01
What these agents do
Analytics agents monitor metrics, detect anomalies, explain movements in plain language, compare cohorts, and produce forecasts. Assistive ones surface findings; autonomous ones refresh and publish forecasts on their own.
02
Genuinely good at
- Watching more metrics, more often, than any analyst team
- Turning a warehouse query into a readable narrative
- Surfacing an anomaly quickly enough to matter
- Consistent, repeatable scenario modelling
03
Genuinely bad at
- Sparse data, where the explanation will still sound certain
- Causation. It will find correlation and describe it fluently.
- Working without a well-defined semantic layer
- Knowing that an upstream pipeline broke rather than that demand fell
04
The risks that matter
- Reliability: a plausible explanation is more dangerous than an obviously wrong one.
- Decision risk: forecasts published without review get treated as facts.
- Access: warehouse credentials are the widest data access most organisations grant.
- Cost: query-generating agents can be expensive in ways that surface a month later.
05
How to evaluate one responsibly
- Ask what happens when the data is thin — does it abstain or does it answer?
- Test it against a movement you already understand.
- Confirm the credentials are read-only.
- Ask about query cost controls before, not after, the pilot.
Now compare what is actually declared.
The registry holds each agent’s stated facts — autonomy, oversight, compliance, residency, sustainability disclosure — with provenance on every field.
Compare analytics agentsContinue