AgentOps is the operating discipline for autonomous AI agents: lifecycle management, observability, evaluation, governance, cost control, and runtime reliability. The first training track focuses on OpenTelemetry for agents.
AgentOps is the emerging practice for lifecycle management of autonomous AI agents, bringing DevOps and MLOps-style management, monitoring, and improvement into agentic pipelines.
IBM defines AgentOps as an emerging discipline for managing, monitoring, and improving agentic development pipelines. Red Hat independently frames AgentOps around observing, evaluating, governing, and optimizing agentic systems. Academic literature uses AgentOps as the discipline name for tracing, monitoring, logging, and analytics for agent safety in production.
The phrase is not theoretical. Multiple enterprise vendors are naming it, tooling is being built around it, and the operational motions it describes — trace what the agent did, catch failures, measure cost, govern access — are already required in every production agent deployment.
From prototype to production: deployment, versioning, rollback, and shutdown controls for autonomous agent systems.
Traces, logs, and metrics across agent loops, tool calls, model requests, and infrastructure — without modifying agent code.
Access controls, policy boundaries, human handoff triggers, audit trails, and incident review for production agent workloads.
A production AI agent is a loop: model call → tool selection → tool execution → output → next model call. Every leg of that loop can fail, drift, or cost more than expected. Without observability, you cannot see where.
The questions that matter in production:
Which MCP tools, which sequences, which failed, which were called unexpectedly.
Model request latency vs tool execution vs orchestration overhead. Which step is the bottleneck.
Token spend per agent run, per tool call, per user. Where costs are growing unexpectedly.
Errors, timeouts, unexpected tool outputs, policy violations, and loop termination events.
OpenTelemetry (OTel) is the common language between agent behavior and production infrastructure. It is not specific to AI — it is the same telemetry standard that runs in distributed microservices at scale. AgentOps brings it to the agent layer.
A practical training path for running AI agents in production. Modules focus on the operational skills required to instrument, monitor, debug, and govern autonomous agent systems.
For engineers, automation leads, AI ops teams, and operators building production agent workflows.
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