Sierra ブログ

2026年3月27日

Sierra、日本発のAI企業「OPERA TECH」を買収

本日、Sierraは東京を拠点とするエンタープライズAI企業、株式会社OPERA TECHを買収したことを発表いたします。

Text: Sierra acquires Opera Tech in Japan.

2025年11月5日

Sierra Agent OS 2.0: from answers to memory and action

At Sierra Summit, we unveiled the future of AI agents: a single agent that works across every channel, learns from every interaction, and builds real customer relationships. With eight new products, including Agent Studio 2.0 and the Agent Data Platform, Sierra is turning the promise of AI-powered customer experience into reality.

2026年3月25日

Agents as a Service

Sierra is reimagining software for the agent era—where you simply describe the outcome, and intelligent agents build, execute, and continuously improve the work for you. Meet Ghostwriter, the agent that creates and optimizes other agents, turning your ideas into production-ready customer experiences without clicks, code, or complexity.

2026年2月6日

Year two in review

Sierra is heading into year three with over $150M in ARR, powered by rapid adoption from some of the world’s largest companies. The growth reflects a simple idea: when AI is built around real jobs to be done (not experiments), even the biggest enterprises can see meaningful impact fast.

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2025年11月5日

Introducing Agent Data Platform

Sierra’s first-of-its-kind Agent Data Platform (ADP) gives agents memory, context, and intelligence — turning every interaction into a human, personalized experience. Powered by Agent OS, ADP helps companies move from answering questions to anticipating needs.

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Who monitors the monitors?

Sierra's always-on evaluation layer use an LLM-as-judge to review every conversation so businesses can track agent quality and customer sentiment. But who evaluates the monitors?

2026年5月7日

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Context engineering: the key to great agents

Getting LLMs the right context, at the right time, is the central challenge in building sophisticated, real-world agents. The solution: Context engineering.

2026年5月5日

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𝜏-voice: benchmarking real-time voice agents on real-world tasks

𝜏-voice is a benchmark for real-time voice agents on 278 grounded customer-service tasks across retail, airline, and telecom. It pairs deterministic, end-to-end task scoring with realistic, controllable audio — diverse personas, environmental noise, and free-form turn-taking.

2026年5月1日

The AI-native interview

We’ve redesigned our engineering interview process from the ground up.

2026年4月22日

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μ-Bench: an open multilingual transcription benchmark

μ-Bench is an open multilingual transcription benchmark built from customer service phone calls across five locales. It aims to measure transcription providers on the errors that matter in production: not just how closely transcripts match their reference, but whether they preserve the speaker’s intent.

2026年4月20日

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Golden articles: Evaluating and improving search

Search evaluation shouldn’t be static — it should reflect what actually helps resolve real customer issues. By measuring performance daily against production conversations and feeding those signals back into the system, we've built a continuously improving system for resolving customers’ needs.

2026年4月14日

Meet Linnaeus and Darwin: Search models that drive higher resolution rates

Our purpose-built retrieval and reranking models outperform off-the-shelf ones, driving up to 16 percentage point improvements in resolution rates.

2026年4月3日

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𝜏³-Bench: Advancing agent benchmarking to knowledge and voice

𝜏³-Bench is here. We've expanded agent evaluation to two new frontiers: knowledge retrieval and voice.

2026年3月18日

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Preserving agent behavior while serving LLMs reliably

Keeping an AI agent online isn’t enough — its behavior must remain consistent under provider stress. Here’s how we built the infrastructure to make that possible.

2026年2月13日

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