| name | powerbi-autonomous-forecast-agents |
| description | Use when coordinating multiple AI forecasting perspectives for Power BI sales forecasts, including backlog, seasonality, budget, sales skepticism, risk, consensus, dissent, and explainable forecast arbitration. |
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
- Load the latest
ai-forecast-summary.csv, ai-forecast-detail.csv, ai-forecast-top-deltas.csv, and ai-forecast-model-quality.csv when available.
- Run each agent independently against the same month and grain.
- Require every agent to provide:
- forecast direction: up, down, or hold
- evidence
- confidence
- top 3 risks
- recommended action
- 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.