
v0.7.0
LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
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LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
When a conversation in Assembled’s AI Chat Agent reaches the point of handoff, it can take one of two paths: live chat with a person or email for follow-up. The right path depends not only on what the customer needs, but also on whether the right team is open for live support.
The work of running support well rarely announces itself. It shows up in a filter that used to hang for thirty seconds, a setting that used to require a ticket, a column that finally shows the right data. Here's what changed in August.
After we adopted coding agents at Assembled, engineers kept telling me they felt much faster. But when we looked at our delivery metrics, the improvement was much smaller than we expected. We were writing more code and creating more PRs, but only merging slightly more of them.
Today we're introducing a redesigned workflow monitoring experience in Assembled: a new Workflows page that shows how every workflow is performing at a glance, plus deeper per-workflow monitoring that pinpoints the exact step causing problems.
Data connectors are a new, easy way to efficiently retrieve your company data that produces faster, more accurate responses from your AI agents across voice, chat, email, and Copilot.
The conceptual case for connecting your WFM data to AI is easy to make. The harder question is what it actually looks like when someone builds it and runs it in production.
Workforce managers and support leaders can use plain language to diagnose, plan, and act on live Assembled data directly from Claude, ChatGPT, or any MCP-compatible AI assistant.
A real-time analyst opens a dashboard, spots an adherence miss, messages the agent. A planner pulls historical volumes to build next quarter's staffing plan. A scheduler opens a spreadsheet to balance coverage against fairness rules. This is how WFM has worked for 30 years.
A mobile-first experience for agents that puts their schedule, shifts, and requests in their pocket.
Discover how to identify and act on knowledge gaps in your support team to improve AI performance, agent quality, and customer satisfaction.
Agentic workforce management meets enterprise CX: Assembled and Five9 help teams manage AI, human agents, and workflows in one platform.
Assembled deployed its own voice AI in support. Here’s what the rollout revealed about making AI phone support actually work.
Introducing Brand Studio: a unified system for creating, managing, and deploying distinct AI agent identities across chat, voice, email, and copilot.
Go beyond containment and CSAT. Assembled AI Experience Scores delivers automatic, explainable evaluation of AI customer experience at scale.
Genesys Cloud users can now access Assembled's AI-powered workforce management suite with automated schedule, accurate forecasting, and real-time insights.
Learn how AI chat agents are transforming customer support — how they work, what they can resolve, and how to evaluate them for your team.
Compare the 7 best AI copilots for customer support in 2026. See pricing, setup effort, ideal org size, and which tools actually help agents in real time
Assembled's new AI-powered Schedule Generation feature builds optimal support schedules automatically — saving hours and reducing human error.
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