This is Part 1 of a short series on agentic AI. Part 2 is here. Subsequent posts will be linked here.
The new ‘frontier’ of generative AI is the agent. Agentic AI is any configuration that allows LLMs to act autonomously. LLMs leverage their reasoning abilities to take in information, then act on that information; most agentic setups are a loop, so once that action is done, the agent either repeats the task immediately based on what has changed, or waits a period of time before repeating.
Agents have a lineage in other automations, like macros, scripts, and bots. In a piece of software, there might be a task that you repeat over and over, such as indenting a line a certain number of times, or formatting a category of text in a particular way. Macros allow you to record a sequence of actions, that you can then repeat with a keyboard shortcut.
You might set a folder to back up from your laptop to an external hard drive: this can be automated with a script and cron job, through an OS feature like Mac’s Automator, or SaaS providers like IFTTT or n8n.io.
You might write a bot that scrapes blog posts on a particular topic, then sends you a list as an email at the end of the week. These are fairly straightforward tasks, each with perhaps a handful of steps. Easy enough to script up (or find a script, plugin, or app online).
This is a spectrum of delegation, of automation. Streamlining the things we do day to day, getting technology to help us, to save us time. AI agents are adjacent to this spectrum, but also a little different — in the same way that LLMs are not really like other pieces of software, no matter how good they’re getting at certain tasks.
To massively reduce the complexity of LLMs, they are probability machines. That’s not to say they’re random or chance-based. They map out connections between concepts, in hugely complex webs or meshworks. The word ‘pawn’, for example, has a location in the LLM’s internal map — a set of coordinates. ‘Pawn’ might sit near ‘king’, because it’s often used to describe chess moves; but ‘pawn’ might also sit near ‘broker’ or ‘shop’. And ‘king’ might sit next to ‘queen’ or ‘bed’, and ‘shop’ might sit near ‘store’ which sits near ‘data’; language, it turns out, is complicated. LLMs manage this complexity through developing their map across multiple dimensions. When given a prompt, the model navigates through the map to find the most probable path through these conceptual clusters, and generate a response based on that.
No matter how good LLMs get, they will only ever deliver their best guess in response to a query. So how do we help LLMs make better guesses? Through tool calls. Allow the LLM to verify its guess with access to a search engine, for instance. This is now common across the major proprietary models like Claude and Gemini.
Tool calls, Skills, MCPs — these are the explicit harnesses and suspenders that we can put on LLMs to make them more useful. That we have to do so at all says a lot about how innately unreliable LLMs still are for a whole bunch of tasks — but that’s a separate conversation.
We’ve given LLMs tools — that’s great in the moment. It helps us use them more efficiently, it lets them be more helpful to us. But that’s still a transaction. What if we could get them to head off and work for us? They can reason, after all — even if it’s only a semantic kind of reasoning. That’s no different to me writing out a blog post off the top of my head like I’m doing now. I’m reasoning out the structure, the argument. I’m making judgements through writing.
It would be great if we could leverage that kind of reasoning to automate longer strings of tasks: entire processes or workflows. Hell, if we handed over access to our internet accounts, it could take care of emails, scheduling, restaurant and holiday bookings, even do some copy writing and social posting for us.
That was the reasoning (so to speak) behind Claude Code, OpenAI’s Operator, and various AutoGPTs. But it was OpenClaw, an open-source tool released in late 2025, that changed the game. The earlier tools still needed humans to click, copy, paste, verify, and oversee the various stages of the automation; OpenClaw automated all those steps too, but also went one step further. OpenClaw, at least initially, was by default granted broad access to filesystems and user credentials; a huge problem if users didn’t intend that, but also a new level of affordance for automations.
So how are we leveraging these new autonomous workers?
To automate the boring stuff, same as always. Manage my email and my calendar. Give me a daily briefing. Help me meal plan. Keep track of my saves and bookmarks.
Mid-tier usage is around researching and outlining content for blogs or socials, automating software development or sysadmin workflows, tracking and comparing prices of various items across vendors (be it groceries, wholesale purchases for business, or real estate).
At the advanced or enterprise level, multi-agent setups are the go-to. Give each agent a particular job, and then put them into service together. These configurations are sometimes deployed in finance, internal business operations, and engineering.
The initial fear with agentic systems was that they’d run wild, or that they’d build new skills and take over; good old-fashioned sci-fi tech panic fun. But the truth is far less scary. These agents are great when given very specific tasks, and clearly-defined instructions around tool use. Open-ended deployment — where agents are allowed to act beyond clear instructions — often results in error or failure.
While everyone else is (rightly) concerned about deployment at scale, I’m trying to figure out how the agent itself operates. More to come.
