AgentOps
A monitoring and observability platform for AI Agents that provides session tracing, cost analysis, and performance monitoring.
Timeline (last 90 days)
Existing AI observability tools on the market such as LangSmith and AgentOps focus on tracing and debugging, whereas AgentTrust focuses on interception.
MLOps focuses on the observability of model training, version management, and inference services, with the core object being stateless or lightly stateful inference endpoints; AgentOps focuses on agent processes that are stateful, long-running, and actively change the external environment.
AgentOps is not a replacement for MLOps, but a new layer of capability stacked on top of it: model serving is still managed by MLOps, while the Harness lifecycle, sandbox policy, and trajectory tracing are taken up by AgentOps.
The AgentOps operational paradigm encompasses key elements including observability, security guardrails, version management and rollback, human-in-the-loop collaboration, and fleet orchestration.
All Facts (5)
AI Agent的模型版本更新可能导致行为的静默变化（Silent Regression），这是传统软件运维体系完全没有对应工具的新问题
80%UnverifiedExisting AI observability tools on the market such as LangSmith and AgentOps focus on tracing and debugging, whereas AgentTrust focuses on interception.
50%UnverifiedMLOps focuses on the observability of model training, version management, and inference services, with the core object being stateless or lightly stateful inference endpoints; AgentOps focuses on agent processes that are stateful, long-running, and actively change the external environment.
50%UnverifiedAgentOps is not a replacement for MLOps, but a new layer of capability stacked on top of it: model serving is still managed by MLOps, while the Harness lifecycle, sandbox policy, and trajectory tracing are taken up by AgentOps.
50%UnverifiedThe AgentOps operational paradigm encompasses key elements including observability, security guardrails, version management and rollback, human-in-the-loop collaboration, and fleet orchestration.
50%