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Observability

As explained in AI Agents in Action

Observability is the collection and analysis of execution signals that reveal an agent system's behavior, performance, and failures.

Observability collects execution signals to reveal how agent systems behave, perform, and fail. Application tracing is especially valuable for complex multi-agent systems. section 4.4.2

A practical AgentOps setup follows these steps:

  • Create an AgentOps project, generate an API key, and store it in the project’s .env file. section 4.4.2

Inspecting a running crew

After running a joke-generating crew, the AgentOps dashboard exposes agent interactions, total duration, environment, prompt and completion tokens, LLM call timings, and estimated cost. section 4.4.2

How AI Agents in Action covers it

How deep it goes
Walks through installation, API-key configuration, initialization, execution, and dashboard inspection without developing tracing internals.
Examples
Uses a joke-generating crew and customer-review sentiment analysis to demonstrate dashboard monitoring and error detection.
What it assumes
Assumes a CrewAI multi-agent script and teaches AgentOps installation, credentials, initialization, and dashboard use.
What you can do after
Operate/configure AgentOps in CrewAI and evaluate agent performance, cost, outputs, inefficiencies, and interaction errors.
How it connects
Connects observability with application tracing, multi-agent systems, CrewAI, and AgentOps.

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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.