Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
An AI agent platform built on messaging infrastructure handling 7 billion monthly conversations, deploying customer support agents for enterprise companies across retail, travel, B2B SaaS, and marketplaces.
Errors in production AI agents, like wrong pricing or incorrect legal language, demanded immediate fixes. Each engineer maintained their own Python...
“We've essentially built our whole debugging and regression testing workflow on Claude Code. It lets us test agents at scale and catch issues before they reach customers, which we couldn't really do before.”
AI concierge platform for automated customer experience and support.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Delight AI's Software development is part of this use case:
Related implementations across industries and use cases
Novus's agent drew on analytics or code—never both—invisible to the team. Traces caught it; 60% of problems now caught before customers.
Developers kept hitting the same silent API pitfalls alone. One PM built a pipeline that learns from each session and shares the knowledge.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Developers kept hitting the same silent API pitfalls alone. One PM built a pipeline that learns from each session and shares the knowledge.
Rapid code changes left documentation outdated for weeks. An AI agent now monitors every commit and auto-generates PRs to fix discrepancies.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
An AI agent platform built on messaging infrastructure handling 7 billion monthly conversations, deploying customer support agents for enterprise companies across retail, travel, B2B SaaS, and marketplaces.
Errors in production AI agents, like wrong pricing or incorrect legal language, demanded immediate fixes. Each engineer maintained their own Python...
“We've essentially built our whole debugging and regression testing workflow on Claude Code. It lets us test agents at scale and catch issues before they reach customers, which we couldn't really do before.”
AI concierge platform for automated customer experience and support.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Delight AI's Software development is part of this use case:
Related implementations across industries and use cases
Novus's agent drew on analytics or code—never both—invisible to the team. Traces caught it; 60% of problems now caught before customers.
Developers kept hitting the same silent API pitfalls alone. One PM built a pipeline that learns from each session and shares the knowledge.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Developers kept hitting the same silent API pitfalls alone. One PM built a pipeline that learns from each session and shares the knowledge.
Rapid code changes left documentation outdated for weeks. An AI agent now monitors every commit and auto-generates PRs to fix discrepancies.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.