"AI agents combine language and reasoning models with the ability to take action through automations and APIs. Agent-to-agent protocols like the Model Context Protocol (MCP) enable integrations, making each agent discoverable and capable of orchestrating more complex operations. Many organizations will first experiment with AI agents embedded in their SaaS applications. AI agents in HR can assist recruiters with the hiring process, while AI agents in operations address complex supply-chain issues."
"AI agents are also transforming the future of work by taking notes, scheduling meetings, and capturing tasks in workflow tools. Innovative companies are taking the next steps and developing AI agents. These agents will augment proprietary workflows, support industry-specific types of work, and will be integrated into customer experiences. To develop these AI agents, organizations must consider the development principles, architecture, non-functional requirements, and testing methodologies that will guide AI agent rollouts."
"These steps are essential before deploying experiments or promoting AI agents into production. Rapidly deploying AI agents poses operational and security risks, prompting IT leaders to consider a new set of agentic operations practices. agenticops will extend devops practices and IT service management functions to secure, observe, monitor, and respond to AI agent incidents. Agenticops builds on several existing IT operational capabilities: AIops emerged several years ago to address the problem of having too many independent monitoring tools."
AI agents combine language and reasoning models with automations and APIs to take actions and orchestrate complex operations. Agent-to-agent protocols like the Model Context Protocol (MCP) enable integrations that make agents discoverable and able to coordinate. Organizations commonly embed agents in SaaS applications for functions such as HR recruiting, supply-chain operations, note-taking, meeting scheduling, and task capture. Innovative firms augment proprietary workflows and customer experiences with industry-specific agents. Development requires attention to principles, architecture, non-functional requirements, and testing before experiments or production. Rapid deployment introduces operational and security risks, motivating agenticops practices that extend devops, AIops, and modelops for securing, observing, and responding to incidents.
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