Arthur: Full-Lifecycle Platform for Reliable AI Agents
Arthur provides continuous evals, agent governance, built-in guardrails, and flexible deployment to ship reliable AI agents fast, addressing the 25% ROI failure rate of most AI projects.
Platform Delivers End-to-End AI Reliability
Arthur's platform covers the full AI lifecycle with continuous performance evals for visibility into model reliability, agent discovery and governance to enforce policies and oversight, built-in guardrails to block misuse and off-brand outputs, and model-agnostic support for ML, GenAI, or agentic systems. Deployment options include SaaS, on-prem, GCP, or AWS, plus an Engine Toolkit for real-time monitoring and custom dashboards. This setup claims 99% reliability, 24/7 monitoring of all interactions, and 0 unwanted outputs by blocking issues pre-user.
Enterprise Outcomes Cut Maintenance and Speed Deployment
Trusted by teams at Axios, Upsolve, and Expel. Axios reduced maintenance workload by 50% with one-stop monitoring; Upsolve built trusted agentic AI; Expel cut ML monitoring time by 50%. One team shipped a production model from idea to implementation in hours via seamless integration that enforces data best practices. Counters the stat that only 25% of AI projects return investment by focusing on production-scale reliability over prototypes.
Resources for Production-Ready Agents
Blog covers turning vibe-coded Jira bots into reliable agents in two weeks, best practices for agent building (Part 4: experiments and supervised evals), and prompt management from hardcoded to production agents. Studio videos include building agent discovery/governance strategy, moving past vibes to production agents, and executive guide to AI agent innovation.