Data, BI and analytics

Data Agent

It can query sources, explain metrics, validate quality, prepare analysis and turn questions into traceable deliverables, focused on data. It works with authorized system context, connected tools and verifiable evidence before closing or escalating a case.

Supports the team in data: understands requests, checks sources, prepares recommendations and executes only actions allowed by permissions and policies.

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Signals, actions and outputs

This model keeps the agent from being just a conversation: it defines what it reads, what it can execute and what it leaves ready for the team.

Input signals

data requests or alerts
data operating data
data warehouse
official KPI definition

Connected tools

safe SQL lookup
dataset catalog
ETL monitor
commentary generator

Agent actions

Classifies the request within data and determines urgency, owner and confidence level.
Checks safe SQL lookup and dataset catalog before recommending or preparing an action.
Prepares drafts, tasks, alerts or updates so the team can act faster.
Hands off when confidence is low, there is financial impact, an external commitment, a policy exception or a decision requiring human approval.

Operating outputs

actionable data summary with cited sources
recommendation with confidence, owner and next step
evidence ready for review, audit or operational follow-up

How the agent operates

The cycle starts with context, applies rules, executes actions and ends with reviewable evidence.

Reads context

Checks authorized sources, messages, documents or process data.

Reasons with limits

Uses guardrails, thresholds and policies to prioritize and decide next steps.

Acts or escalates

Runs an automation, prepares an answer or assigns the case to an owner.

Leaves evidence

Stores summaries, decisions, errors, files and session traceability.

Operating governance

Guardrails

Human approval for critical changes, sensitive external messages or financial impact.
Role-limited access; every lookup and action is audited.
Mandatory escalation when sources are missing, confidence is low or a policy exception is detected.

Channels

BI portal
Slack/Teams
QAC Inbox

Human handoff

Hands off when confidence is low, there is financial impact, an external commitment, a policy exception or a decision requiring human approval.

Evidence

Each interaction can stay linked to session, execution, user, source consulted and proposed or executed action.

Applied real-world pattern

Inspired by real data agents that prepare datasets for AI, explain dashboards, validate quality and turn ambiguous requests into traceable analysis.

data warehouse

data catalog

Power BI/Tableau

ETL orchestrator

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Review Data Agent with a real process

We validate sources, permissions, available tools and escalation criteria before proposing the first deployment.

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