small team manually investigated alerts across 35 AWS accounts. Now, engineers use AI chat to instantly pinpoint system anomalies.
A small team manually investigated alerts across 35 AWS accounts. Now, engineers use AI chat to instantly pinpoint system anomalies.
With 4,000 services, manual triage couldn't keep up. AI agents now run parallel checks to pinpoint the root cause in seconds.
Quality reviews dragged for weeks, gated by a handful of specialists. Now employees build agents that move them through in about an hour.
Manual 12-day checks for EU deforestation rules created risk. Now, an AI traces the supply chain in 3 minutes, ensuring full compliance.
Manual alert triage overwhelmed analysts. Now, AI resolves 80% of threats autonomously, freeing teams for proactive threat hunting.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.
Engineers manually scoured dashboards to investigate alerts. Now, they query an AI agent to pinpoint root causes and surface exact fixes.
A global provider of food safety and resource management solutions with over 100 years in operation, running site reliability teams that monitor approximately 550 Azure cloud resources within a single application monolith.
SRE teams fielded 30-40 performance alerts daily, manually navigating multiple dashboards and data sources to diagnose each incident. The reactive...
“Azure SRE Agent changes our overall interaction with the system, allowing us to pose questions and receive precise, targeted answers.”
Water, hygiene, and infection prevention services for global industrial markets.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Ecolab's Incident response is part of this use case:
Related implementations across industries and use cases
Comparing quotes and sorting inquiries manually bottlenecked staff. Now, AI handles both while custom agents proactively flag supply risks.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.
A small team manually investigated alerts across 35 AWS accounts. Now, engineers use AI chat to instantly pinpoint system anomalies.
A small team manually investigated alerts across 35 AWS accounts. Now, engineers use AI chat to instantly pinpoint system anomalies.
With 4,000 services, manual triage couldn't keep up. AI agents now run parallel checks to pinpoint the root cause in seconds.
Quality reviews dragged for weeks, gated by a handful of specialists. Now employees build agents that move them through in about an hour.
Manual 12-day checks for EU deforestation rules created risk. Now, an AI traces the supply chain in 3 minutes, ensuring full compliance.
Manual alert triage overwhelmed analysts. Now, AI resolves 80% of threats autonomously, freeing teams for proactive threat hunting.
Finding root cause meant stitching logs, traces, and metrics by hand during incidents. Now AI surfaces the hypothesis; engineers verify.