AI Report Generation Agent

How It Works

Tell it your data sources, format and cadence. This generates a report agent spec the way an experienced automation strategist would build it - a real, usable deliverable, not a generic checklist. The output follows the standard: report-agent spec: data map, metric dictionary, template, schedule, delivery, QA gate. Replace every [[token]] with your specifics and it is ready to implement.

What to Provide

AI Report Generation Agent

This is the finished deliverable.

1. Data sources and connectors

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for data sources and connectors]]. Base it on your data sources and adjust as real cases come in.

Signal of expertise this section should show: KPI definitions pinned to source-of-truth.

Mistake this guards against: No metric definitions or data validation.

2. Report scope, sections, and required metrics/KPIs with definitions

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for report scope, sections, and required metrics/kpis with definitions]]. Base it on your data sources and adjust as real cases come in.

Signal of expertise this section should show: data-freshness/validation before generation.

Mistake this guards against: Static template with no anomaly logic.

3. Data-validation and freshness checks

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for data-validation and freshness checks]]. Base it on your data sources and adjust as real cases come in.

Signal of expertise this section should show: auto-narrative tied to thresholds (not generic).

Mistake this guards against: Ignoring scheduling/delivery and permissions.

4. Templating/layout

Define this concretely: [[narrative + tables + charts]] - spell out the actual rule, not just that one exists.

Signal of expertise this section should show: versioning and audit trail.

Mistake this guards against: No review gate.

5. Scheduling/cadence and triggers

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for scheduling/cadence and triggers]]. Base it on your data sources and adjust as real cases come in.

Signal of expertise this section should show: KPI definitions pinned to source-of-truth.

Mistake this guards against: No metric definitions or data validation.

6. Delivery channels and formats (PDF/HTML)

Cover each of these explicitly rather than leaving them implied: [[email]], [[Slack]], [[dashboard]]. Define [[the specific rule or default for email]] so nothing is left to guesswork.

Signal of expertise this section should show: data-freshness/validation before generation.

Mistake this guards against: Static template with no anomaly logic.

7. Anomaly callouts and auto-commentary logic

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for anomaly callouts and auto-commentary logic]]. Base it on your data sources and adjust as real cases come in.

Signal of expertise this section should show: auto-narrative tied to thresholds (not generic).

Mistake this guards against: Ignoring scheduling/delivery and permissions.

8. Access/permissions and audit logging

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for access/permissions and audit logging]]. Base it on your data sources and adjust as real cases come in.

Signal of expertise this section should show: versioning and audit trail.

Mistake this guards against: No review gate.

9. Human-review/approval gate

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for human-review/approval gate]]. Base it on your data sources and adjust as real cases come in.

Signal of expertise this section should show: KPI definitions pinned to source-of-truth.

Mistake this guards against: No metric definitions or data validation.

Worked Examples

Example 1 - Finance team monthly board deck

Inputs: NetSuite + Stripe, PDF+Slack, monthly cadence

Result: Auto-narrative flagged a 14% MRR dip driver automatically instead of the analyst hunting for it.

Example 2 - Marketing weekly performance digest

Inputs: GA4 + ad platforms, HTML email, weekly

Result: Freshness checks caught a broken GA4 connector before a stale report went to leadership.

Format Checklist

Common Mistakes to Avoid

• No metric definitions or data validation.
• Static template with no anomaly logic.
• Ignoring scheduling/delivery and permissions.
• No review gate.

Next Steps After You Generate This

Week 1: pilot with a small internal group or a single channel/segment. Week 2: review real output against the format checklist below and fix the top 2-3 gaps. Weeks 3-4: expand scope and set a recurring review cadence so the workflow stays accurate as your data and process change.

This deliverable gives you a working starting point on day one - keep the [[tokens]] current as your process, tools, and volume change.