Simple routers lost context on complex code. A modular state machine design enabled the system to autonomously fix 243 real-world bugs.
A developer tool provider needed to validate its new open-source IDE by building an autonomous agent capable of solving real-world GitHub issues in libraries like Django and Flask.
Traditional agent architectures often rely on simple routers that struggle to manage hidden states or control complex workflows effectively. Without...
Integration layer for connecting AI agents with external tools and APIs.
Framework and developer platform for building LLM-powered applications.
Related implementations across industries and use cases
Single-step workflows failed at deep research. Autonomous agents now run parallel investigations to deliver structured data in seconds.
Ambiguity slowed development cycles. Agents now reason through code reviews and refactors, saving 2,300 hours per client.
Previous models wrote unusable partial code. Now, specialized agents with VM access build and deploy 5,000-line apps in two hours.
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.