If I were a college career counselor or in career services, I’d quickly be figuring out how to get students to understand these forward deployed engineer jobs exist and how to get them. The requirements are a mix of deep technical skills, often CS majors or minors. You must be great at understanding problem solving, how to have systems thinking, and have a strong business acumen. The kicker, of course, is to make sure you’re very deep in AI agents; you need to have fluency in coding agents, MCP, CLIs, Skills, and so on. Hundreds (thousands?) of technology companies will be hiring for these roles, same with any consulting and IT services company, and the vast major of mid-size and large enterprises will be hiring for this talent internally as well. One great example of opportunity for highly technical talent out there.

Forward deployed engineers, or equivalent, are about to become one of the most in-demand jobs in tech. And one of the most important functions for AI rollouts. Deploying agents is far more technical of a task than most people realize, often far more involved than deploying software. Software generally works the same way every time, and generally for the past few decades has been updated versions of an existing technology or concept (which basically means easier for the enterprise to update their workflows on a newer system). With agents, you’re actually deploying the equivalent of work output within the enterprise. The customer is effectively using you as a professional services provider for a task, which they expect to get solved nearly end-to-end now. This means you need to actually deeply understand the business process as a vendor, and get the customer from the current to the end state seamlessly. Companies need help figuring out which models will work best for their workflows, they need extensive evals setup often, they need change management support for workflows, they need to get their data setup for the agents, and constant tuning of the agentic system for their process. Massive role in tech now. And another example of the kind of highly technical work that AI is creating.

Agents are quickly moving from coding to the rest of knowledge work. But to do this we need ways of bridging the advanced capabilities of the AI models with the real-life workflows in the enterprise, by industry and line of business. The models will remain general purpose, but we’ll move to ways of aligning agents to the unique work that gets done in legal, financial services, insurance, healthcare, life sciences, and more. Each industry has its own set of workflows, domain specific context, and data sources that agents need to have access to and be familiar with. Claude just launched an updated set of plugins and skills for the legal industry, including Box. You can now take any of your enterprise contracts or documents and securely work with them in a headless fashion via Claude in a legal workflow. This is just the start of what industry-specific adoption of AI will look like, and equally shows what the future of headless software interaction will look like in the future.

The need and opportunity for professional services and FDEs to deploy agents right now is massive. Every tech wave offers a new era of consulting and tech services requirements. Moving from analog to digital led to a massive wave in the 90s. Moving from on-prem to cloud did the same in the 2000s. But this is going to be at a scale far greater than the others. The reason is that agents fundamentally change the underlying workflows of an organization. Unlike most prior eras of technology, where it was a change in medium of the service being delivered (on-prem CRM to cloud CRM), agents rewire the business process itself. And unlike upgrading a tech system, business processes are full of idiosyncrasies. Every industry will have its own variants, and every department within those industries will have variants as well. Not to mention the bespoke difference between firms. Bringing agents to marketing in CPG will look different from marketing in healthcare. Bringing agents to sales in a B2B software company will look different from a car dealership. And none of the change is easy technically. You need to first modernize your infrastructure and data and make sure it’s ready for agents; access controls, entitlements, and permissions need to be mapped in a way that works for agents and people; you need to make sure agents have the right context to work with; you need to consistently eval and maintain the agents when there are model upgrades; and you need to drive the change management of the process itself to figure out which parts the people do and what agents do. That’s an insane amount of technical and domain-specific process work to be done to make this all happen. Huge opportunity for new service providers, as well as internally teams and roles to emerge, to help drive this change.

As advanced agents move from coding to the rest of knowledge work, it takes a real amount of work and know-how to get right. You need to ensure agents have the right context and data to work with, wire up systems to agents in a safe and secure way, ensure that the agents are producing quality output, design the end-state workflow where and how humans will be in the loop, maintain the agents when there are model and system upgrades, and more. This isn’t a side project or something you can just do on nights and weekends. You need to design and develop robust agents that will be used in mission critical workflows. It’s a highly technical job, very much akin to a forward deployed engineer for internal functions. This is why, at Box, we’re starting to hire for AI automation engineering roles. This a technical role that will partner with the business directly and help augment how they work to drive even more output, and deliver better experiences for employees and ultimately customers. This is just one example of the kind of role that AI will start to open up in the future. I expect most companies will have many flavors of this going forward.

For everything we’ve seen about agents so far, it’s clear that they will make it far easier for people to get into previously extremely complicated fields. That will most certainly mean far more people will build software, explore creative work, research spaces they couldn’t do before, and so on. Yet, equally, we’ve seen that people with experience in every one of those fields have a huge edge with the right judgment and historical context to leverage these tools in ways that exceed the output of the novices (if they choose to). They know when the agents are making catastrophic mistakes, can give the agents the right context to do the job better than they otherwise would have, and so on. The combination of these two facts essentially means that we will continue to get the same lift as we’ve seen in any other technological revolution. More democratization, but similarly greater output from the experts. This then makes the experts continue to be in higher demand because over time our expectation for what we can get out of any field will just go up. This is going to be true in essentially every important field. You’ll trust a lawyer using an agent for legal advice over someone who’s never had to experience how well a contract holds up. You’ll trust an engineer developing and running software over someone who’s never seen a production system. You’ll rely on the important instincts of a designer using agents over the average prompter. The quality and volume of output we expect from these functions will certainly go up meaningfully, but the person with experience will always have a leg up, which is why the jobs don’t go away.

A common trend emerging in larger enterprises is token budgeting as a major topic. As agents can do more and more long running tasks, and thus take vastly more compute, allocation of tokens across teams becomes a very real thing in the enterprise. Companies spend a meaningful amount of time deciding how much to spend on talent, marketing campaigns, events, laptop setups, and even the cost of lunches. Tokens will be no different. Tokens will similarly need to be excruciatingly well-managed because you’ll need to ensure you don’t blow up your budget, and you’ll need to ensure that the tokens are flowing to the highest and most useful parts of work. You don’t want to find out you burned your monthly budget on something relatively low value and then be blocked on the much higher value task later. Doing this at large company scale is extremely hard as you have layers of abstraction on data and visibility into the digital work being done by agents in any central way. This is going to mean that agentic spend will increasingly will expand beyond the confines of the IT budget, and end up in organizational budgets like other expenses. Ultimately team and org leaders will have to be given budgets for this, but even they don’t have adequate visibility and controls in most cases. We’ll need all new software just to solve this problem, and it’s probably an opportunity for startups in its own right. Going to be an all new era of enterprise resource allocation, especially while we compute constrained.

When AI makes one thing easy to do, it’s always good to assume that will equally be the case for everyone else. If it’s the case for everyone else, then that means competitive forces will ensure that resources move to new or other areas that create differentiation. If AI makes building software easier, then there will be a relative increase in resources going into sales, marketing, and customer success, because standing out or going deeper with customers else becomes even more important. This will also apply to lots of other areas of work. If you automate getting financial advice and insights, then the differentiation is in client engagement. And on and on. Just ask yourself: if everyone else does exactly what I do with this technology, how will I stand out from everyone else? That’s what happens next.

Both Anthropic and OpenAI have new initiatives to help enterprises deploy AI agents within their organizations. This is a trend that’s early but going to get very big fast. As agents enter knowledge work beyond coding, there is very real work to upgrade IT systems, get agents the context they need, modernize the workflows to work with agents, figure out the human-agent relationship in the workflow, drive adoption and do change management, and much more. While AI models have an incredible amount of capability packed into them, there’s no shortcut to getting that intelligence applied to a business process in a stable way. This is creating tons of opportunities across the market for new jobs and firms, and the labs are equally recognizing the criticality here.

Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today. The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do. First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents. Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do. Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes. Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design. All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it. This is a huge opportunity right now whether you’re doing this internally or as an external business provider.

If you think AI replaces software engineers, here’s a quick thought experiment. Imagine you’re a life sciences company. 10 years ago you want to invest heavily in lab automation, processing data at scale, and other software. You look at the cost of doing so and realize you can’t compete with tech for as many engineers as you need, so you pare down your goals and do what you can. Every new software project has a fixed cost of a certain sized team, so you can only do so much given budgets, ability to compete for talent, and other trade offs. Now, AI comes along. And all of a sudden you have the *exact same* output tokens as the best tech companies in the world. Your engineers are using the same AI models as the tech industry, which means you have just boosted your engineering team by a some meaningful amount, while also neutralizing your differences with tech. Do you continue with your pared down approach, or do you start to hire more engineers because each engineer is 2X or 5X more capable than before? In almost every company I’m talking to, they’re doing the latter. Now extrapolate this to every bank, manufacturer, industrial company, retailer, and on and on. And extrapolate it not to just large enterprises, but also every SMB up and down the stack of these value chains. Oh, and also extrapolate this to other job functions, not just engineers. Resource scarce domains in marketing, legal, finance, design, and so on. If you’re wondering why new jobs show up because of AI this is the reason. Any other view of what happens doesn’t contemplate the variety of unmet needs there are in the economy.

Atlassian’s results surprised Wall Street, but it shouldn’t be a surprise. The simple heuristic for the future of software is that when there are 100X more agents than people, which parts of software will grow because agents are doing more work that the underlying software is tied to. If the world generates more code, generates more leads, reviews more contracts, processes more invoices, creates more designs, transacts with more payments, and so on, what are the underlying systems that are managing that work? That will give you a hint as to what happens next. These agents still need guardrails, security, compliance, workflows to be tied to, data stored, and so on. Those parts of the system of record ecosystem will only go up over time in a world of 100X more untrusted (and trusted) agents used in your workflows.

When I talk to enterprises outside of Silicon Valley, most of the use-cases they have in mind with AI are to augment and accelerate how they work, simply because of how much more they can do right now. Most companies are not satisfied with how much they’re doing, and they’re always constrained by some bottleneck. So they’re looking at processes that are slow and inefficient and wondering if AI can make it so they can ship more product, speed up customer onboarding, better resolve customer issues, more comprehensively understand their customers, and more. They’re also bringing intelligence to work that would have never been possible to do before. Tech jobs got concentrated in valley and the tech industry, and enterprises or SMBs have not been able to build the products or bring automation to most areas of work. AI lets them do so now. This will be true of many other fields. And in the areas where there may be some cost cutting, usually that’s in service of funding another area of growth, or it’s temporary. AI cost cutting quickly gets eroded when your competition uses AI to better serve the customer and compete more effectively.

As agents become the biggest users of software, then all software has to be available in a headless fashion. Agents won’t be using your UI, they’ll be talking to your APIs. So the question becomes what is the business model of software and this headless approach in the future? Here are a few thoughts on how everything plays out based on what we’re seeing and doing at Box, but also conversation with other platforms. 1) Seats don’t go away for *people*. Seats are still a convenient and efficient way to have a customer use technology predictably for a set of users within a baseline set of usage. The key, though, is that when the customer pays for a seat, it has to come with a set of usage of APIs on behalf of that user that the agent can use on their behalf. The user will need to be able to interact with their data and the underlying tool via any agent they work with, and an embedded amount of usage will come with the seat. I would imagine most software -Box included- will enable seats to work with their data at a relatively high volume via systems like ChatGPT, Codex, Claude, Gemini, Cursor, Copilot, Perplexity, Factory, Cogniton, et al. quite seamlessly. If you don’t do this, you’re DOA. 2) Agents may have “seats” if they are doing stateful work in the system, but they will be priced very differently than people. Seats (or the equivalent) can make sense when you have an agent that has its own workspace, stores its own data, needs a different set of permissions compared to the user, and so on. If a company wants this agent to be around for long period of time, that may very well look like another “user” in the system. Openclaw-style agents highlight what this future could look like. The only issue on pricing here is that one customer could decide to do all their work in 1 agent, and another might split it into 1,000 agents. So pricing like a human seat is nearly impossible and impractical; each company will have a different approach for this as it gets tricky perfectly trying to capture all the value within an agent seat. 3) The dominant pricing for headless use that goes above the seat allotment, or when an agent is firmly acting on their own, will be a consumption model. Many enterprises software platforms have previously operated like this with PaaS options, and agents will look like another machine user of their system. In some cases the APIs might get priced just as they did previously, but in other cases there may need to be new types of APIs that represent the work an agent would do in one go -more akin to an outcome- instead of a series of API calls. This is especially germane when the headless software also has an agentic use-case embedded within in, such as orchestrating the process within their own system via AI. Overall the growth of this usage pattern is effectively unbounded as the use-cases for agents operating on data in these systems will dramatically exceed what people do with their data and tools today. Every platform that goes headless (which will be anyone that wants to take advantage of agents) will need to adopt a model like this. Some may fight it initially but it’s an inevitably as there will always be more and more agents outside your platform than people. Overall, there’s a lot of really interesting changes left to come in software due to headless use of these systems. Early days.

Starting to hire and retrain for new agent engineering roles for *internal* functions to help get more powerful agents working well on critical business processes. I expect this type of role to be a very big deal over time at Box and other companies. It looks something like an internal FDE, whose job it is to wire up internal systems and get agents working with them effectively. The person will be extremely technical and capable of building secure, governed agents for internal workflows that connect to business systems (like Box, Salesforce, Workday, etc.), and codify workflows in skills. In some cases this person may understand the business process well enough to do it fully, but in most cases I expect them to work with the business directly in an embedded fashion. Ironically, that may introduce another new role on the business side that is more akin to agent product management for internal processes. The key is that you need technical + process people that can span multiple teams or functions in an organization. It’s not about brining automation to a job, but bringing automation to a process. This is going to be a very big trend in most companies going forward. Fun to watch the early innings of what this will look like.

Will keep saying this, but software jobs aren’t going away. Agents are the single biggest form of leverage for anyone technical in history. Probably has never been a better time to be technical in terms of being able to accomplish something solo, in a team, or company. We think that most of the world’s software has already been built and that agents will just reduce work from an existing pie. In fact, we are about to experience 100X more software than before. Think about how many apps you regularly use that need to get better. How many legacy on prem systems that have to get replatformed for the cloud. How many SMBs never could hire developers. How many security issues are about to be uncovered and need to get patched. How many IT organizations are about to bring automation to workflows they never could have automated. How much data is about to processed and connected in most organizations. This is all what the agents will be working on. And every one of those agents will need a person to kick them off, manage their work, orchestrate them, and get their output into a workable and useful form. That person will generally need to be technical (or become technical quickly), and this will create a huge amount of opportunity for anyone up to the task.

Agentic coding is a huge boon for software developers that want to get far more done, great for IT people to build vastly more custom systems internally, great for domain experts that want to automate workflows or wire systems together, and absolutely fantastic for anyone curious to learn how to start coding. What it’s less great for is casually building complex software that you have to maintain on an ongoing basis and take on all the risk for. Upgrades, maintenance, keeping up to date with latest security issues, and so on, are taxes most knowledge workers aren’t familiar with or prepared for. Net net: we’re going to get 100X more software and vastly more software developers in the future. But that’s different from *everyone* rolling their own.