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.

White text "Year two" on a grainy background with diagonal orange, yellow, and blue color gradients.

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.

"Multimodal agents" on a blurry green and pink background

Next gen multimodal agents: an interface that morphs with the conversation

Sierra’s multimodal agents bring voice, text, and visuals into the same conversation, so customers get the best of each medium without having to pick just one.

2026年9月10日

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Hyper-𝜏-bench: Evaluating agents that build agents

Today we’re open-sourcing hyper-𝜏-bench, a new long horizon agent evaluation that measures how well models can not only act as an agent, but construct one.

2026年9月8日

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Agency: Secure, scalable sandboxes for agents

The hardest part of building great agents isn't the model — it's everything around it. That’s where Agency comes in, the infrastructure that gives every Pinecone session and every task for Ghostwriter (our agent-building agent for customers) a secure place to live and run.

2026年7月29日

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Building Sierra’s MCP Gateway: An engineering iceberg

Sharing the biggest lessons we learned building Sierra’s MCP Gateway — and what it looks like to build software in an AI-first engineering organization.

2026年7月22日

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Pinecone: Harnessing the wisdom of the workforce

As frontier models become more capable, companies are increasingly asking themselves the same question: What's our moat? We believe a large part of the answer lies in context.

2026年7月15日

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AI-pilling our company: lessons learned

Our AI acceleration team has been developing agents for employees across every department at Sierra. Here's what they built — and lessons learned along the way.

2026年7月9日

AI-native product localization

One Sierra engineer localized Agent Studio in four months — mostly solo, with AI coding agents. Here's what the process revealed about coordination overhead, feedback loops, and the changing shape of software engineering.

2026年5月28日

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Voice AI is only as good as what it hears

We’ve built a transcription platform that dynamically routes across providers, incorporates a customer’s context, supports 70+ languages, and adapts to real-world variability, improving both agent effectiveness and customer satisfaction.

2026年5月18日

Text "τ-knowledge" in white, with a glowing 'e', against a grainy, abstract background of shifting pink, purple, and green tones.

𝜏-knowledge: benchmarking agents on real-world knowledge

𝜏-Knowledge measures how well agents can work through messy, evolving knowledge bases to complete complex, multi-step tasks. While models are improving, they still struggle to reliably use this information in practice, leaving a large gap to real-world performance.

2026年5月13日

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