Agentic Analytics vs. Decision-Making Skills
Agentic Analytics Answers When Asked. Fig Notices First.
Conversational BI and analytics agents wait for a question. Fig is the decision-making skills layer: it holds expectations for your key numbers, notices surprise unasked, gives every agent the same governed decision, remembers what happened, and acts within your rules.
Agentic Analytics Conversational BI · analytics agents | Fig Decision-making skills layer | |
|---|---|---|
| Natural-language questions over data | ||
| Governed definitions enforced on every agent | ||
| Causal map (what drives what) | ||
| Holds expectations (what's normal) | ||
| Notices surprise unasked + traces cascade | ||
| Remembers outcomes and learns | ||
| Acts within governed guardrails |
Fig notices before you ask.
Conversational BI and analytics agents are reactive — they answer when prompted. Nothing watches for the surprise that should have started the conversation.
Fig measures answers against what should be true.
Agentic analytics returns an answer to your question. Without an expectation tied to a forecast or goal, it can't tell you the answer is a problem.
Fig gives every agent the same governed decision.
When each agent reasons from raw data, different agents give different answers. Fig enforces your definitions and policies so the decision is consistent everywhere.
Fig learns from outcomes.
Most agentic analytics has no outcome feedback loop — it doesn't get more accurate as decisions play out. A decision-making skills layer consolidates what happened into sharper judgments.
Questions people ask
What is the difference between agentic analytics and a decision-making skills layer?
Agentic analytics (conversational BI and analytics agents) answers questions over your data on demand. A decision-making skills layer is proactive and governed: it holds expectations for your key numbers, notices surprise without being asked, traces the cause and who's affected, gives every agent the same governed decision, remembers outcomes, and acts within guardrails. Agentic analytics responds; a decision-making skills layer decides.
Don't analytics agents already make decisions?
They mostly make answers. An analytics agent can run multi-step queries and explain its logic, but without shared governed definitions, an expectation of what's normal, memory of outcomes, and a codified action boundary, it isn't making a governed, repeatable decision — it's producing a well-reasoned response to a prompt.
Can I use my analytics agents with a decision-making skills layer?
Yes. Fig works with any AI agent via the Model Context Protocol (MCP). Your agents call Fig to get the same governed definitions, expectations, and policies — so the decisions they produce are consistent and safe, not improvised per agent.
What does 'governed action' add over an agent that can act?
An agent that can act on model confidence alone is a risk. A decision-making skills layer puts every action through a deterministic boundary — policy compliance, authority limits, budget, and preview-or-undo — with a record of why it was allowed. That's what makes letting AI act safe at scale.
See what deciding — not just reporting — looks like
Connect your data and put governed decision-making in front of your whole team and every AI agent.