Flying blind with agents in critical infrastructure — raw logs the only window. Now production traces feed evaluation before every release.
A global energy technology company with 160,000 employees and roughly 40 billion euros in annual revenue, operating an AI Hub of 350 specialists who had deployed 60+ agents across critical infrastructure in over 100 countries, subject to strict data residency and cybersecurity requirements.
Their traditional MLOps approaches didn't translate to LLM-based systems, leaving teams unable to debug agent behavior beyond raw logs, measure the...
“The challenge of accuracy, the challenge of quality of answers, the challenge of guardrailing, are very real. When you deploy a solution at scale, you need tooling like LangSmith. Everything linked with trustability and understanding what happens is extremely valuable for us.”
Energy management and industrial automation solutions provider.
Framework and developer platform for building LLM-powered applications.
Schneider Electric's Enterprise automation platform is part of this use case:
Related implementations across industries and use cases
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Teams searched 32,000 scattered docs to fix devices. AI now mines that data to resolve 50% of cases autonomously.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Minor updates required 3.5-hour re-recordings. Now, teams use AI avatars to regenerate training videos from slides in 30 minutes.
Complex multi-tab spreadsheets limited early bots. Now, an extraction pipeline structures this data for AI to reliably resolve queries.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
Flying blind with agents in critical infrastructure — raw logs the only window. Now production traces feed evaluation before every release.
A global energy technology company with 160,000 employees and roughly 40 billion euros in annual revenue, operating an AI Hub of 350 specialists who had deployed 60+ agents across critical infrastructure in over 100 countries, subject to strict data residency and cybersecurity requirements.
Their traditional MLOps approaches didn't translate to LLM-based systems, leaving teams unable to debug agent behavior beyond raw logs, measure the...
“The challenge of accuracy, the challenge of quality of answers, the challenge of guardrailing, are very real. When you deploy a solution at scale, you need tooling like LangSmith. Everything linked with trustability and understanding what happens is extremely valuable for us.”
Energy management and industrial automation solutions provider.
Framework and developer platform for building LLM-powered applications.
Schneider Electric's Enterprise automation platform is part of this use case:
Related implementations across industries and use cases
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Teams searched 32,000 scattered docs to fix devices. AI now mines that data to resolve 50% of cases autonomously.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Minor updates required 3.5-hour re-recordings. Now, teams use AI avatars to regenerate training videos from slides in 30 minutes.
Complex multi-tab spreadsheets limited early bots. Now, an extraction pipeline structures this data for AI to reliably resolve queries.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.