Single-step workflows failed at deep research. Autonomous agents now run parallel investigations to deliver structured data in seconds.
A search technology provider building an autonomous agent capable of exploring the web to find structured information for complex user queries.
Moving beyond simple question-answering required an architecture capable of handling long-running, autonomous workflows. A rigid, single-step process...
“The observability – understanding the token usage – that LangSmith provided was really important. It was also super easy to set up.”
AI-native search engine and API for developers and LLM applications.
Framework and developer platform for building LLM-powered applications.
Related implementations across industries and use cases
Static workflows bottlenecked complex tasks. Now, Claude coordinates 8 agents to generate unique execution strategies for every query.
Simple routers lost context on complex code. A modular state machine design enabled the system to autonomously fix 243 real-world bugs.
Engineers struggled to link scattered data. Now, an automated system connects sources instantly, fueling accurate autonomous agents.
Engineers manually correlated alerts across systems. AI agents now diagnose issues and suggest fixes, cutting recovery time by 35%.
Minor edits required days of crew coordination. Now, staff use avatars to modify dialogue and translate languages instantly.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.