AI AGENTS
Noise Pattern Knowledge Base Builder
On demand or schedule, an agent clusters recent Axiom log patterns and upserts each into a Postgres catalog with its disposition.
How it runs
The automated pipeline, trigger to output.
- TriggerManual run or schedule
- ActionQuery and cluster recent Axiom logsAxiom
- LogicFingerprint clusters, lookup in Postgres catalogPostgres
- ActionLLM proposes disposition for unknown patternsOpenAI
- OutputUpsert catalog and post Slack summarySlack
What it does
This agent builds and maintains the institutional memory that other log-triage workflows lean on. It clusters recent Axiom log patterns, looks up each cluster's fingerprint in a Postgres catalog, and records new ones with a proposed disposition (known noise, watch, alert). Over time the catalog learns the service's normal chatter so repeat patterns are classified instantly.
When to use it
Use it to seed and grow a reusable noise dictionary, especially before rolling out automated suppression or alert-promotion agents that need a baseline of what is already understood.
How it works
- 1A manual run or schedule triggers the build.
- 2The agent queries Axiom for the recent window's log events and clusters them into normalized patterns.
- 3A logic step computes a stable fingerprint per cluster and checks it against the Postgres catalog.
- 4For unknown patterns an LLM step proposes a disposition and human-readable description.
- 5It upserts each pattern with counts and disposition into Postgres and posts a Slack summary of what was newly catalogued.
Set it up
What you configure once, before turning it on.
- 1Connect AxiomLog streams, queries, dashboards.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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