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Zhongwei Li
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# Phase 2: Analysis Design
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## Objective
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**DEFINE** autonomously which analyses the agent will perform and how.
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## Detailed Process
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### Step 1: Brainstorm Use Cases
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From the workflow described by the user, think of typical questions they will ask.
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**Technique**: "If I were this user, what would I ask?"
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**Example (US agriculture)**:
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User said: "download crop data, compare year vs year, make rankings"
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**Questions they likely ask**:
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1. "What's the corn production in 2023?"
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2. "How's soybean compared to last year?"
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3. "Did production grow or fall?"
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4. "How much did it grow?"
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5. "Does growth come from area or productivity?"
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6. "Which states produce most wheat?"
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7. "Top 5 soybean producers"
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8. "Did the ranking change vs last year?"
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9. "Production trend last 5 years?"
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10. "Forecast for next year?"
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11. "Average US yield"
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12. "Which state has best productivity?"
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13. "Did planted area increase?"
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14. "Compare Midwest vs South"
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15. "Production by region"
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**Objective**: List 15-20 typical questions
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### Step 2: Group by Analysis Type
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Group similar questions:
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**Group 1: Simple Queries** (fetching + formatting)
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- Questions: 1, 11, 13
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- Required analysis: **Data Retrieval**
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- Complexity: Low
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**Group 2: Temporal Comparisons** (YoY)
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- Questions: 2, 3, 4, 5
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- Required analysis: **YoY Comparison + Decomposition**
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- Complexity: Medium
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**Group 3: Rankings** (sorting + share)
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- Questions: 6, 7, 8
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- Required analysis: **State Ranking**
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- Complexity: Medium
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**Group 4: Trends** (time series)
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- Questions: 9
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- Required analysis: **Trend Analysis**
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- Complexity: Medium-High
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**Group 5: Projections** (forecasting)
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- Questions: 10
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- Required analysis: **Forecasting**
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- Complexity: High
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**Group 6: Geographic Aggregations**
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- Questions: 12, 14, 15
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- Required analysis: **Regional Aggregation**
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- Complexity: Medium
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### Step 3: Prioritize Analyses
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**Prioritization criteria**:
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1. **Frequency of use** (based on described workflow)
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2. **Analytical value** (insight vs effort)
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3. **Implementation complexity** (easier first)
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4. **Dependencies** (does one analysis depend on another?)
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**Scoring**:
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| Analysis | Frequency | Value | Ease | Score |
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|---------|------------|-------|------------|-------|
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| YoY Comparison | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | 9.3/10 |
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| State Ranking | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | 9.3/10 |
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| Regional Agg | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | 8.0/10 |
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| Trend Analysis | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | 7.3/10 |
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| Data Retrieval | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | 8.3/10 |
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| Forecasting | ⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ | 5.3/10 |
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**DECISION**: Implement top 5
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1. YoY Comparison (9.3)
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2. State Ranking (9.3)
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3. Data Retrieval (8.3)
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4. Regional Aggregation (8.0)
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5. Trend Analysis (7.3)
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**Don't implement initially** (can add later):
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- Forecasting (5.3) - complex, occasional use
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### Step 4: Specify Each Analysis
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For each selected analysis:
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```markdown
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## Analysis: [Name]
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**Objective**: [What it does in 1 sentence]
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**When to use**: [Types of questions that trigger it]
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**Required inputs**:
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- Input 1: [type, description]
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- Input 2: [type, description]
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**Expected outputs**:
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- Output 1: [type, description]
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- Output 2: [type, description]
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**Methodology**:
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[Explanation in natural language]
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**Formulas**:
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```
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Formula 1 = ...
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Formula 2 = ...
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```
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**Data transformations**:
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1. [Transformation 1]
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2. [Transformation 2]
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**Validations**:
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- Validation 1: [criteria]
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- Validation 2: [criteria]
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**Interpretation**:
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- If result > X: [interpretation]
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- If result < Y: [interpretation]
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**Concrete example**:
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Input:
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- Commodity: Corn
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- Year current: 2023
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- Year previous: 2022
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Processing:
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- Fetch 2023 production: 15.3B bu
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- Fetch 2022 production: 13.7B bu
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- Calculate: (15.3 - 13.7) / 13.7 = +11.7%
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Output:
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```json
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{
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"commodity": "CORN",
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"year_current": 2023,
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"year_previous": 2022,
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"production_current": 15.3,
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"production_previous": 13.7,
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"change_absolute": 1.6,
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"change_percent": 11.7,
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"interpretation": "significant_increase"
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}
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```
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Response to user:
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"Corn production grew 11.7% in 2023 vs 2022 (15.3B bu vs 13.7B bu)."
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```
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### Step 5: Specify Methodologies
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For quantitative analyses, detail methodology:
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**Example: YoY Decomposition**
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```markdown
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### Growth Decomposition
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**Objective**: Understand how much of production growth comes from:
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- Planted area increase (extensive)
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- Productivity/yield increase (intensive)
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**Mathematics**:
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Production = Area × Yield
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Δ Production = Δ Area × Yield(t-1) + Area(t-1) × Δ Yield + Δ Area × Δ Yield
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Interaction term usually small, so approximation:
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Δ Production ≈ Δ Area × Yield(t-1) + Area(t-1) × Δ Yield
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**Percentage contributions**:
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Contrib_Area = (Δ Area% / Δ Production%) × 100
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Contrib_Yield = (Δ Yield% / Δ Production%) × 100
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**Interpretation**:
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- Contrib_Area > 60%: **Extensive growth**
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→ Area expansion is main driver
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→ Agricultural frontier expanding
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- Contrib_Yield > 60%: **Intensive growth**
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→ Technology improvement is main driver
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→ Productivity/ha increasing
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- Both ~50%: **Balanced growth**
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**Validation**:
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Check: Production(t) ≈ Area(t) × Yield(t) (margin 1%)
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Check: Contrib_Area + Contrib_Yield ≈ 100% (margin 5%)
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**Example**:
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Soybeans 2023 vs 2022:
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- Δ Production: +12.4%
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- Δ Area: +6.1%
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- Δ Yield: +7.6%
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Contrib_Area = (6.1 / 12.4) × 100 = 49%
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Contrib_Yield = (7.6 / 12.4) × 100 = 61%
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**Interpretation**: Intensive growth (61% from yield).
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Technology and management improving.
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```
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### Step 6: Document Analyses
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Save all specifications in `references/analysis-methods.md` of the agent.
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## Phase 2 Checklist
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- [ ] 15+ typical questions listed
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- [ ] Questions grouped by analysis type
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- [ ] 4-6 analyses prioritized (with scoring)
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- [ ] Each analysis specified (objective, inputs, outputs, methodology)
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- [ ] Methodologies detailed with formulas
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- [ ] Validations defined
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- [ ] Interpretations specified
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- [ ] Concrete examples included
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