The DevOps concept emerged in 2007-2008 due to frustration with the friction between software developers and IT operations teams. Developers were under pressure to release new features quickly, while operations teams prioritized system stability and reliability. This led to a siloed and inefficient process in which developers often “threw code over the wall” to operations. Deployment was often delayed, and bugs frequently emerged when software was put into production.
DevOps breaks down these silos, leading to a more agile, efficient and stable workflow. The result is faster, more frequent releases, higher quality and more reliable software, and reduced operational costs through automation.
Fast forward almost 20 years. DevOps has proven so successful that it has been expanded to embrace new paradigms. XOps is a broad term for an operational approach that extends the principles of DevOps to various specialized fields by breaking down silos and promoting collaboration.
Two of the latest entries in the XOps lexicon are MLOps and AgentOps. As the names suggest, they are concerned with operationalizing the deployment of machine learning and agentic AI.
MLOps applies DevOps principles to machine learning workflows. It combines machine learning, data engineering and operations to automate the deployment of ML models. The goal is to streamline the entire lifecycle, from development to deployment and maintenance, making the process more efficient, reliable and scalable.
Like DevOps, MLOps automates manual tasks and applies CI/CD practices to testing and deployment. Automating various stages in the ML lifecycle, including data preparation, model training, validation and deployment, helps ensure consistency, repeatability and efficiency. MLOps also fosters collaboration between data scientists and operations teams to align models with business requirements.
A key difference between DevOps and MLOps is the role of data and experimentation. While DevOps focuses on automating the delivery of stable software, MLOps must handle the experimental nature of data and the need to constantly adapt and fine-tune models alongside the data used to train them.
As a result, MLOps practices include monitoring of model performance to detect data drift (changes in the statistical properties of input data) and concept drift (changes in the relationship between input data and the target variable). MLOps also manages resources to ensure that models can be scaled to growing demands.
AgentOps refers to an operational framework and set of practices used to safely and reliably deploy, monitor and manage autonomous AI agents. It extends the concepts of DevOps and MLOps to address the unique challenges of agentic AI.
Agentic AI is more complex and dynamic than static software or even machine learning. Agent behavior can be unpredictable, creating the need for continuous evaluation, logging and governance. The autonomous nature of AI agents raises questions about accountability for their decisions and actions, particularly in high-risk use cases.
AgentOps focuses on bringing operational discipline to chaotic agentic systems, managing issues such as unexpected costs, runaway loops, and concerns about bias and privacy. It also implements guardrails to prevent unwanted actions and manage access to data.
A key component of AgentOps is observability, which provides visibility into an agent’s performance, decision-making and interactions to ensure quality and safety, control costs, and minimize latency. Orchestration tools manage the complex interactions between multiple agents and external systems to ensure they work together efficiently. With adaptive learning, AI agents use feedback and data from the production environment to continuously refine their performance and decision-making capabilities without manual retraining.
Technologent recognizes that machine learning and agentic AI introduce new IT operational challenges. We have a long history of helping organizations leverage DevOps tools and practices, and that experience informs our AI development and deployment practices. Let us help you leverage MLOps and AgentOps to ensure the safety, reliability and long-term value of your AI investments.