Use agent observability to explain the cost of a finished task

The incident summary costs less to generate, but the engineer spends longer checking it. A missing fact sends them back to the timeline; an unsupported claim needs repair; a failed tool step needs another attempt. In this illustrative workflow, lower token spending has not yet established lower effort.

Splunk’s September release notes list AI Token and Cost Monitoring on September 10 and hosted Agent Observability integrated with Observability Cloud on September 15. The latter combines observability, evaluation and guardrails, with access through the customer’s Splunk team.

Start the demonstration with the rejected summary

Cisco’s September 15 announcement describes Tokenomics for attributing coding-agent usage and cost alongside evaluation and behavioral controls. These are vendor capability descriptions, not measured benefits for your workload.

Ask the demonstration to open a rejected or unresolved summary. Which model attempts produced it? Which tools ran? What did a reviewer change? Following that record shows which activities are included, excluded or disconnected, questions a tidy successful trace may never exercise.

Connect a task to its cost and result

Read diagram description

A proposed investigation path: correlate the task with model calls, tool activity and the final result. Review time is additional evidence to collect; token spending alone cannot establish whether the task saved work. Diagram labels: Agent task: Retain one task identifier; Model calls: Tokens and model charges; Tool activity: Attempts and failures; Task result + review: Useful completion, corrections and total effort.

Connect the attempts to the summary people use

Keep each model and tool attempt attached to the requested update. Charges per attempt measure one thing; all charges divided by accepted summaries measure another. Three rejected drafts followed by a usable fourth are one completed summary with four attempts behind it.

The records need both views: attempt model, duration, charge and result, then task acceptance and correction. Keep human effort beside money in its own unit. The token-usage guide develops that accounting.

The same discipline helps when a summary is later written to an incident record under separate authorization. If that write times out, inspect the destination before repeating it. Connect its cost to the original task and keep its outcome unknown until resolved. It is not another accepted summary, nor merely a failed generation.

Give drafts and writes different failure paths

An incorrect claim and an unknown write need different repairs. A draft may remain useful to a human even if evaluation is unavailable, provided it is marked unevaluated. A consequential write may need to wait for its required check.

The production-agent article connects that behavior to authority. Combining writing quality and write success in one efficiency score would hide the very consequences that distinguish them.

Compare the trace with the records it claims to explain

Use a repeatable workflow with known cases: a wrong answer, a tool timeout and an interrupted evaluation. Keep model and inputs fixed while assessing instrumentation, so changed work does not masquerade as improved visibility. This is a proposed trial, not a Splunk benchmark.

Have the people using the updates define acceptance. Compare captured attempts with provider usage records, accounting for reporting lag and pricing differences. A mismatch deserves an accounting investigation before being announced as a saving.

Include the people maintaining this connection. Configuration, missing attempts and mismatched charges create ongoing work. Judge the product against the completed task and the effort needed to accept it, not only the clarity of its spending chart.

Finally, give the records to someone who did not run the trial. Can they explain one rejected summary and one uncertain update, including sources, attempts, charges and unfinished work? If so, there is a concrete benefit to weigh against upkeep: the next engineer can investigate the cheaper summary without rebuilding its history from memory.