SUMMARIZATION

On-Demand Axiom Cost-Attribution Chat Agent

An agent that answers ad-hoc questions like 'which service drove our log spend last Tuesday?' by querying Axiom live, attributing cost by service.

CategorySummarization
Enginepaperclip
Difficultyadvanced
Triggerchat
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerChat question about log cost
  • LogicParse time window; ask to clarify if vague
  • ActionQuery Axiom: ingest by service for windowAxiom
  • ActionAttribute cost shares and flag outliersOpenAI
  • OutputReply in chat with ranked breakdownOpenAI

What it does

Lets anyone ask, in plain language, where Axiom log spend went over an arbitrary window. The agent interprets the question, builds the appropriate Axiom query, attributes ingest cost per service, and replies in chat with a ranked breakdown and a one-paragraph explanation. No dashboard navigation required.

When to use it

Use it for the irregular, investigative questions a fixed weekly digest can't answer: a finance lead asking about a specific day, or an engineer checking whether yesterday's deploy moved the bill. It complements scheduled reports rather than replacing them.

How it works

  1. 1A chat message triggers the agent with a natural-language cost question.
  2. 2The agent parses intent and time window, then calls Axiom to aggregate ingest and events by service for that range.
  3. 3A logic step decides whether the window is well-defined or needs a clarifying reply.
  4. 4The agent computes per-service cost shares and identifies outliers.
  5. 5It composes a ranked, plain-English answer and returns it in the same chat thread.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AxiomLog streams, queries, dashboards.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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