How To Recognise Agentic AI When You See It

Prologue

The confusion is understandable. In less than three years, we’ve progressed from AI systems that simply predict the next word in a sequence to intelligent agents that observe environments, form plans, execute complex workflows, and make autonomous decisions.

The terminology has exploded: chatbots, copilots, agents, agentic AI, autonomous systems. Each term carrying different implications for capability and organisational readiness.

For non-technical minds navigating this landscape, distinguishing genuine agentic capability from rebranded automation has become essential to sound decision-making.

This two part article returns to the Brainfood Maturity Model introduced earlier in this series. This provides a practical framework for recognising where solutions genuinely sit on the evolutionary spectrum from foundation models to multi-agent systems.

In other words, it allows you to assess your organisation’s readiness to deploy and scale these capabilities and so avoid one of the most common sources of failure and frustration being reported throughout 2025.

In part two, we zoom into Stage 3 of the model (where most deployments are currently focussed) by expanding it into ‘early’, ‘middle’, and ‘late-stage’ competencies with concrete use cases across eight business functions. Showing that even within a single stage, competency thresholds evolve and must be carefully assessed.

The use cases are included to help ground your understanding and improve your ability to spot differences. Hopefully that will rollup in contextual insight that helps in sound decision making.

Understanding the Core Features of Agentic Behaviour

Before exploring the maturity stages, we need to establish what genuinely qualifies as agentic behaviour which is what part one of the article concentrates on.

Much of what is called agentic today, barely qualifies against the full set of behaviours that’s considered genuine Agentic AI.

The path towards autonomous, adaptive decision making and execution is iterative based on organisational readiness. Even though some of the most advanced deployments have now crossed that threshold, all start on a tight leash prone to break as complexity increases.

For these reasons, it’s important to recognise the distinguishing characteristics that separate true agentic systems from traditional automation or reactive AI.

Here are six. They provide a clear view on why Agentic AI is of such strategic importance.

1. Environment interaction (perception): Agentic AI is constantly aware of what’s happening around it. It picks up information from many sources such as real-time data flowing from your systems, what people tell it, changes in your business environment, and even images or audio when relevant. It processes all these different types of information together to get a complete picture of the situation, then adjusts its approach based on what it learns.
2. Goal-oriented behaviour: Agents are designed to pursue specific objectives, optimising their actions to achieve desired outcomes rather than simply responding to prompts. They maintain persistent task orientation within explicitly defined scopes, understanding not just what to do but why they’re doing it.
3. Autonomy: Agents work on their own once deployed. They observe what’s happening around them, think through the situation, and take action toward a goal without waiting for a human to tell them what to do next. They learn and adapt as they go.
4. Workflow optimisation: Agents make workflows smarter by combining language understanding with thinking and planning. They figure out how to use resources efficiently, spot where work can be automated, and execute complex multi-step tasks. When something goes wrong, they recover from the error and adjust their plan rather than giving up. They break big tasks into smaller pieces, handle them in the right order, and replan if circumstances change.
5. Multi-agent coordination: Multiple specialised agents can work together on complex goals. They communicate with each other, break down big objectives into smaller tasks, assign work to the best resource, and coordinate to reach a shared outcome. Agents can also connect to external tools and systems such as email, search engines, document editors, databases to get work done across your entire organisation.
6. Learning capability: Agentic AI systems get smarter over time through experience. They learn from feedback. When something works well, they repeat it; when something fails, they adjust. They learn from patterns in large amounts of data and from every interaction they have. Unlike systems that stay the same after initial training, agents continuously adapt to new situations and information, becoming more skilled and effective the longer they operate.

The Core Behaviours of Agentic Systems

Do they work all the time as just described? No. It’s early days. The technology is still maturing and being tuned to work in the real world of messy, ‘less than perfect’ organisational conditions.

Are organisations ‘fit’ to run this marathon? Never at first. And probably only just even in organisations now recognised for making significant headway.

Even so, the core promise is extraordinary and a clear step change from what was previously possible.

So, is it for real?

This is an important question we all need to have informed opinions about. Those who are content to just believe the marketing hype are as likely to make wrong decisions as those who see it as an elaborate hoax.

Much better to be critically curious and dig into what’s really going on. In that spirit, here’s the next level of explanation. It’s a ‘how they work under the bonnet’ description using language designed to resonate in a non technical mind.

Agentic systems demonstrate operational patterns that distinguish them from static AI models. These behaviours create closed feedback loops enabling agents to operate autonomously, adapt dynamically, and coordinate intelligently in the ways just described. Here are the core ones:

1. Observe (perception): Agents constantly pay attention to everything happening around them: what people ask them to do, answers they get back from company systems, real-time data, and changes in how things are running. As new information arrives, agents update their understanding of the situation, so they can maintain awareness of what’s happening over hours or days, not just in a single moment.
2. Reason (deliberation): Before doing anything, agents ‘think’ through the situation. They interpret what they’re being asked to do, figure out what’s needed, and decide on the best course of action based on the information they have. Agents work through problems step-by-step: they think about what to do, take action, see what happens, learn from the result, then think again. They repeat this cycle of ‘think, act, observe, think again’, until they solve the problem.
3. Plan (goal decomposition): Agents work backwards from a goal. They figure out what needs to happen in what order to achieve that goal, then plan those steps before executing them. For complex goals, agents break the work into smaller chunks, assign each piece to the right tool or person, and make sure everything happens in the right sequence so nothing gets blocked waiting for something else.
4. Act (tool use and execution): Agents make their decisions actionable by connecting to company’s systems and tools. They can look up information in databases, send messages, update records, or start workflows. The key difference is that agents can access and use real-time information rather than relying only on what they were trained on. So, they always have current, accurate data to work with.
5. Observe (feedback and reflection): After taking action, agents look at what happened and evaluate whether it worked. They ‘think’ about what went right or wrong, learn from their mistakes, and remember those lessons for next time. If something didn’t work, they figure out why and consider better approaches for similar situations in the future.
6. Learn and adapt (memory and iteration): Agents have different types of memory to keep track of information over time. They remember what they’ve done and the feedback they received, they know domain-specific information relevant to their work, and they can find similar past situations to learn from. They improve their approach through feedback loops: the more they do something, the better they get at it, learning from each interaction.

Simple use cases might only need a single agent. As job complexity increases multiple agents work together. They communicate with each other, divide up work based on who’s best suited for each task. Make decisions as a team, and adjust their roles as needed.

The overall impact of these behaviours working together is to transform AI from simply answering questions when asked (Generative AI) into systems that actively pursue goals and manage complex work over extended periods without constant human direction (Agentic AI).

Please remember that while non technical language helps develop a mental model of what’s going on, these behaviours do not map directly onto human equivalents. How an agent acts, learns or observes is different. If you are curious, copy and paste the behaviours listed above into an LLM and ask for a technical explanation of what’s going on. It will sound less human but also more complex with a new set of concepts to absorb.

And here’s a final point everyone should log.

Even though there is overwhelming encouragement to relate to this functionality as AI co-workers, there are not and never will be the same as us. Instead, leading practioners recognise that there is synergy when the strategy is to compliment not compete.

You Might Be Eager, But Are You Ready?

So that’s what Agentic AI does and how it does it. In the context of any truly empowering technology, it promises to remove humdrum routine at the heart of daily work life. Exactly how that saved time is re-invested is for another article.

But if we concentrate exclusively on the positives of this promise, it’s understandable many want to rush at the opportunity assuming they can simply swap out one way of working for another. Like replacing a dishwasher.

The truth is quite different. Imagine you decide to run a local marathon within the next 48 hours without any training or preparation that your body and mind require to achieve that goal.

Most instantly recognise the foolishness of that impulse. In truth, we only get there as fast as we can adapt to the demands of the mission. To that end, maybe we download a health app to help organise us, track our progress and suggest how to reach our goal of being ready to run that marathon.

The Agentic AI equivalent is a maturity model. One that synthesises emerging good practice to provide a structured lens through which to assess both technological capability and organisational readiness.

Each stage in the model represents a fundamental shift in how AI systems operate and the competencies required to deploy them successfully. Like building the stamina for five to ten miles runs then progressively to the full marathon distance.

What are the equivalent milestones in Agentic AI?

Stage 1: Foundation Models are trained to predictively generate text, images, or other outputs. They excel at pattern completion but operate without agency, autonomy, or persistent memory. Think of these as sophisticated autocomplete systems. Impressive in their linguistic capabilities but entirely reactive.

Stage 2: Chatbots apply foundation models to engage and answer questions through conversational interfaces. They understand context within a single dialogue but lack the ability to take action beyond generating responses. These systems transformed customer service by handling routine enquiries, but their impact remains constrained to information provision rather than task execution.

Stage 3: Workflow Systems represent the critical threshold where AI transitions from generation to execution. These systems are trained to use tools and act with parameters: querying databases, triggering processes, updating records, orchestrating multi-step workflows. This is where most of the market currently operates, with varying degrees of sophistication from early-stage task automation to late-stage complex workflow orchestration.

Stage 4: Autonomous Agents are trained to observe, plan, and reason independently. They can monitor environments, identify patterns, formulate strategies, and take proactive action with minimal human oversight. Where Stage 3 agents respond to triggers, Stage 4 agents initiate action based on environmental observation and strategic reasoning. They operate end-to-end with minimal oversight, handling exceptions and edge cases autonomously.

Stage 5: Multi-Agent Systems involve networks of specialised agents trained for collaborative achievement. These systems distribute complex work across multiple autonomous agents, each with domain expertise, coordinating through sophisticated orchestration layers. Agents communicate, negotiate, delegate, and collectively solve problems that exceed the capability of any single agent.

These descriptions help us understand a crucial point. Only Stages 4 and 5 genuinely qualify as “agentic” according to the defining characteristics outlined earlier.

Stages 1-3, whilst powerful and valuable, operate with constrained autonomy and limited proactive agency. This distinction matters profoundly for business case development and resource allocation decisions as we will explore in part two.

Moving across these maturity stages is influenced by two prime factors. Technology and Organisational readiness. The first pulls us forward. The other constrains that momentum. Both need in depth understanding which we will do in the second part of this article.

Meanwhile it’s time to reflect on what’s been covered and whether your ability to recognise Agentic AI is any stronger as a result of what you’ve learnt from reading at this point in the exploration.