AgentOps: Operationalize AI Agents
Operationalizing AI agents involves moving them from prototype to production, ensuring they are cost-effective, observable, and maintainable in live environments.
Join Sokratis Kartakis and Sita Lakshmi Sangameswaran to dive into the essential practices for operationalizing Generative AI Agents.
This comprehensive guide introduces AgentOps, building upon the foundations of DevOps, MLOps, and GenAIOps to tackle the unique challenges of managing AI agents.
AI agents are autonomous programs powered by large language models (LLMs) that can handle tasks ranging from customer service to data analysis and supply chain optimization.
These agents are transforming industries by automating repetitive processes, personalizing user interactions, and enabling data-driven decision-making. However, deploying AI agents at scale presents challenges such as managing costs, ensuring reliability, debugging complex interactions, and maintaining compliance with enterprise standards.
Operationalizing AI agents involves moving them from prototype to production, ensuring they are cost-effective, observable, and maintainable in live environments. This requires tools that provide granular insights into agent performance, seamless integration with existing systems, and flexible frameworks for building multi-agent architectures.
Google’s ADK and AgentOps address these needs by offering a code-first approach to agent development and comprehensive observability for production-grade deployments.
