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Agent Ops AI Operations Lifecycle Management Infrastructure

[PDF] Architecting AgentOps Needs CHANGE

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Agentic AI systems have outpaced architectural thinking required to operate them effectively. These agents differ fundamentally from traditional software: their behavior is not fixed at deployment but continuously shaped by experience, feedback, and context. Traditional DevOps or MLOps principles assume system behavior can be managed through versioning, monitoring, and rollback. This assumption breaks down for Agentic AI systems whose learning trajectories diverge over time, introducing non-determinism that makes system reliability challenging at runtime. CHANGE is a conceptual framework comprising six capabilities for operationalizing Agentic AI systems: Contextualize, Harmonize, Anticipate, Negotiate, Generate, and Evolve. CHANGE provides a foundation for architecting an AgentOps platform to manage the lifecycle of evolving Agentic AI systems.

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