LLM Observability for Agentic Workflows

Quick answer

Agent observability should track model, tokens, retries, tool calls, phase, artifact progress, and final outcome. Total spend alone is too late and too vague.

Provider dashboards stop at the wrong layer

OpenAI, Anthropic, and Google can show account-level usage. They usually cannot tell you that a content workflow spent 70 percent of its budget re-reading the same files or that a fallback model handled routine retries.

Workflows need semantic cost labels

Label the run by product, phase, trigger, and expected artifact. Then cost review can answer the useful question: did this spend produce the thing the workflow promised?

Tool loops are the expensive failure

A model that repeatedly calls the same search, browser, or test command may look active while making no progress. Observability should flag repeated tool sequences and growing context with no changed output.

How Clawback helps

Clawback maps LLM spend back to agent workflows so teams can tune models, retries, and prompts where the waste actually happens.

FAQ

Is token count enough?

No. You need outcome and workflow context to know whether the tokens were useful.

What is the most useful label?

Phase. Research, drafting, validation, and closeout fail in different ways.

Should observability block runs?

It should block or pause repeated identical failures, runaway retries, and spend beyond the phase budget.