AI Agent for Spreadsheets: Automate Excel & Google Sheets with AI

You spend hours cleaning data, writing formulas, and building reports. An AI agent does all three — and catches errors you'd miss. Here's how to build one that turns your spreadsheets into an autopilot system.

Why Spreadsheets Need AI Agents

Spreadsheets are the world's most popular programming language. Over 1 billion people use Excel or Google Sheets. But most of them are doing the same painful tasks over and over:

• Data cleaning — fixing formats, removing duplicates, standardizing names. Takes 60% of data work time.
• Formula writing — Googling "VLOOKUP nested IF XLOOKUP" for the 100th time.
• Report building — copy-paste data from 5 sources, create pivot tables, format charts. Every. Single. Week.
• Error checking — finding the one cell with a typo that breaks the whole model.

An AI agent handles all of this. You describe what you want in plain English, and the agent reads your data, writes formulas, cleans values, builds charts, and catches errors — all through the Google Sheets or Excel API.

6 Things a Spreadsheet Agent Can Do

Build: Data Cleaning Agent

import Anthropic from "@anthropic-ai/sdk";
import { google } from "googleapis";

const anthropic = new Anthropic();
const sheets = google.sheets({ version: "v4", auth: oAuth2Client });

async function cleanColumn(spreadsheetId, sheetName, column, rules) {
  // Read the column data
  const range = `${sheetName}!${column}:${column}`;
  const { data } = await sheets.spreadsheets.values.get({
    spreadsheetId, range,
  });

  const values = data.values?.flat() || [];
  const header = values[0];
  const rows = values.slice(1);

  // Process in batches of 100
  const batchSize = 100;
  const cleaned = [];

  for (let i = 0; i < rows.length; i += batchSize) {
    const batch = rows.slice(i, i + batchSize);

    const response = await anthropic.messages.create({
      model: "claude-sonnet-4-20250514",
      max_tokens: 2000,
      messages: [{
        role: "user",
        content: `Clean this data column. Return a JSON array of cleaned values.

Column: "${header}"
Cleaning rules: ${rules}

Original values:
${JSON.stringify(batch)}

Rules:
- Preserve the original order
- Return exactly ${batch.length} values
- If a value can't be cleaned, return it unchanged
- Return ONLY the JSON array, nothing else

Common cleaning operations:
- Standardize capitalization (title case for names)
- Remove extra whitespace
- Fix obvious typos
- Standardize date formats to YYYY-MM-DD
- Normalize phone numbers
- Standardize country/state names
- Remove special characters where inappropriate`
      }],
    });

    const cleanedBatch = JSON.parse(response.content[0].text);
    cleaned.push(...cleanedBatch);
  }

  // Write cleaned values back
  await sheets.spreadsheets.values.update({
    spreadsheetId,
    range: `${sheetName}!${column}2:${column}${rows.length + 1}`,
    valueInputOption: "USER_ENTERED",
    requestBody: {
      values: cleaned.map(v => [v]),
    },
  });

  // Report changes
  const changes = rows.reduce((count, original, idx) =>
    original !== cleaned[idx] ? count + 1 : count, 0);

  return { total: rows.length, changed: changes, unchanged: rows.length - changes };
}

// Example usage
await cleanColumn(
  "spreadsheet_id_here",
  "Customers",
  "B",  // Company names column
  "Standardize company names: title case, remove Inc/LLC/Ltd suffixes, fix common abbreviations"
);

Build: Formula Generator

async function generateFormula(spreadsheetId, sheetName, request) {
  // Get sheet structure (headers + sample data)
  const { data } = await sheets.spreadsheets.values.get({
    spreadsheetId,
    range: `${sheetName}!A1:Z5`, // Headers + 4 rows of sample data
  });

  const headers = data.values?.[0] || [];
  const sampleData = data.values?.slice(1, 5) || [];

  const response = await anthropic.messages.create({
    model: "claude-sonnet-4-20250514",
    max_tokens: 500,
    messages: [{
      role: "user",
      content: `Generate a Google Sheets formula for this request.

Sheet structure:
Columns: ${headers.map((h, i) =>
  `${String.fromCharCode(65 + i)}: ${h}`
).join(", ")}

Sample data (first 4 rows):
${sampleData.map((row, i) =>
  row.map((cell, j) => `${headers[j]}: ${cell}`).join(" | ")
).join("\n")}

Request: "${request}"

Return JSON:
{
  "formula": "=THE_FORMULA",
  "explanation": "what it does in plain English",
  "column": "which column to put it in (letter)",
  "applies_to": "single_cell|entire_column",
  "example_result": "what the result looks like"
}

Rules:
- Use Google Sheets syntax (not Excel)
- Prefer ARRAYFORMULA for column-wide formulas
- Use named ranges or column letters, not hardcoded ranges
- Handle edge cases (empty cells, #N/A, etc.)`
    }],
  });

  const result = JSON.parse(response.content[0].text);

  // Optionally insert the formula
  if (result.applies_to === "entire_column") {
    const col = result.column;
    await sheets.spreadsheets.values.update({
      spreadsheetId,
      range: `${sheetName}!${col}1`,
      valueInputOption: "USER_ENTERED",
      requestBody: { values: [[result.formula]] },
    });
  }

  return result;
}

// Example usage
await generateFormula(
  "spreadsheet_id",
  "Sales",
  "Calculate the running total of column D, resetting at the start of each month based on the date in column A"
);
// → =ARRAYFORMULA(IF(MONTH(A2:A)=MONTH(A1:INDIRECT("A"&ROW(A2:A)-1)),
//   SUMIFS(D$2:D,MONTH(A$2:A),MONTH(A2:A),ROW(A$2:A),"<="&ROW(A2:A)),D2:D))

Build: Automated Report Builder

async function generateWeeklyReport(spreadsheetId, config) {
  // Read source data
  const { data } = await sheets.spreadsheets.values.get({
    spreadsheetId,
    range: config.dataRange,  // e.g., "Sales!A:F"
  });

  const headers = data.values[0];
  const rows = data.values.slice(1);

  // Filter to this week
  const weekStart = getWeekStart();
  const thisWeekRows = rows.filter(row => {
    const date = new Date(row[config.dateColumn]);
    return date >= weekStart;
  });

  // Generate analysis with Claude
  const response = await anthropic.messages.create({
    model: "claude-sonnet-4-20250514",
    max_tokens: 1500,
    messages: [{
      role: "user",
      content: `Analyze this week's data and generate a report.

Headers: ${headers.join(", ")}
Data (${thisWeekRows.length} rows this week):
${thisWeekRows.slice(0, 50).map(r => r.join(" | ")).join("\n")}
${thisWeekRows.length > 50 ? `... and ${thisWeekRows.length - 50} more rows` : ""}

Generate:
1. Key metrics (total revenue, count, averages)
2. Top 5 performers (by ${config.metricColumn})
3. Week-over-week comparison
4. Notable trends or anomalies
5. 3 recommended actions

Format as a clean report with sections.`
    }],
  });

  // Create a new sheet for the report
  const reportSheetName = `Report ${new Date().toISOString().split("T")[0]}`;

  await sheets.spreadsheets.batchUpdate({
    spreadsheetId,
    requestBody: {
      requests: [{
        addSheet: {
          properties: { title: reportSheetName },
        },
      }],
    },
  });

  // Parse the report into cells and write
  const reportLines = response.content[0].text.split("\n");
  await sheets.spreadsheets.values.update({
    spreadsheetId,
    range: `${reportSheetName}!A1`,
    valueInputOption: "USER_ENTERED",
    requestBody: {
      values: reportLines.map(line => [line]),
    },
  });

  return { sheet: reportSheetName, analysis: response.content[0].text };
}

Build: Anomaly Detection Agent

async function detectAnomalies(spreadsheetId, sheetName, config) {
  const { data } = await sheets.spreadsheets.values.get({
    spreadsheetId,
    range: `${sheetName}!A:Z`,
  });

  const headers = data.values[0];
  const rows = data.values.slice(1);

  // Calculate basic statistics for numeric columns
  const numericCols = headers.map((h, i) => ({
    header: h,
    index: i,
    values: rows.map(r => parseFloat(r[i])).filter(v => !isNaN(v)),
  })).filter(col => col.values.length > 0);

  const stats = numericCols.map(col => {
    const mean = col.values.reduce((a, b) => a + b, 0) / col.values.length;
    const stdDev = Math.sqrt(
      col.values.reduce((sum, v) => sum + (v - mean) ** 2, 0) / col.values.length
    );
    return { ...col, mean, stdDev };
  });

  // Find statistical outliers (> 3 standard deviations)
  const outliers = [];
  rows.forEach((row, rowIdx) => {
    stats.forEach(col => {
      const val = parseFloat(row[col.index]);
      if (!isNaN(val) && Math.abs(val - col.mean) > 3 * col.stdDev) {
        outliers.push({
          row: rowIdx + 2,
          column: col.header,
          value: val,
          expected_range: `${(col.mean - 2 * col.stdDev).toFixed(2)} – ${(col.mean + 2 * col.stdDev).toFixed(2)}`,
          deviation: ((val - col.mean) / col.stdDev).toFixed(1) + " σ",
        });
      }
    });
  });

  // Use Claude for contextual anomaly detection
  const response = await anthropic.messages.create({
    model: "claude-sonnet-4-20250514",
    max_tokens: 1000,
    messages: [{
      role: "user",
      content: `Analyze this data for anomalies beyond simple statistics.

Headers: ${headers.join(", ")}
Row count: ${rows.length}
Statistical outliers found: ${outliers.length}

Column statistics:
${stats.map(s =>
  `${s.header}: mean=${s.mean.toFixed(2)}, stdDev=${s.stdDev.toFixed(2)}, min=${Math.min(...s.values)}, max=${Math.max(...s.values)}`
).join("\n")}

Sample data (first 20 rows):
${rows.slice(0, 20).map(r => r.join(" | ")).join("\n")}

Statistical outliers:
${outliers.map(o =>
  `Row ${o.row}: ${o.column} = ${o.value} (expected ${o.expected_range}, ${o.deviation})`
).join("\n")}

Look for:
1. Values that are technically within range but contextually wrong
2. Missing patterns (gaps in dates, skipped IDs)
3. Inconsistencies between related columns
4. Sudden trend changes
5. Data entry errors (swapped columns, wrong units)

Return JSON array of anomalies with: row, column, issue, severity (critical/warning/info), suggestion`
    }],
  });

  const contextualAnomalies = JSON.parse(response.content[0].text);

  // Highlight anomalies in the sheet
  for (const anomaly of [...outliers.slice(0, 20), ...contextualAnomalies.slice(0, 20)]) {
    // Could use conditional formatting API here
    console.log(`⚠️ Row ${anomaly.row}: ${anomaly.column || anomaly.issue}`);
  }

  return { statistical: outliers, contextual: contextualAnomalies };
}

Tool Comparison: 7 Spreadsheet AI Tools

Tool	Best For	Price	Custom Logic
Google Sheets AI (Gemini)	In-sheet formula help, summaries	$20/mo (Workspace)	❌ None
Excel Copilot	Formula help, pivot tables, charts	$30/user/mo (M365)	❌ Limited
SheetAI	AI formulas inside Google Sheets	Free / $7/mo	⚠️ Some
Rows.com	AI-native spreadsheet platform	Free / $12/mo	⚠️ Moderate
Airtable AI	Database-style with AI fields	$20/user/mo	⚠️ Some
Julius AI	Data analysis from uploaded CSVs	Free / $20/mo	⚠️ Moderate
Custom Agent (this guide)	Full control, multi-sheet workflows	~$3-10/mo (API)	✅ Full

Our recommendation: Use Excel Copilot or Gemini in Sheets for quick formula help. Use Rows.com if you want an AI-native spreadsheet from scratch. Build custom when you need automated pipelines (data arrives → clean → analyze → report → email) or when you work across multiple spreadsheets.

Build Your Spreadsheet Agent in 30 Minutes

5 Mistakes That Corrupt Your Data

What's Next

Start with the formula generator — it has the fastest time-to-value and the lowest risk (it adds formulas, doesn't modify data). Then build the data cleaning agent with proper backups. Finally, set up automated reports and anomaly detection on a schedule.

The best spreadsheet agents aren't the most sophisticated. They're the ones that save someone 2 hours every Monday morning by having the weekly report ready before they arrive. Build for that kind of impact.