AI AGENTS

Cost-Anomaly Evidence Bundle Builder

Deterministically collects the cost breakdown, deploy log, and infra metrics for any detected spike into a single timestamped evidence record stored in Snowflake for later audit…

CategoryAI Agents
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled baseline scan
  • LogicFilter services exceeding baselineSnowflakeSnowflake
  • ActionPull infra metrics for windowDatadogDatadog
  • ActionFetch deploys and MRs for windowGitLabGitLab
  • OutputWrite timestamped evidence record to SnowflakeSnowflakeSnowflake

What it does

Builds a durable, queryable record for every cost anomaly. Rather than reasoning about a cause, this pipeline reliably gathers the three inputs an investigation needs — the cost breakdown, the deploy and MR log, and the relevant infra metrics for the window — normalizes them, and writes one timestamped evidence row back to Snowflake. Later anomalies and audits can query a clean history instead of re-pulling raw APIs.

When to use it

Use this as the data-collection backbone under your cost investigations, or when finance needs an auditable trail of what was happening during each spike. It is deterministic by design, so the same anomaly always produces the same bundle.

How it works

  1. 1A schedule scans Snowflake for any service exceeding its cost baseline.
  2. 2For each flagged service, the pipeline pulls infra metrics for the anomaly window from Datadog.
  3. 3It fetches deploys and merged MRs in that window from GitLab.
  4. 4It normalizes all three sources into a single structured bundle.
  5. 5It writes the timestamped evidence record back to a Snowflake table for audit and downstream agents.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SnowflakeWarehouses, queries, shares.
  2. 2
    Connect DatadogMetrics, traces, log search.
  3. 3
    Connect GitLabRepos, MRs, pipelines, registry.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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