| name | quota-methodology |
| version | 1.0.0 |
| description | Versioned quota-estimation methodology for Wide World Importers customers. Encodes the exact, deterministic growth-rate model the quota estimator implements so the same logic can be reused across the Copilot CLI, a Foundry agent system prompt, or a Databricks Supervisor tool. |
| owner | agent-demo-dev |
| last_reviewed | "2026-06-15T00:00:00.000Z" |
| tools | ["wwi-sales-data","researcher-agent","workiq","quota-estimator"] |
Quota Estimation Methodology (v1.0.0)
This is a versioned skill: a single source of truth for how a Wide World Importers (WWI) quota is
estimated. The CLI skill, the Foundry agent prompt, and the Databricks Supervisor uc_function should all
produce the same numbers because they all follow the formula below. Bump version whenever the formula,
bounds, or scenario adjustments change, and update last_reviewed.
The reference implementation lives in src/agents/quota_estimator/pipeline.py. Keep this file in sync with it.
Intent
Generate a fiscal-year quota recommendation per (territory, product category) group for one customer,
grounded in trailing sales, market research, and engagement signals, under a named scenario. Produce real
artifacts (.xlsx, .html, .pdf), not just inline prose.
Inputs
| Parameter | Required | Notes |
|---|
customer_name | yes | e.g. Tailspin Toys. |
sales_rows | yes | Trailing rows with territory, category, order_date, revenue, quantity. |
research_data | no | Market context; a growth_rate / key_metrics.* hint feeds the market adjustment. |
workiq_activity | no | Engagement score + activity count; mock when no credentials. |
scenario | no | One of conservative, base (default), aggressive. |
data_source | no | fabric or databricks; selects the citation/query surface only. |
Methodology (the formula)
For each (territory, category) group:
trailing_revenue = sum of revenue in the group.historical_growth_rate = bounded trailing trend: split the rows at their date midpoint, compute
(recent_revenue - previous_revenue) / previous_revenue, then clamp to [-0.20, 0.30]. With fewer
than two rows or a single date the trend is 0.0. If previous_revenue <= 0, use 0.05 when there is
recent revenue, else 0.0.market_adjustment = clamp(growth_rate_hint / 4, -0.03, 0.05) (0.0 when no hint).engagement_adjustment = clamp(score_adjustment + volume_adjustment, -0.03, 0.05) where
score_adjustment is very high +0.04 / high +0.03 / medium / moderate +0.01 / low -0.02 / none -0.03
(unknown 0.0) and volume_adjustment = min(activity_count, 6) * 0.002.scenario_adjustment = conservative -0.03 / base 0.0 / aggressive +0.03.recommended_growth_rate =
clamp(0.04 + 0.5 * historical_growth_rate + market_adjustment + engagement_adjustment + scenario_adjustment, -0.05, 0.25).recommended_quota = trailing_revenue * (1 + recommended_growth_rate).
The 0.04 base, the 0.5 trend weight, every clamp bound, and the scenario/engagement tables are the
contract. Do not invent alternative numbers — that is the whole point of versioning this skill.
Steps
- Query sales via
wwi-sales-data (Fabric Data Agent MCP) — or the Databricks Genie equivalent when the
customer is on the Databricks path. Return at least territory, category, order date, revenue, quantity.
- Gather market context via
researcher-agent.research_company. If live search is unavailable, accept the
mock response and say so.
- Gather engagement via the configured WorkIQ tool, or synthesize realistic activity with an engagement
score. Never call real WorkIQ APIs without credentials.
- Generate artifacts by calling
quota-estimator.generate_quota_estimation_report with customer_name,
sales_rows, research_data, workiq_activity, scenario, and formats: ["xlsx", "html", "pdf"].
- Return an executive summary, the chosen scenario, the total recommended quota, and the artifact paths.
Output contract
Per group, surface trailing_revenue, recommended_growth_rate, recommended_quota, and a one-line rationale
of the form:
<territory> / <category>: trend <h>%, market adjustment <m>%, engagement adjustment <e>%, <scenario> scenario adjustment <s>%.
Always include the data-source citation, research citations, and a WorkIQ context line so the recommendation is
auditable.
Scenario selection hint
Infer scenario from intent: "stretch" / "upside" maps to aggressive; "downside" / "floor" maps to
conservative; otherwise base.
Changelog
- 1.0.0 (2026-06-15) — Initial versioned methodology extracted from
quota_estimator/pipeline.py.
