Will AI replace Agricultural Engineers?

Not as a job, but it is already doing parts of the work. Across the 14 official task statements scored for Agricultural Engineers (United States, SOC 17-2021), 9% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 26 out of 100 (range 20–32, band: low). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Agricultural Engineers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Prepare reports, sketches, working drawings, specifications, proposals, and budgets for proposed sites or systems” (66/100, high); “Design agricultural machinery components and equipment, using computer-aided design (CAD) technology” (50/100, partial); “Design sensing, measuring, and recording devices, and other instrumentation used to study plant or animal life” (43/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Agricultural Engineers” stay human?

About 76% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Visit sites to observe environmental problems, to consult with contractors, or to monitor construction activities” (0/100, minimal); “Supervise food processing or manufacturing plant operations” (6/100, minimal); “Test agricultural machinery and equipment to ensure adequate performance” (8/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Agricultural Engineers” do about AI?

Start from the ledger rather than the headline: 9% of this job's weighted core work is exposed, and roughly 76% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Agricultural Engineers calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 14 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Agricultural Engineers

preparing reports, sketches, working drawings, specifications, designing food processing plants and related mechanical systems and testing agricultural machinery and equipment to ensure adequate performance. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: visiting sites to observe environmental problems is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 9%
changing shape: 15%
staying human: 76%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 26 out of 100 (20–32 allowing for uncertainty): low exposure, across 14 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for agricultural engineers is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

What shifts is preparing reports, sketches, working drawings, specifications, proposals and budgets for proposed sites or systems: the paper around the work, not the work.

Your week, as this page understands it

Apply knowledge of engineering technology and biological science to agricultural problems concerned with power and machinery, electrification, structures, soil and water conservation, and processing of agricultural products. The job title says “agricultural engineers”. The real job is the part underneath: visiting sites to observe environmental problems. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But agricultural engineers is not one task. It is 14 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is visiting sites to observe environmental problems, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 9%
changing shape: 15%
staying human: 76%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

1 task

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Preparing reports, sketches, working drawings, specifications, proposals and budgets for proposed sites or systems
This is reading one thing and writing another: reports, sketches, working drawings, specifications, proposals and budgets in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare reports, sketches, working drawings, specifications, proposals, and budgets for proposed sites or systems.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reports, drawings and budgets follow standard formats AI drafts well, with an engineer responsible for them.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

2 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Designing sensing, measuring and recording devices and other instrumentation used to study plant or animal life
The software now makes the first pass at devices, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Design sensing, measuring, and recording devices, and other instrumentation used to study plant or animal life.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Instrument design draws on documented engineering, though building and proving it takes a bench.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing agricultural machinery components and equipment
The software now makes the first pass at agricultural machinery components, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Core
Source: “Design agricultural machinery components and equipment, using computer-aided design (CAD) technology.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: AI can suggest component ideas, but turning them into buildable machinery is still the engineer's work.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Staying human

11 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Visiting sites to observe environmental problems
This work happens in the physical world: sites, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Visit sites to observe environmental problems, to consult with contractors, or to monitor construction activities.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: A site visit means standing on the ground looking at the problem.
The five ratings: output a model can produce 1/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Discussing plans with clients, contractors, consultants and other engineers so that they can be evaluated and necessary changes made
The value here is that a specific person handles plans and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Discuss plans with clients, contractors, consultants, and other engineers so that they can be evaluated and necessary changes made.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Working plan changes through happens in back-and-forth conversation among the people responsible.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Meeting with clients, such as district or regional councils, farmers and developers, to discuss their needs
This work happens in the physical world: clients, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Meet with clients, such as district or regional councils, farmers, and developers, to discuss their needs.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Client meetings work because farmers and councils talk to an engineer they trust.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Testing agricultural machinery and equipment to ensure adequate performance
This work happens in the physical world: agricultural machinery, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Test agricultural machinery and equipment to ensure adequate performance.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Testing a machine means running it and watching how it performs.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Providing advice on water quality and issues related to pollution management
The ratings behind this row put advice well outside what today's tools can do on their own.
importance 4 · Core
Source: “Provide advice on water quality and issues related to pollution management, river control, and ground and surface water resources.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Water-quality guidance is well documented, though the decisive details are specific to each catchment.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Conducting educational programs that provide farmers or farm cooperative members with information that can help them improve agricultural productivity
This work happens in the physical world: educational programs, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Conduct educational programs that provide farmers or farm cooperative members with information that can help them improve agricultural productivity.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Training material drafts easily, yet farmers turn up to learn from a person.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Planning and directing construction of rural electric-power distribution systems
This work happens in the physical world: construction of rural electric-power distribution systems, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Plan and direct construction of rural electric-power distribution systems, and irrigation, drainage, and flood control systems for soil and water conservation.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (12–26 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The planning side can be drafted, but directing construction happens out on site.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.

Show the other 4 tasks

Designing food processing plants and related mechanical systems
staying human
The ratings behind this row put food processing plants well outside what today's tools can do on their own.
importance 4 · Supplemental
Source: “Design food processing plants and related mechanical systems.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Plant layouts follow documented engineering standards, with an engineer answerable for the final design.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing structures for crop storage
staying human
This work happens in the physical world: structures, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Design structures for crop storage, animal shelter and loading, and animal and crop processing, and supervise their construction.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (18–32 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Structure designs draft from standards, but supervising the build means being there.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing and supervising environmental and land reclamation projects in agriculture and related industries
staying human
This work happens in the physical world: environmental, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Design and supervise environmental and land reclamation projects in agriculture and related industries.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (12–26 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Reclamation plans can be drafted; supervising the works means being on the land.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Supervising food processing or manufacturing plant operations
staying human
This work happens in the physical world: food, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Supervise food processing or manufacturing plant operations.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Supervising a processing plant means being on the floor with the crew.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $98,590a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 1,480in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: reports, sketches, working drawings, specifications, proposals and budgets in, a record out. The rows above are exactly that shape: preparing reports, sketches, working drawings, specifications, proposals and budgets for proposed sites or systems and designing sensing. What it cannot do is be there in the room, and that is still where sites get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: visiting sites to observe environmental problems is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 9% of this job's task weight sits in rows the software is already learning, 15% in rows that change shape rather than disappear, and 76% in rows it is nowhere near. That is the position, measured across 14 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Preparing reports, sketches, working drawings, specifications, proposals and budgets for proposed sites or systems is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to reports, sketches, working drawings, specifications, proposals and budgets, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether sites are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near designing sensing, measuring and recording devices and other instrumentation used to study plant or animal life. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to agricultural engineers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was mechanical drafters: only about 6% of its durable work is work you already do and it pays 27.4% less. And on the numbers you do not need one. This job scores 26/100 here, with only 9% of the task list in the top band, and “visit sites to observe environmental problems, to consult with contractors, or to…” is not work that hands over cleanly. None of them beats deepening what you already have.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Mechanical Drafters
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already design agricultural machinery components and equipment, using computer-aided design (CAD) technology, and their equivalent is to produce three-dimensional models, using computer-aided design (CAD) software. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $71,550 against your $98,590, 27.4% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Agricultural Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already design structures for crop storage, animal shelter and loading, and animal and crop…, and their equivalent is to collect animal or crop samples. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $49,630 against your $98,590, 49.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Materials Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already design food processing plants and related mechanical systems, and their equivalent is to design processing plants and equipment. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 9% of its task weight, across 14 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like visiting sites to observe environmental problems arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Mechanical engineers (professional) is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

Aerospace engineers

In UK official statistics this job is counted as Mechanical engineers (professional) and Aerospace engineers. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for agricultural engineers, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 9% of the work on this page is already inside what they can do.

Try The AI Authority free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

The AI Authority is a general community about working with AI, not a course for agricultural engineers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for agricultural engineers launches. Nothing else.

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No Space for agricultural engineers yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for agricultural engineers existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for agricultural engineers launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Agricultural Engineers?: Not as a job, but it is already doing parts of the work. Across the 14 official task statements scored for Agricultural Engineers (United States, SOC 17-2021), 9% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 26 out of 100 (range 20–32, band: low). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Agricultural Engineers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Prepare reports, sketches, working drawings, specifications, proposals, and budgets for proposed sites or systems” (66/100, high); “Design agricultural machinery components and equipment, using computer-aided design (CAD) technology” (50/100, partial); “Design sensing, measuring, and recording devices, and other instrumentation used to study plant or animal life” (43/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Agricultural Engineers” stay human?: About 76% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Visit sites to observe environmental problems, to consult with contractors, or to monitor construction activities” (0/100, minimal); “Supervise food processing or manufacturing plant operations” (6/100, minimal); “Test agricultural machinery and equipment to ensure adequate performance” (8/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Agricultural Engineers” do about AI?: Start from the ledger rather than the headline: 9% of this job's weighted core work is exposed, and roughly 76% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Agricultural Engineers calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 14 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.