Post by Turing

CASE STUDY: Most multilingual AI agents can translate. Far fewer can reason, plan, and use tools the way people actually do across different languages and cultures. That gap is becoming one of the biggest challenges in building production-ready AI agents. In a recent engagement, Turing delivered: -> 3,500+ multi-turn agentic conversations across 15+ locales -> Conversations spanning 10 to 15 turns with sequential reasoning, parallel tool calls, corrected responses, and locale-specific instruction following -> 10+ quality dimensions evaluated per task, including tool accuracy, hallucinations, system prompt adherence, datetime reasoning, dialogue naturalness, and grammar -> 100% human review coverage, reinforced by automated validation and calibration audits -> 300+ multilingual evaluators with software engineering, machine learning, and data science expertise The challenge wasn't simply translating prompts. Every response had to remain consistent with the user's language, currency, location, formatting conventions, cultural context, and system instructions while selecting the right tools and executing multi-step workflows. This is the difference between multilingual chatbots and multilingual AI agents. As AI moves beyond text generation into autonomous workflows, models need training data that reflects how people actually work across languages, regions, and cultures. That's the kind of high-signal supervision that makes agents more reliable in production. Read the full case study: https://lnkd.in/gHVCjCji