How deeply does AI Agents in Action cover AgentOps?

Provides installation, API-key configuration, initialization, dashboard inspection, and repetition diagnosis, without detailing platform internals.

AgentOps

As explained in AI Agents in Action

AgentOps is a platform for monitoring, tracing, and analyzing AI-agent executions, including their costs and behavior.

AgentOps is an observability platform for monitoring AI-agent executions, interactions, performance, repetition, and cost. It is especially useful when multi-agent behavior becomes difficult to inspect through application tracing alone. section 4.4.2 section 4.7

A basic CrewAI integration follows three steps:

Create an AgentOps project, obtain an API key, and place it in the project’s .env file. section 4.4.2
Import AgentOps, load environment variables, initialize it, and run the CrewAI script. section 4.4.2

Inspecting an agent run

After running a CrewAI joke crew, the dashboard can show total duration, run environment, prompt and completion tokens, LLM-call timings, and estimated cost. A coding crew can also reveal repeated thoughts, indicating that agents may be cycling through the same actions. section 4.5

How AI Agents in Action covers it

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Related concepts

monitoringhas part · Monitoring is the ongoing observation and measurement of a system’s behavior, state, or operational signals to assess its condition and detect changes or problems.
CrewAIuses · CrewAI is an open-source framework for building and orchestrating collaborative AI agents and multi-agent workflows.
AI agentuses · An AI agent is a computational system that uses artificial intelligence to interpret information, select actions, and pursue goals through interaction with an environment.
cause analysishas part · Cause analysis is a systematic investigation of the underlying factors that produced a problem, failure, incident, or other outcome.
anomaly detectionhas part · Anomaly detection is the identification of observations, events, or behaviors that substantially deviate from expected patterns or a normal baseline.

This explainer was generated by Manning's knowledge graph from the text of AI Agents in Action. The section links point to the parts of the book it draws on.