Agents, everywhere. This is the week it truly goes gangbusters.

𝗙𝗿𝗮𝗻𝗸 𝘁𝗵𝗲 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 𝗔𝗴𝗲𝗻𝘁. 𝗖𝗮𝘁𝗵𝘆 𝘁𝗵𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝗔𝗴𝗲𝗻𝘁. 𝗦𝗮𝗺 𝘁𝗵𝗲 𝗦𝗮𝗹𝗲𝘀 𝗘𝗻𝗮𝗯𝗹𝗲𝗺𝗲𝗻𝘁 𝗔𝗴𝗲𝗻𝘁. 𝗥𝗼𝗿𝘆 𝘁𝗵𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁. 

The world of agentic workforces are storming in to sight, but what exactly are they, and what are they not?

Here’s a primer that’ll hopefully help you to break this all down and figure out how to make the most of them. After all as the saying goes, AI won’t replace you, but humans backed by a team of AI agents probably will.

I’ve been building specialized agents for a while now, in order to help me plan serendipity in to my holidays, help me with my financial goals or help me to address chronic health issues with preventative behavioural changes instead of medicine prescriptions which is the only thing human doctors were giving me.

I also write a lot, and nowadays I write the seed of a weird idea in to a ‘writing agent’ which is trained on my writing style and idea extrapolation, and riffs with me. I then use it’s output in different formats to provide myself with better ideas, and then I write the final draft up myself.

Up until this week, building an agent that does anything more than talk to you has required you to manually create workflows, connect APIs and use a string of different tools to tackle each part of the work flow. Errors and clumsiness has made the widespread creation of agents pretty difficult unless you either have a lot of money or technical expertise to hand. In my view, that’s all turned on its head this week.

I think this is a seminal moment for agents - we’re seeing a number of incredible tools come to the fore which enable you to build visual workflows and orchestrate teams of agents that co-operate together to get things done.

What are agents? Specialists or generalists?

Agents are different to Gen AI tools, which generate content using intelligence. Agents use AI to get specific tasks done within the bounds of goals or objectives that have been set for it by a human or another agent. Does that sound kind of like an employee? Bingo.

Up until now Gen AI has been the ultimate generalist, trawling the entire internet to answer your question. Agents are specialists. Think of AI agents as hyper-specialized freelancers: they're task-driven, autonomous digital workers capable of performing specific actions independently—like booking meetings, analyzing contracts, or running entire marketing campaigns. Unlike generalist AI (such as ChatGPT), which generates broad content based on massive datasets, AI agents are action-oriented + designed to execute precise tasks or sequences of tasks with ideally not much oversight. I for example can already see multiple agents I build, thinking through a problem together and giving each other feedback, but each doing their own separate tasks. It’s a wild thing to watch!

So the difference between Gen AI and AI agents lies in breadth VS depth.

• Generative AI = generates and synthesizes content, providing broad solutions across varied topics. Think of gen AI as the guy who just ‘gets it done’. The number two. The fixer guy who sorts the ambiguous problem out, but goes a bit rogue and doesn’t always listen to you.

• AI agents = go deeper and narrower, completing specific workflows with specific guardrails and training, with precision, reasoning and autonomy. Think of it as the employee you interview, qualify for the role, onboard and train. It’s your job to keep them on track and performing.

So what’s next?

Will the future workplace will change as a result of this? You’d better believe it will. Pre internet people were mainly full-time generalists (business people, farmers, shop owners etc) and then once the internet came about we moved towards being full time specialists (tech sales people, data scientists) who use technology and are highly competent at one specific task.

The next era shows up as a fluid and likely fractional workforce comprising both human specialists and AI agents, or potentially human generalists orchestrating specialist agents. Each agent can be optimized to be great in a specific job role, and made better with constant feedback loops. Very much like training a new hire to master one critical role exceptionally well. Sort of like you'd deploy a brilliant salesperson specifically trained to close enterprise deals, you’d similarly activate an AI agent finely tuned to optimize digital ad spend, do market research for you or dynamically adjust pricing models.

For now, agents can either be your assistant doing the jobs you hate, or your analyst helping you to get to your end point faster. Business leaders have been hiring people to do the jobs they hate for years, so this is nothing new.

But in the future we’ll have teams of agents. Every human team will have an AI agent (or multiple) in big companies. In start ups, one person might run a business with 50 AI agents doing most of the work. And, you’ll probably have an agent acting as your digital twin helping you with every task you do.

In this fractional future, the pain and risk of traditional hiring is gone —no recruitment delays, onboarding overhead, or realizing the person you hired isn’t right. Instead, you'll ‘onboard’ specialized digital expertise on-demand, dynamically scaling up or down depending on immediate business needs. You’ll train agents, but you won’t have to tell them twice if you tell them properly once. You’ll be free of the emotional baggage that comes with large teams and focus on the optimal efficiency of the tasks at hand. You’ll focus your time on outputs, and it’ll focus its time on problem solving.

The major risks I see in this area are similar to what we’ve experienced with v1 of the internet - kind of like most of us have forgotten how to hand write or navigate themselves around a city without Google Maps, I see a world where lots of humans forget how to do basic things. I love thinking about the future, but I’m also a bit old school; I handwrite all my tasks each day and I always turn off my phone and wander around when I arrive in a new city. My personal attitude is that it’s important to embrace new technologies, but I wouldn’t ask my employees to do something which I don’t even understand. So retaining a basic working understanding of the things you’re asking Agents to do is - for me - a critical part of the first wave of successful agent builders.

Why is it exploding now? Didn’t this already happen?

Up until very recently, LLMs have acted more like chatbots that you can’t really give boundaries to, nor keep them on a single trail of thought. You’ll have noticed if you use a big LLM that once it starts going off track it’s very difficult to get it back on track (usually you have to literally start a new session), and it also struggles to interact with the world around it properly or remember/stick to a pre-defined set of instructions.

What’s changed? Amidst a bunch of model improvements, the main one is - in technical terms - that AI is now incorporating MCP (Model Context Protocols). MCP tackles these issues and has a single instance of an AI LLM acting in a more consistent way, with stored context from your instructions or prior convos, feedback loops that make more sense and also the ability to interact with the internet around it instead of just scraping it for knowledge.

Because of the above, Agents are not just a one way dialogue from internet>AI like your favourite chatbot. They’re now able to follow rules, loop back on themselves, and have a two way conversation with the internet. These changes are pretty recent, and the difference is pretty big.

Humans + agents > humans or agents

We will (or should) all spend the next two years building personal agentic workflows that amplify our unique skill sets and delegate the manual and tedious tasks that take up your valuable time. It is pretty likely that the new world of agentic work forces won’t require as many humans doing the same things as they’re doing now, which means we as a species will be either finding things that AI cannot do, or orchestrating AI to make us 10x more productive.

If there’s two things you should take away from this post, it’s that:

1. Agents are coming, like it or not. If you use a laptop to do your work and avoid the topic of AI entirely, you’ll likely be replaced pretty quickly unless you're the best in the world at your job or have skills AI can't yet replicate. But if you're reading this, you're right on time.

2. Agents are like employees that you onboard, give a job and course-correct them when they’re having issues.

In the next series of posts, I will share some simple workflows, my favourite tools/guides on training agents, and some tips that everyone can use in order to get their heads in the Agent game. I’ll provide the first steps I’d recommend everyone take to become literate, and I’ll also make myself available for anyone in my network that wants to discuss this topic or get any advice on how to get started. Stay tuned!