Agentic 101: From LLM Apps to Digital Coworkers

“Agentic” is the most over-used and under-defined word in tech right now. Every product has agents; almost none of them agree on what that means. But the fog around the word is no excuse to wait — the teams pulling ahead right now are the ones who stopped debating definitions and started shipping. So let me plant a stake in the ground and, more importantly, tell you what to do about it: what agentic actually is, the different shapes it takes, why it marks the arrival of a genuinely new kind of worker — and where to start this quarter.

First, the distinction that matters most

The single most useful thing you can learn about this space is the difference between an LLM-enabled application and an agentic application. They look similar from the outside — both “use AI” — but they’re architecturally worlds apart.

In an LLM-enabled app, the language model is a feature. You wrote the control flow; the model fills a slot in it. Input goes in, the model returns text, and your code — or a human — decides what happens next. A “summarise this” button, a chatbot that answers from a prompt, a classifier that tags a ticket: all valuable, all LLM-enabled, none of them agentic.

In an agentic app, the model is the control flow. You give it a goal, not a task, and it decides the steps: it plans, calls tools, observes what comes back, and loops — adjusting until the goal is met or it hits a guardrail.

An LLM-enabled app executes a plan you wrote. An agentic app writes the plan, then executes it — and re-plans when reality pushes back.

Here’s the contrast in one table:

	LLM-enabled app	Agentic app
You give it	A task	A goal
Decides next step	Your code / a human	The model
Tools	Optional, fixed	Central — chooses which to call
Loop	One shot	Plan → act → observe → repeat
State / memory	Usually stateless	Carries context across steps
Failure	Returns an error	Notices, retries, re-plans
Mental model	A smart function	A junior colleague

That last row is the tell. If what you built behaves like a function, it’s LLM-enabled. If it behaves like a colleague you delegate to, it’s agentic.

The many shapes of “agentic”

“Agentic” isn’t one thing — it’s a spectrum of patterns. Most confusion comes from people arguing about different points on it. Here are the ones worth knowing:

• Tool-using single agent. One model in a loop with a set of tools (search, code execution, an API). The simplest true agent — and often enough.
• Retrieval-grounded agent (RAG). An agent that pulls in external knowledge before it acts, so its decisions are grounded in your data, not just its training.
• Orchestrated multi-agent. A planner decomposes the goal and delegates to specialists — a code agent, a review agent, a remediation agent — coordinating through shared state. More capable, more to govern.
• Deterministic agent workflows. Your code owns the control flow and calls agents to fill specific steps. You trade some autonomy for repeatability — the right call when the process is known and the stakes are high.
• Background / autonomous agents. Long-running or event-triggered agents that act without a human in the room — the 3 a.m. failed-deploy fixer.
• Copilots. Human-in-the-loop agents that work alongside you inline, suggesting and acting only with your nod.

And cutting across all of those is a ladder of autonomy — the dial that actually matters in production:

Assistive     ->   Supervised      ->   Autonomous
suggests,          acts, waits for      acts within policy,
human executes     approval             human audits after

Most real systems aren’t at one rung; they’re at different rungs for different actions. My rule of thumb: the more irreversible the action, the lower the autonomy — reading data can be autonomous, spending money or deleting things should not be. The art of agentic engineering is choosing the right rung per action, not maxing the dial.

Which brings me to the bigger idea: agentic as the new knowledge worker

Step back from the architecture and something larger is happening. For two centuries we automated muscle — machines that lift, move, and assemble. What we never automated was judgment: reading a situation, deciding what to do, and doing it. That’s knowledge work, and it’s been stubbornly human.

Agents change that. Not because they’re smarter than people — they aren’t — but because, for the first time, software can be handed an objective instead of an instruction. A tool-using, goal-seeking, self-correcting agent is, functionally, a digital knowledge worker: it researches, drafts, analyses, writes code, triages, and follows up — the substance of a great deal of white-collar work.

This reframes the whole conversation. The unit of AI stops being “a smarter autocomplete” and becomes “a coworker you can delegate to.” And once that’s true, the interesting questions stop being about models and start being about management:

• Delegation. What do you hand off, and how do you specify the goal well enough that an agent can run with it?
• Trust and verification. You don’t check a colleague’s every keystroke — you review outcomes. Agents need the same: verify results, not intentions.
• Least privilege. A new coworker doesn’t get the keys to everything on day one. Neither should an agent — scope its access to the job.
• Accountability. When an agent acts, someone owns the outcome. Auditable, event-sourced trails aren’t optional; they’re how you keep a human answerable.

If you’ve ever built and led a team, this should feel familiar — because it’s the same discipline. The org chart of the near future has agents on it, and our job shifts from doing the knowledge work to directing, constraining, and verifying a workforce that does it with us.

Which forces an uncomfortable question worth sitting with: if an agent can be handed an objective and pursue it — planning, using tools, correcting itself — then what, exactly, is the durable human advantage? I don’t think it’s raw output anymore. I think it’s taste, accountability, and knowing which goals are worth pursuing in the first place. That’s a very different career to be building toward than the one most of us trained for.

Don’t wait — start this quarter

Thinking is cheap; the compounding advantage goes to whoever starts. If you take one thing from this, make it an action:

1. Pick one painful, bounded workflow — a migration, a triage queue, a report nobody enjoys — and give an agent the goal, not a script.
2. Set the autonomy dial low to begin: let it propose, you approve. Move the dial up only where the action is reversible and the wins are obvious.
3. Instrument everything. Log every action and outcome from day one — you can’t trust what you can’t audit, and you’ll want the evidence later.
4. Review outcomes, not keystrokes. Manage the agent like a new hire: judge the result, coach the goal, expand the remit as trust is earned.

Do that once and the concepts in this post stop being theory — you’ll feel, in a single afternoon, the gap between an app that uses AI and one that acts.

The takeaway

If you remember one thing: an LLM in your app is a feature; an agent in your app is a colleague. Everything else — the patterns, the autonomy ladder, the governance — follows from taking that second sentence seriously.

Agentic isn’t a smarter chatbot. It’s the first time we get to automate judgment itself — less a new tool than a new hire. The question isn’t whether agents join your team; it’s whether you’ll have learned to lead them before they do. I’ll be writing a lot more about exactly that. More soon.