The Five Levels of Agentic AI Maturity

From Ad Hoc to Adaptive 

You’ve seen it.

Slack threads cluttered with prompt screenshots. One colleague shares a magical ChatGPT output that saved them three hours, another tries to remember which prompt actually worked last week. Someone builds a GPT to summarize reports. Someone else wires together a Frankenstein of tools using Zapier, a Notion database, and a Google Sheet. It works—until it doesn’t.

This is where many of the most forward-leaning organizations are right now: duct-taping intelligence onto brittle workflows, held together by hope and half-documented shortcuts. It sounds chaotic—and it is—but make no mistake: this is the leading edge. Because while these teams are out there experimenting, iterating, and occasionally breaking things, many others are still stuck using AI like it’s Google on steroids—isolated, transactional, and largely disconnected from real systems of work.

What we’re experiencing isn’t just another wave of automation. It’s the early, awkward rise of agentic AI—a new operational layer built not just on code, but on intention, attention, and execution. And it’s moving fast.

The question isn’t whether your business will use AI. It already is—whether you planned for it or not. If citizen developers have made up 30% of enterprise build efforts in recent years, it’s not a stretch to say that AI usage is already eclipsing that—happening across teams and roles in ways leadership often can’t yet see.

The real question is: do you know where you are on the curve—and what comes next?

Defining Agentic AI

When most people hear the phrase agentic AI, their minds jump to sci-fi tropes—autonomous bots, thinking machines, digital assistants with strong opinions and perfect recall. But in practice, agentic AI isn’t a singular system or sci-fi dream. It’s a spectrum—a range of increasingly capable systems that can perceive, decide, and act with varying degrees of independence, based on goals, context, and constraints.

At the lower end, that might look like a simple GPT tool used to generate email drafts or summarize documents. At the higher end, it’s an orchestrated network of tools, APIs, models, and memory—an AI that doesn’t just complete tasks, but sequences them, adjusts its workflow in real time, and makes contextual decisions along the way. It’s not a static app. It’s an evolving partner in your digital operations.

At the core of agentic AI is fusion—the merging of what we used to think of as separate domains:

• Deterministic logic: rules, structured data, repeatable processes.
• Probabilistic reasoning: natural language, uncertainty, nuance, creativity.
• Structured inputs like databases and dashboards, alongside unstructured inputs like emails, chat logs, PDFs, and live conversations.

Agentic AI thrives in that messy middle—where business logic meets ambiguity, and where intelligent systems can fluidly bridge those two worlds.

This shift isn’t just technical—it’s not even just architectural. It is a foundational paradigm shift. The way we build, delegate, and automate work is being redefined by systems that aren’t just tools, but collaborators—systems that can interpret intent, adapt to changing input, and reshape their behavior on the fly. Whether you’re deploying a lightweight assistant or orchestrating a complex, multi-agent workflow, you’re engaging with the early architecture of something new: operational intelligence that acts with a degree of purpose.

The Agentic AI Ladder

A Spectrum of Capability, Context, and Autonomy

AI systems don’t flip from “dumb” to “super smart” overnight. They evolve—sometimes awkwardly—through stages of increasing autonomy, contextual awareness, and orchestration complexity. That’s where the Agentic AI Ladder comes in.

This model maps six distinct levels of agentic AI capability, from isolated prompt tools to highly integrated, self-evolving systems. It’s not just about what the AI does—it’s about how it decides, adapts, and fits into the broader business architecture. Each level builds on the last, moving from tool to assistant to collaborator to co-architect.

Here’s what that progression looks like:

These levels aren’t just about technical progression—they represent entirely different relationships between humans and machines. At the bottom of the ladder, the AI is a tool. At the top, it’s a systemic force—a thinking layer that shapes how the organization runs.

This view of Agentic AI does not contradict Sam Altman’s (OpenAI) 5 Levels toward AI, it merely make a little bit finer distinctions regarding where we are today (levels 2-4 by Sam’s definition).

Each step upward changes the questions you need to ask:

• Who’s in control?
• What happens when the environment changes?
• Who—or what—defines the goal?

And maybe the most important question of all:

Where are you on this ladder today—and where are you ready to go next?

What It Means to Businesses

From Curiosity to Capability

The Agentic AI Ladder isn’t just a conceptual model—it’s a mirror. Because where your organization sits on that ladder is already shaping how fast you move, how well you scale, and how ready you are for what’s next.

The stakes? Competitive agility, operational scale, and workforce augmentation—the kind that makes the difference between shipping in days or drowning in backlog, between a business that reacts and one that adapts.

Let’s break it down with some real-world (and near-future) scenarios:

Level 0: Ad Hoc

• A marketing intern uses ChatGPT to reword a product description. It’s fast and clever, but the output vanishes into the ether—no trace, no system integration. Helpful? Sure. Scalable? Not at all.

Level 1: Reactive Agents

• A recruiting team uses a chatbot to auto-reply to inbound candidates based on keywords. It saves time—but if the context shifts (say, a hiring freeze), someone still has to reprogram the logic.

Level 2: Sequenced Autonomy

• A sales team sets up an automation to generate outreach emails, summarize LinkedIn profiles, and log everything to a CRM. The workflow works—but it’s rigid. Every change requires manual intervention.

Level 3: Adaptive Orchestration

• A customer support system uses an orchestrator AI to detect when complaints about a new product spike, then pulls data from tickets, FAQs, and product docs to generate a temporary help page—without being asked.

Level 4: Intent-Aware Systems

• An internal operations AI notices that inventory turnover is dropping. It proposes a new reordering policy, including safety stock and revised EOQs, then models out the potential impact—before anyone flags the issue.

Level 5: Self-Evolving Systems

• An enterprise resource planning system rewires itself in response to a market shift—adjusting payment terms, reorganizing logistics workflows, and updating data schemas across departments, all within governance policy.

This is the future businesses are walking into—some with awareness, confidence, and intent, some by accident.

And here’s the thing: playing with AI—prompting it, tinkering, automating a few things—isn’t the same as building with it. One is an experiment. The other is a strategic shift in how your organization functions.

The gap between those two modes is growing fast.

So, again, the question isn’t if your business is using AI. It’s how consciously, how cohesively, and how high up the ladder you’re willing to climb.

Where It’s Going (and Why It’s Moving So Fast)

From Hand-Coded Hacks to Agentic Infrastructure—in 12 Months

Not long ago, I was writing my own orchestration logic in Python—chaining LLM calls together, managing intermediate steps manually, juggling prompt templates in notebooks, and praying a variable didn’t vanish midstream. It worked… kind of. But it was fragile. One broken input or output, and the whole thing collapsed, or worse, looped endlessly.

That was less than a year ago.

Now? There are drag-and-drop orchestration platforms with embedded memory, goal-driven planning layers, multi-agent coordination, real-time tool switching, and persistent storage. You can build in hours what used to take days—and hand it off to non-technical users.

This is how fast the terrain is shifting. The same use case you coded in Python six months ago might now be solved with a few clicks in Make, N8N, LangGraph, or CrewAI. What felt like innovation yesterday is quickly becoming infrastructure.

But with that acceleration comes something else: instability.

• Tools are evolving faster than most businesses can vet them.
• Interoperability is still patchy.
• Standards for memory, agent state, and orchestration are still in flux.
• And the risk of lock-in—or worse, obsolescence—is very real.

So, what do smart businesses do?

They build adaptively.

That means choosing loosely coupled systems over monoliths. Separating logic from orchestration. Designing for reversibility and traceability. And resisting the urge to over-engineer when the terrain ahead is still shifting under your feet.

Because agentic AI isn’t just an emerging capability—it’s a moving target. And what matters now isn’t just building the smartest system—it’s building the most adaptable one.

What Comes Next

From Map to Movement

If you’ve made it this far, you already know this isn’t about AI hype. It’s about capability. Direction. Readiness.

The Agentic AI Ladder is a map—but it’s just the beginning. Because once you know where you are, the next question is: how do you move up, smartly, and without getting burned?

In upcoming posts, we’ll go deeper into the practical realities of operating in this fast-evolving space:

• How to Navigate AI Tool Chaos
• How to stay agile and modular while the orchestration layer keeps shifting.
• The Attention Gap: Why Today’s AI Can Act but Not Focus
• Why task execution isn’t enough—and how the future of agentic systems depends on managing what matters.
• Agent, Orchestrator, or Mind?
• A diagnostic for deciding what kind of intelligence you’re building—and how to align it with real business goals.

But for now, a simple question:

Where is your organization on the ladder—and where could it be six months from now?

Because of the gap between those two points, that’s where the future is being built.