Assembled combines AI agents with advanced workforce management to give support teams the speed, flexibility, and control they need to excel. Our platform streamlines staffing for both in-house and outsourced teams, delivers forecasts with over 90% accuracy, and automates more than half of customer conversations. Whether it’s chat, email, or voice, Assembled orchestrates every interaction, allocating work between AI and human agents in real time. Leading brands like Stripe, Canva, and Robinhood rely on Assembled to boost performance and turn support into a growth driver. Key capabilities include scheduling, forecasting, live performance monitoring, vendor management, AI-powered chat, voice, and email agents, plus an AI Copilot that provides instant guidance, suggested responses, and rapid action tools for agents.
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BAND creates robust interaction frameworks designed for enterprise-level applications of distributed AI agents. The platform facilitates immediate, collaborative interactions among both agents and humans, incorporating a runtime control plane that upholds policies, defines authority limits, and ensures transparency across diverse systems.
Additionally, BAND empowers developers, engineering teams, and leaders of enterprise platforms who are managing multi-agent ecosystems spanning internal infrastructures, SaaS solutions, and environments shared with partners. This support enhances operational efficiency and fosters innovation within complex organizational structures.
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Cosmos
Augment Code serves as a dynamic development platform designed to assist engineering teams in transitioning from standalone AI coding assistants to a unified system of software agents. Its innovative Cosmos platform efficiently operates software agents on a large scale, providing them with the necessary context, tools, environments, memory, and feedback mechanisms to enhance their performance with each workflow. Cosmos integrates seamlessly throughout the entire software development lifecycle, featuring reusable expert agents that facilitate the creation of pull requests, review modifications, assess risks, conduct tests, and aid teams in designing their own tailored workflows. The PR Author can manage tasks from the initial commit to the final merge, while the Pair Review function collaborates with the author to evaluate changes. Additionally, the Deep Code Review agent analyzes pull requests comprehensively and provides inline feedback, whereas the PR Risk Analysis identifies potential blast radius, security vulnerabilities, and migration challenges. Finally, the Tester agent thoroughly tests changes from start to finish, delivering results complete with screenshots, ensuring quality and transparency in the development process. With this comprehensive suite of tools, teams can significantly enhance their efficiency and collaboration.
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Bevel
Bevel serves as a vendor-neutral, Git-integrated control plane tailored for enterprise AI agents, allowing organizations to define their agents, context, skills, tools, permissions, and identities as owned files within their infrastructure, which can then be accessed by any agent runtime through MCP. The context is organized as typed knowledge nodes, each with documented provenance detailing its source, the last modification, and verification timestamps, and this information is compiled into a navigable graph that can be updated and utilized for creating dashboards. Skills are articulated as straightforward Markdown procedures, enabling process owners to easily read, review changes, and transfer them across different runtimes. Additionally, tool manifests outline the capabilities available, while sensitive information is stored securely in a vault, governed by access rules that dictate which agents can read certain files or invoke specific endpoints. Each agent is assigned a unique identity and credentials, ensuring that all actions can be traced back to their source. This comprehensive framework not only enhances security and organization but also promotes transparency and accountability in AI operations.
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