Anthropic Introduces Claude Tag: A New Way for Teams to Work with Claude
Score
67Why selected
Claude Tag provides practical functionality for team collaboration scenarios, likely to significantly boost productivity in multi-user AI usage, though large-scale user feedback is yet to validate its effectiveness.
AI commentary
「This update expands Claude from a personal AI assistant to a team collaboration tool, providing stronger infrastructure for enterprise AI applications.」
Quick overview
Anthropic announces Claude Tag, a new feature that enables teams to work with Claude more efficiently. By using tags, team members can organize, share, and track conversations and outputs, enhancing productivity and transparency in AI collaboration.
- Claude Tag allows teams to add tags to conversations and generated content for easy categorization and retrieval.
- Supports sharing tag views among team members to facilitate collaboration and information synchronization.
- Integrates into existing Claude workflows without additional learning cost.
Full text
Showing the source text; the full translation is being generated.
Introducing Claude Tag
Jun 23, 2026
Claude Tag is a new way for teams to work with Claude.
We’re starting on Slack, which Claude can join as a team member. Grant Claude access to selected channels, and connect it to whichever tools, data—and even codebases—you choose. Then, anyone in the channel can tag @Claude in, and delegate tasks to it while they focus on other work. Claude builds context by remembering relevant information from the channels it’s in, and can plan out tasks to complete in the future.
We see Claude Tag as the beginning of an evolution of Claude Code: it makes the model even more proactive, and it works better with a full team. Tagging @Claude is now one of the main ways we get things done at Anthropic. Today, 65% of our product team’s code is created by our internal version of Claude Tag. The same pattern is now spreading well beyond engineering—we’re tagging Claude to chase down product metrics and data, work through support tickets, or even help find the root cause of tricky bugs.
We’re launching Claude Tag on Slack, since it’s a natural home for collaborative work between teams and AI, and where much of Anthropic’s day-to-day work already happens. It’s available today in beta for Claude Enterprise and Team customers. Our goal is to expand where it’s available more widely, so that teams can tag @Claude in the many other places they work.
Working with @Claude
If you’ve worked with Claude Code or Cowork before, Claude Tag will feel familiar. Tag @Claude with a request in simple terms and it’ll break its task down into stages and then work through them in turn, using the tools it has access to. Once it’s done, it’ll respond in a Slack thread with what it’s created.
But tagging Claude comes with a few new advantages:
@Claude is multiplayer. Within a given Slack channel, there’s one Claude that interacts with everyone. This means that anyone can see what it’s working on, and can pick up the conversation from where the last person left off. This makes tagging Claude very different from working within a single chat or for a single task—it’s much more like interacting collaboratively with a teammate.
@Claude learns over time. As Claude follows along with its channel, it builds more context about the work. This means that users don’t need to explain things to it from scratch over and over again. And Claude can even automatically learn from other Slack channels and data sources, if it’s granted permission. (It doesn’t report from private channels.) This gives it the tacit knowledge necessary for it to provide the best possible work.
@Claude takes initiative. If “ambient” behavior is enabled, Claude will proactively keep you updated about whatever it thinks you might need to know. It’ll flag relevant information from across the channels it’s in and the tools it’s connected to, and follow up on threads or tasks that have gone quiet without being resolved.
@Claude works asynchronously. Set Claude a task, and you can focus on your other priorities while it works. It can also schedule tasks for itself, pursuing a project autonomously over hours or days. We’ve found this particularly helpful at Anthropic: we now spend much more of our time delegating tasks to many Claudes in parallel.
You can also send Claude direct messages: it’ll respond privately, using the personal tools and connectors you’ve set up.
Getting started
We’ve designed Claude Tag with teams and organizations in mind: @Claude’s access to sensitive data and task-specific tools can be very tightly controlled.
To get up and running, system administrators specify which tools and information the model should have access to, in which channels. Think of it as creating separate Claude identities for different uses: everything, including its memories, will stay scoped to the channels defined by the administrators. For example, a model set up for sales work won’t pass on memories to one set up for engineering; nor will it give engineers access to any sales data or tools. More information about provisioning access is available here.
Once permissions are set, everyone can begin tagging right away. Administrators can set limits for token spend (both for the organization and for individual channels), and can view a log of everything that @Claude has done, along with who requested each task.
If you’re a Claude Enterprise or Team customer, you have access to Claude Tag in beta starting today. To get started, visit here and follow these four steps:
- Pair Claude Tag with your Slack workspace
- Give Claude access to your tools
- Set a limit on your organization’s monthly spend
- Test Claude in a private channel to confirm it works.
Claude Tag replaces the existing Claude in Slack app. To migrate, administrators can opt in within 30 days. We’re issuing an introductory launch credit to eligible Enterprise and Team organizations so that the whole company can try it out.
Claude Tag works with Opus 4.8. You can read our docs and product page.
Related content
More details on Fable 5’s cyber safeguards and our jailbreak framework
Read moreIntroducing Claude Sonnet 5
Sonnet 5 delivers frontier performance across coding, agents, and professional work at scale.
Read moreRedeploying Fable 5
Fable 5 returns globally July 1. We're also proposing an industry-wide framework for scoring jailbreak severity, together with Amazon, Microsoft, Google, and other Glasswing partners.
Read moreOther reports on this story
- T2TechCrunch：AI（RSS）Anthropic Launches Claude Science: A Workbench for Scientists01:00
Related items
FeaturedDeepSeek V4 Doubles Token Share on OpenRouter, Driven by Agentic Workloads
DeepSeek V4 在 OpenRouter 上的代币份额翻倍，智能体工作负载成主要驱动力
The rapid adoption of DeepSeek V4 signals a strong market preference for high-performance, low-cost inference models, especially as agentic use cases reshape AI deployment.
91
FeaturedEnabling Two Settings Triples ARC-AGI-3 Benchmark Scores
启用两项设置使ARC-AGI-3基准测试分数提升三倍
This finding is crucial! It shows that model performance depends not only on training but also on inference-time configuration. For AI engineers and researchers, this is a must-know optimization direction. Teams deploying models on resource-constrained devices in particular could find a low-cost way to boost capabilities.
91
FeaturedGPT-5.6: Fusing Frontier Intelligence with Frontier Efficiency
GPT-5.6：前沿智能与前沿效率的融合
This news is worth watching as it marks a shift from pure parameter scaling to efficiency competition. For AI developers and business decision-makers, GPT-5.6 means higher performance at lower cost, especially for long-context or agentic tasks previously hindered by inference expenses. OpenAI's move may redefine what 'frontier' means.
91
FeaturedLFM2.5-Encoders: New Models for Fast Long-Context Inference on CPU
LFM2.5-Encoder：面向CPU快速长上下文推理的新模型
This blog post catches my eye because it directly solves the pain point of running long-context LLMs on CPU. For teams or individual developers without high-end GPUs, the LFM2.5-Encoder series offers a ready-to-use open-source solution. Highly recommended for those working on RAG, document search, or code analysis—now a standard server can handle million-token documents.
91
FeaturedGPT-Red: OpenAI Automates Red Teaming via Self-Play to Boost Model Robustness
GPT-Red：OpenAI通过自我对弈实现AI红队自动化，提升模型鲁棒性
GPT-Red marks a milestone from human to automated red-teaming—essential for safety engineers and alignment researchers. While reducing cost, it also raises ethical questions about automated attacks, worth pondering for everyone in AI governance.
91
FeaturedOpenAI Launches GPT-5.5 Bio Bug Bounty Program
OpenAI推出GPT-5.5生物漏洞赏金计划
This program deserves attention as it pioneers the extension of AI bug bounty to biosecurity, offering a new research direction for the security community and a practical training ground for AI risk governance. It is particularly important for model safety researchers, biosecurity experts, and practitioners concerned about dual-use AI risks.
91
FeaturedvLLM Introduces Native-Speed Transformers Modeling Backend for Enhanced Inference Performance
vLLM推出原生速度Transformers建模后端，大幅提升推理性能
This update is critical for AI engineering teams: eliminating format conversion speeds up model iteration, and 2-5x speedup directly reduces GPU costs. If you're building LLM online services or batch inference, this is practically a must-read technical update.
90
FeaturedGoogle DeepMind Introduces SL2T, a Sign Language-to-Text Model for Deaf Users
Google DeepMind 发布手语转文本模型 SL2T，助力聋人用户
This news matters not only as a technical breakthrough but also as a sign that AI is beginning to address the needs of marginalized groups. For developers, the architecture and technical approach of SL2T merit deep study; for accessibility advocates, it's a key case for advancing industry standards.