Manual prompt tuning couldn't keep pace. Automated feedback loops now refine models using real-time user comments.
An enterprise AI company automates software development lifecycles using autonomous agents for clients with strict data privacy requirements.
Debugging context-awareness issues across complex agent workflows was cumbersome in secure client environments. Manual prompt optimization based on...
“Our collaboration with LangChain has been critical to successfully deploying enterprise LLM-based systems. We are significantly more confident in our decision making and operational capabilities thanks to the observability and orchestration-layer tooling that we get from the LangChain team.”
AI software development agents for automating engineering workflows.
Framework and developer platform for building LLM-powered applications.
Related implementations across industries and use cases
Agent interactions were a black box. Engineers now link live sessions to full execution traces, spotting root causes instantly.
Standard inference stalled at 1k tokens/sec. A custom engine hit 10k/sec, cutting 20-second refactors to under 400ms.
Ambiguity slowed development cycles. Agents now reason through code reviews and refactors, saving 2,300 hours per client.
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.