Exceptions, claims, disputes sat beyond automation's reach—too complex for bots, too routine for engineers. Custom agents handle them.
A customer service AI platform that already automates a large share of routine requests for enterprise clients, including companies handling more than 14 million service interactions annually.
The work that resists automation sits in the middle: return policy exceptions, complicated warranty claims, detailed billing disputes. Smaller...
“Every customer is so unique to what they do. There’s a lot that happens behind-the-scenes, and a lot of this was being handled manually.”
AI-powered customer service and employee experience software platform.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Zendesk's Custom agent builder is part of this use case:
Related implementations across industries and use cases
Dense API docs bottlenecked non-technical users. Now, Claude builds fully configured voice agents from simple plain-text requests.
Agents were buried in common questions, with resolutions taking 3 hours. Now, an AI agent fields FAQs, freeing experts for complex cases.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Developers kept hitting the same silent API pitfalls alone. One PM built a pipeline that learns from each session and shares the knowledge.
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.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Exceptions, claims, disputes sat beyond automation's reach—too complex for bots, too routine for engineers. Custom agents handle them.
A customer service AI platform that already automates a large share of routine requests for enterprise clients, including companies handling more than 14 million service interactions annually.
The work that resists automation sits in the middle: return policy exceptions, complicated warranty claims, detailed billing disputes. Smaller...
“Every customer is so unique to what they do. There’s a lot that happens behind-the-scenes, and a lot of this was being handled manually.”
AI-powered customer service and employee experience software platform.
Anthropic is a technology company specializing in artificial intelligence and machine learning solutions.
Zendesk's Custom agent builder is part of this use case:
Related implementations across industries and use cases
Dense API docs bottlenecked non-technical users. Now, Claude builds fully configured voice agents from simple plain-text requests.
Agents were buried in common questions, with resolutions taking 3 hours. Now, an AI agent fields FAQs, freeing experts for complex cases.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Developers kept hitting the same silent API pitfalls alone. One PM built a pipeline that learns from each session and shares the knowledge.
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.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.