Agilent Technologies (A) Operating Expenses (2010 - 2026)
Agilent Technologies (A) recorded Operating Expenses of $1.43 billion in fiscal Q3 2026 (quarter ended Jul 31, 2026), up 4.1% from $1.38 billion a year earlier but down 0.1% from the prior quarter.
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Analysis
Agilent Technologies (A) Operating Expenses (2010 - 2026) Analysis & Trends
On a TTM basis, Agilent Technologies' Operating Expenses came in at $5.73 billion as of Jul 31, 2026, up 7.3% year-over-year; for FY2025 (ended Oct 31, 2025), it came in at $5.47 billion, up 8.9% from FY2024.
- Annual Operating Expenses has a five-year compound annual growth rate of 4.0% (FY2020 to FY2025).
- Across earlier fiscal years, Operating Expenses came in at $5.02 billion in FY2024 (-8.4%), $5.48 billion in FY2023 (+4.8%), $5.23 billion in FY2022 (+5.2%) and $4.97 billion in FY2021 (+10.7%).
- Quarterly Operating Expenses has ranged from $1.21 billion in fiscal Q2 2024 to $1.54 billion in fiscal Q3 2023 over the past five years.
- On a year-over-year basis, Operating Expenses has increased for eight consecutive quarters, with growth averaging 7.1% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 17.8% in fiscal Q3 2023, against a decline of 19.1% in fiscal Q3 2024 at the low end.
- Per Business Quant, the preceding three fiscal quarters came in at $1.44 billion (Q2 2026), $1.45 billion (Q1 2026) and $1.42 billion (Q4 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 258.21 Bn | 258.21 Bn | - | 9.87 Bn |
| 2 | Abbott Laboratories | 184.72 Bn | 184.72 Bn | - | 11.66 Bn |
| 3 | Danaher | 163.63 Bn | 147.44 Bn | 3.61 Bn | 2.48 Bn |
| 4 | Intuitive Surgical | 150.86 Bn | 131.14 Bn | 2.08 Bn | 1.49 Bn |
| 5 | Medtronic | 123.68 Bn | 88.93 Bn | 6.34 Bn | 4.06 Bn |
| 6 | Stryker | 111.91 Bn | 111.91 Bn | - | - |
| 7 | Boston Scientific | 61.64 Bn | 61.64 Bn | 3.43 Bn | - |
| 8 | Becton Dickinson | 54.28 Bn | 51.43 Bn | 2.32 Bn | 4.32 Bn |
| 9 | Edwards Lifesciences | 53.21 Bn | 37.22 Bn | 1.35 Bn | 840.10 Mn |
| 10 | Agilent Technologies | 49.54 Bn | 42.43 Bn | 1.04 Bn | 1.43 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | 1.43 Bn |
| Apr 30, 2026 | 1.44 Bn |
| Jan 31, 2026 | 1.45 Bn |
| Oct 31, 2025 | 1.42 Bn |
| Jul 31, 2025 | 1.38 Bn |
| Apr 30, 2025 | 1.37 Bn |
| Jan 31, 2025 | 1.31 Bn |
| Oct 31, 2024 | 1.29 Bn |
| Jul 31, 2024 | 1.25 Bn |
| Apr 30, 2024 | 1.21 Bn |
| Jan 31, 2024 | 1.27 Bn |
| Oct 31, 2023 | 1.28 Bn |
| Jul 31, 2023 | 1.54 Bn |
| Apr 30, 2023 | 1.33 Bn |
| Jan 31, 2023 | 1.33 Bn |
| Oct 31, 2022 | 1.38 Bn |
| Jul 31, 2022 | 1.31 Bn |
| Apr 30, 2022 | 1.25 Bn |
| Jan 31, 2022 | 1.30 Bn |
| Oct 31, 2021 | 1.27 Bn |
API Access
Agilent Technologies Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=A&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "A", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=A&period=max&api_key=YOUR_API_KEY");
const data = await res.json();