Powerbi Autonomous Forecast Agents

Power BI Autonomous Forecast Agents

Use this skill when the user wants an agent council rather than one blended forecast. The goal is a structured debate that exposes why the forecast is high, low, risky, or trustworthy.

Agent roles

• Backlog Agent: trusts open orders, delivery dates, status, order age, and learned conversion.
• Demand Agent: trusts seasonality, trend, intermittent demand, and customer/product history.
• Budget Defense Agent: explains the gap to budget and roll forecast.
• Sales Skeptic Agent: searches for optimism bias, volatile customers, sparse products, and weak evidence.
• Risk Agent: flags data quality, missing snapshots, memory fallback, biased backtests, delivery risk, and low-confidence matching.
• Arbitrator Agent: creates the final consensus, dissent, confidence, and recommended action.

Workflow

1. Load the latest ai-forecast-summary.csv, ai-forecast-detail.csv, ai-forecast-top-deltas.csv, and ai-forecast-model-quality.csv when available.
2. Run each agent independently against the same month and grain.
3. Require every agent to provide:
   • forecast direction: up, down, or hold
   • evidence
   • confidence
   • top 3 risks
   • recommended action
4. The arbitrator produces:
   • consensus forecast
   • dissenting views
   • final risk flag
   • whether AI forecast can replace, challenge, or only annotate the roll forecast

Output columns

• forecast_month
• grain
• customer
• product
• agent_name
• agent_forecast
• agent_confidence
• evidence
• risk_flag
• recommended_action
• arbitrated_decision

Guardrails

• If model quality is worse than roll forecast, mark the result advisory_only.
• Do not hide dissent. Dissent is the value of the council.
• Use concrete customer/product/month evidence, not generic commentary.