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What is Cisco AgenticOps?

AgenticOps introduces a groundbreaking methodology that is transforming IT operations in enterprises to meet the demands of an AI-focused future, leveraging AI agents to translate real-time data, automation, and extensive domain knowledge into intelligent, all-encompassing actions that oversee workflows across networking, security, and applications within a unified platform. At the heart of this advancement lies Cisco’s Deep Network Model, a specialized large language model shaped by over forty years of Cisco expertise, encompassing CCIE-level knowledge, educational resources from CiscoU, and hands-on operational experience, further refined through reinforcement learning, chain-of-thought reasoning, and test-time scaling to guarantee both precision and rapidity. This advanced engine powers AI Canvas, the inaugural generative user interface tailored specifically for IT operations across multiple domains, which integrates live telemetry data into an intelligent workspace. Users are equipped with the integrated Cisco AI Assistant, allowing them to communicate in natural language to troubleshoot issues, explore alternatives, pinpoint root causes, and implement corrective actions. The seamless amalgamation of these diverse functionalities not only boosts operational efficiency but also empowers teams to react promptly and effectively to emerging challenges. As a result, the synergy of these cutting-edge technologies is setting the stage for a more agile and responsive IT landscape, ultimately fostering a more proactive approach to managing enterprise operations.

What is Actionbook?

Actionbook is a groundbreaking open-source platform, crafted as an "action playbook," that provides AI agents with up-to-date manuals and a pertinent Document Object Model (DOM) structure, enabling them to engage with any website quickly and precisely without the necessity of analyzing entire pages or making assumptions about element selectors. This streamlined process greatly reduces execution time and minimizes token expenses while improving resilience against complex or dynamic web applications. By supplying pre-built action manuals with accurately verified selectors, it guarantees that agents consistently target the appropriate elements without consuming extensive context windows for page layout navigation. Furthermore, Actionbook is designed to work seamlessly with modern web technologies such as virtual DOMs, streaming components, and single-page applications, which eliminates the reliance on obsolete scraping methods or fragile automation strategies. The platform's innovative approach can result in a token consumption reduction of up to 100 times, significantly boosting agent performance and allowing it to integrate effortlessly with any large language model, agent framework, or browser automation tool. This adaptability not only improves efficiency but also encourages innovative solutions for managing and automating web interactions, paving the way for developers to explore new possibilities in their projects. Ultimately, Actionbook empowers users to harness the full potential of their existing technology stacks while optimizing web interaction processes.