‍Why GenBI Needs a Different Playbook

Traditional BI is built for (tech-savvy) people to click and read. It lacks context and usually requires significant human effort from data analysts. GenBI adds agents on top of a data layer that decides what to look at, why things moved and what to do next. That means your success depends less on a clever model and more on three things:

1. Agent-friendly tools that do meaningful work in one step (e.g., “explain the spike” rather than “dump a million rows”).

2. Simple evaluation that proves you’re getting the right answers quickly, with receipts.

3. Guardrails so “insight to action” doesn’t create risk.

These three aspects allow you to go from WHAT to WHY to DONE in minutes instead of days.

Think of it as a reliable factory: deterministic data systems underneath, non-deterministic reasoning on top, and clear contracts in between.  

Principle 1: Build Tools for the Agent, Not Just APIs for Humans

Agents struggle with low-signal tools like list_tables or “export everything and I’ll figure it out.” Instead, offer high-signal, single-purpose tools mapped to real workflows:

• Explain a change: explain_metric_change(metric, period, dimensions) returns ranked drivers (e.g., “mobile web, UK, midnight to 2am”), plus quick evidence.

• Get business context: get_customer_context(account) stitches the relevant facts (usage trend, open tickets, last touch) into a tidy package.

• Trigger follow-through: schedule_followups(playbook, scope) runs a policy-approved sequence (Slack summary à PDF à CRM task) and returns a receipt.

Small design choices, clear names, simple parameters, predictable output, dramatically improve reliability and cost. The agent stays focused on decisions, not plumbing.  

Principle 2: Name Things So the Agent Can Choose Well

As you add tools, avoid a “bag of tricks”. Group them by domain so the right one is obvious:

• warehouse.explain_metric_change, warehouse.segment_users

• crm.find_account, crm.create_task

• actions.send_pdf, actions.post_slack_summary

• governance.request_approval

Consistent verbs and parameters prevent overlap and reduce failure modes. You’ll see better plans, shorter transcripts, and fewer retries.  

Principle 3: Return Meaningful Context, Not Everything You Can

Agents do better with interpretable facts than with cryptic IDs and raw dumps. Instead of returning primary keys and long logs, send back:

• A short narrative (“GGR up 14% driven by deposit velocity 3.1between 00:00–02:00, UK, mobile web”).

• A few evidence links (small charts or query receipts), not entire datasets.

• One or two next-best actions the agent can trigger safely.

Add a “concise vs detailed” option so the agent can control verbosity. This trims token usage and keeps things readable.  

Principle 4: Make Efficiency a Feature

Two budgets matter: time and compute. Users notice the first; your CFO notices the second.

• Favor filters, ranges, and top-k responses over full scans.

• Push work down to your query engine (predicate pushdown, column pruning).

• Shape error messages to guide the next call (“Pick ≤3 dimensions; try ['device','hour'].”).

• Log token/compute per task and alert on outliers.

Efficient tools are not “nice to have”. They make agents more accurate by keeping context tight and focused.  

Principle 5: Treat Tool Docs Like Product

Your tool’s description is part of the prompt. Write it like onboarding for a new teammate:

• What this tool is for, what it’s not for.

• Required vs optional inputs, with defaults.

• 1–2 good examples, 1 common pitfall, and one “if this then that” nudge (e.g., “Use explain_metric_change before requesting raw rows”).

• Expected output shape.

Small edits in tool docs often outperform big prompt hacks. Track changes with your evals (next).  

How to Know It Works: Build a Simple Evaluation Loop

You don’t need a research lab. You need 10–30 representative tasks that you can check automatically. For each:

• Write the prompt (“Explain yesterday’s revenue dip; send a summary and create tasks”).

• Define what “good” looks like (drivers present, evidence included, specific actions created).

• Confirm side-effects exist (file saved, Slack post made, CRM task ID).

• Track time-to-insight, time-to-action, tool errors, # tool calls, token/compute cost.

• Keep transcripts to review failures and rough edges.

Run this weekly. Tweak tool specs, parameters, or responses. Re-run. You’ll raise completion rates and lower cost in a steady, visible way.  

Example: iGaming, Game Integrity & Player Risk (From “What” to “Why” to “Done”)

Scenario. A game studio launches 2–3 new titles a month. Overnight, GGR spikes in the UK. The Integrity team needs three things fast: (1) a causal explanation, (2) a partner-ready PDF, and (3) operational follow-through in Slack/CRM, without waking an analyst.

Agent-friendly tools (plain English)

• Explain the spike: warehouse.explain_metric_change("GGR","yesterday",["hour","geo","device","partner"])

• Find the cohort: warehouse.segment_players(criteria)

• Campaign context (CRM): crm.list_active_campaigns(partner, window="last_7d") à name, channel, offer type, geo/segment

• Support context (CS): cs.list_tickets(partner_or_geo, window="last_7d", topic_filters=["payin","latency","bonus"]) à status, severity, tags

• Slack support context: slack.search_messages(channel="#support", query="brand:Brand X AND (deposit OR bonus OR outage)", window="last_48h") → top threads, links

• Summarize in Slack: actions.post_slack_summary(channel, summary, evidence_links)

• Create the PDF: actions.generate_player_assessment_pdf(cohort, window="24h")

• Make it owned: crm.create_partner_task(partner, title, due_date, attachments)

• Stay safe: governance.request_approval(action, scope, threshold)

A clean run looks like this

1. User: “Explain yesterday’s UK GGR spike; send a partner brief to Brand X.”

2. Agent calls explain_metric_change à returns: “Deposit velocity 3.1x baseline between 00:00–02:00, UK, mobile web; device fingerprint change; new IP range.”

3. Agent calls segment_players to define the affected cohort (IDs aliased to safe labels).

4. Agent enriches with context checks:

• CRM campaigns: Finds an active ‘Weekend Freespins Campaign’ promo targeting UK/mobile launched 20:00, UTM aligns with spike window.

• CS tickets: Detects 2 open tickets from UK brands opened 23:30–01:15.

• Slack support: Surfaces a thread in #support-brandX about bonus code misfire for Brand X at 00:40.

5. If outbound comms recipients > policy threshold, agent calls governance.request_approval.

6. On approval, the agent executes:

• Slack summary to #game-integrity: concise narrative + key drivers + context panel (campaign/tickets/Slack links) + confidence note.

• Player assessment PDF: includes drivers, cohort stats, campaign & CS context, recommended actions.

• CRM task on Brand X: “Review overnight GGR spike (campaign interaction + support noise)”; attaches PDF and Slack permalink.

What you measure

• Grounded accuracy: Drivers match warehouse truth; context links resolve (campaign ID, ticket IDs, Slack permalinks).

• Time-to-insight / Time-to-action: P95 ≤ 60s.

• Side-effects: Slack post, PDF in storage, CRM task ID present.

• Cost: token/compute within budget; extra context adds <20% overhead.

• Safety: approvals triggered when required; only allow-listed CRM fields updated; Slack search scoped to approved channels.

Guardrails that matter

• RBAC/tenancy: Read scopes for CRM/CS/Slack restricted by partner/region.

• Allow-lists: Which CRM objects/fields can be written; which Slack channels are searchable/postable.

• Approvals: Required for partner-facing PDFs or multi-recipient comms.

• Privacy: Cohort shown as aggregates + aliased IDs; PDFs watermarked; retention ≤90 days.

• Audit: Every tool call and write returns a receipt (who/what/when/diff) with artifact hashes.

• LLM policy: No training on customer data; ephemeral processing only.

Why this works

The agent doesn’t just shout “spike.” It explains the why, checks real-world context (campaigns, tickets, chatter) that often drives or distorts the numbers, and then closes the loop, Slack à PDF à CRM, under clear policy. That’s GenBI in production: fewer meetings, faster answers, and auditable actions with receipts.

Governance: Keep “Act” Safe and Boring

If your GenBI system can push changes into Slack, email, or CRM, make governance non-negotiable:

• RBAC and tenancy isolation: Who can read what; who can write where.

• Allow-lists: Only certain fields and destinations can be updated.

• Approvals: If a playbook touches many people or sensitive fields, require a human click.

• Undo & audit trail: Every write returns a receipt; every change has a diff; every action can be rolled back.

• Retention: Caches ≤ 30 days; logs ≤ 180; generated files ≤ 90; backups on a short rolling window.

• LLM policy: No training on customer data; ephemeral processing only; documented transfer safeguards.

These controls are easy to explain to security teams and keep everyone comfortable when you move from “insight” to “impact.”

Pitfalls to Avoid

• Tool sprawl: Ten similar tools confuse the agent. Start with a small, clear set and expand.  

• Row dumps as answers: Massive payloads raise cost and worsen explanations. Prefer summaries + evidence links.

• Ambiguous parameters: “user” vs “user_id” sounds minor but sinks success rates. Be explicit.

• Toy evaluations: Sandboxes that never send a PDF or create a task won’t reveal real failure modes. Add side-effects to your tests.  

• Action without policy: If the agent can email anyone or edit any field, you’ll create a new class of incidents. Guardrails first.

A Short Checklist to Ship Your First GenBI Playbook

Use case: “Revenue anomaly—minutes, not meetings.”

1. Pick the tools:

• warehouse.explain_metric_change (concise/detailed modes)

• actions.post_slack_summary, actions.send_pdf, crm.create_task

• governance.request_approval

2. Document them well: one-line purpose, inputs, outputs, two examples, one pitfall.

3. Create 10–20 eval tasks: different segments/time windows; verifiers check the narrative and the side-effects.

4. Add guardrails: RBAC, allow-lists, approvals for external comms, undo, audit.

5. Set targets: P95 time-to-insight ≤ 30s; time-to-action ≤ 60s; token/compute budgets per task.

6. Run weekly: review transcripts; refine tool specs; re-run; publish a tiny change log.

Do this once and you’ll feel the difference: fewer meetings about “what happened,” more “done” in the tools your teams already use.

The Big Idea

Generative BI is not “chat over data.” It’s explainable operations: find the signal, tell the why, and carry out the next step with clear, governed actions. Teams that win don’t chase prompts; they design a small set of agent-friendly tools, measure relentlessly, and put boring, strong guardrails around the “act.” Start with one playbook, prove it end-to-end, then scale.

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