Why agentic AI is about leverage, not automation
The true value of agentic AI comes from using it to do more with less
Moving beyond the automation mindset
Many conversations about AI in small and medium sized businesses start with automation, in an attempt to make current processes faster or cheaper. Whilst understandable, this framing is also limiting. It treats AI as just another efficiency tool, focused on doing the same work faster.
AI’s real impact isn’t defined by how much work it can replace, but by the level of responsibility it can take on. Agentic systems can interpret goals, decide what to do next, and adapt as conditions change. They don’t just accelerate workflows, they share the decision-making burden.
Framed correctly, agentic AI becomes a capacity booster, allowing businesses to pursue more opportunities, and operate at a scale that would normally require more people.
What is agentic AI?
At its core, agentic AI isn’t a single model or product. It’s a way of assembling existing AI capabilities into a system that can work towards a goal over time.
Technically, an agentic system brings together several components:
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A reasoning model, typically a large language model, which can interpret instructions, weigh options, and generate plans.
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A defined goal, which gives the system direction and a sense of what success looks like.
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Memory or context, allowing the system to retain information across steps.
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Access to tools and systems, such as email, databases, CRMs, APIs, or internal software, which allow it to take real actions in the world.
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A feedback loop, enabling the system to observe the results of its actions and respond accordingly.
An agentic system can interpret a goal, break it down into smaller steps, take actions to progress those steps, observe what happens, and adjust its approach if needed. This loop can run once or many times, depending on the task.
How this differs from traditional automation
Traditional automation is built on certainty. A rule is defined, a trigger occurs, and a predetermined action follows. This approach works well when processes are stable and predictable, but it becomes brittle as soon as real-world variation appears. A small change in input or context can cause the entire workflow to fail or require manual intervention.
Because of this, traditional automation requires that the world will behave as expected. Every exception must be anticipated in advance and encoded into the system. Over time, these automations tend to grow complex and difficult to maintain, especially in environments where change occurs frequently.
Agentic AI doesn't need every path mapped in advance
Agentic AI operates differently. Rather than relying on a fixed sequence of steps, it can tolerate ambiguity and adapt to change. When something unexpected happens, the system can reassess the situation, choose an alternative action, or escalate appropriately. It doesn’t need every path mapped in advance.
Traditional
Designed to execute predefined workflows as efficiently as possible.
- ■ Linear, rigid workflows
- ■ Predefined steps and logic
- ■ Optimised for speed and efficiency
- ■ Breaks easily when conditions change
- ■ Requires regular maintenance
Agentic AI
Designed to pursue goals, make decisions, and adapt to change.
- ■ Goal-driven
- ■ Interprets context and decides what to do next
- ■ Handles ambiguity and change
- ■ Value increases as intelligence rises
- ■ Reduces operational and decision burden
You are no longer encoding every step of a process. We, the domain experts, take a step back and define a goal, boundaries, and acceptable behaviour. The system uses these constraints to decide how best to move towards the goal. This is what allows agentic AI to remain useful in dynamic, imperfect environments where traditional automation can break down.
This is why agentic AI feels more like an assistant.
The junior assistant
Like a junior team member, an agentic system performs best when it’s onboarded properly. It needs context about the business, the tools it can use, and what success looks like. It needs clear guidelines that define what it should do, what it should avoid, and when it should escalate.
It also fails in familiar ways, when goals are vague. It may take actions that are technically reasonable but commercially unhelpful. When constraints are missing, it may overstep or behave inconsistently leading to slow and expensive outcomes. When domain knowledge is assumed rather than provided, errors and misjudgements become more likely.
The quality of output is less about technology and more about the quality of guidance.
This mental model is important because it shifts responsibility back to management. The quality of outcomes is less about the sophistication of the technology and more about the quality of guidance it receives, we’re talking about prompts here, written in English (or other languages!). Prompts should be treated as onboarding, not commands.
As with a junior assistant, agentic AI isn’t well suited to high-stakes judgement without oversight. It struggles when goals are poorly defined or constantly shifting, and its usefulness degrades quickly when data is incomplete or unreliable. Understanding these boundaries is key to using it effectively and building trust in its output.
Keeping humans “In The Loop” (pun intended)
Agentic AI works best with humans from the loop. It’s not about stepping back entirely, but about intervening at the right time.
Human intervention is valuable at three points:
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At the start, when goals, constraints, and context are defined.
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During exceptions, when something unexpected occurs or confidence is low.
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In review, where outcomes are assessed and guidance is refined based on what worked and what didn’t.
Feedback plays a central role. Agentic systems improve when their outputs are reviewed and corrected, and when those corrections are fed back into future behaviour. Over time, this reduces the need for constant oversight, but it never eliminates the need for accountability.
Leverage as the real opportunity
I like to think that the true value of agentic AI comes from leverage and not just a way to do the same work faster or at lower cost. Its real value lies in what it allows a small team to take on.
When used well, agentic systems increase capacity as well as throughput. They absorb coordination, enabling people to focus on decisions, relationships, and business direction. This is where leverage is created, not in shaving seconds off individual tasks.
The advantage will belong to those who learn how to delegate goals clearly, set sensible boundaries, and combine agentic AI with human oversight.
