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Artificial Intelligence & Machine Learning
May 27, 2026
Before The First Prompt: What Agents Actually Need
The AI industry has spent the last two years telling us that agents are coming. Systems that don’t just answer questions but take actions - browsing, coding, filing, deciding - with minimal human involvement. And by most measures, the capability is here. The models are good enough and the tooling is mature enough.
So why aren’t organisations running fully autonomous AI workflows at scale?
Most organisations would say the issue is trust. But trust is a symptom, not a cause. The real bottleneck is something more mundane and more fixable: context. Specifically, how well organisations equip their agents with the information they need to act correctly - and how clearly they define the moments when a human still needs to be in the room.
From Assistant to Agent
For most of the last decade, AI in the enterprise meant a chatbot on a website or an algorithm quietly scoring applications in the background. Useful, but bounded. You asked, it answered. You fed it data, and it returned a prediction. The human was always the one deciding what happened next.
Agentic AI changes that relationship. An agent doesn’t just respond - it plans, executes, and iterates. Ask it to pull together background research and a first-cut proposal for a new client pitch, and it might search for relevant material, structure a narrative, draft the document, and flag gaps in the information it has found, all without being prompted at each step. The human sets the goal; the agent works out the path.
Under the hood, that may involve multiple systems, retrieval steps, and decision points - but from the user’s perspective, the agent is operating toward an outcome rather than responding to a single prompt.
That shift in autonomy changes the nature of the risk. With a chatbot, a poor output is a poor answer - easy to spot, easy to correct. With an agent, a poor output is a poor action, and actions have consequences that build on one another. A misunderstood instruction doesn’t produce a wrong sentence. It produces a sequence of wrong steps, each compounding the last.
This is why the conversation about agentic AI can’t stay at the level of capability. The more important question for any organisation considering this technology is: what does it need to know before it starts, and what happens when it gets it wrong?
Context Is The New Briefing
Think about how you’d onboard a capable new contractor. You wouldn’t hand them a task and walk away. You’d give them background on the client, explain what’s already been tried, set out what success looks like, and flag the constraints they can’t cross. The quality of that briefing determines how much you can trust them to get on with it.
Agents work the same way, except the briefing isn’t a conversation over coffee. It’s the context window: everything the agent knows before it takes its first action. The goal, the constraints, the current state of the work, the definition of done. Get that right, and the agent operates with real reliability. Get it wrong, and you don’t get a failed agent - you get a confident one, heading in the wrong direction.
This is the part of agentic AI that doesn’t get enough attention. Organisations invest heavily in choosing the right model, wrapping it in the right tooling, and governing access. Far less thought goes into the quality of the context they’re handing it.
What does it actually need to know?
What assumptions is it likely to make if that information is missing?
Where will ambiguity cause it to fill in the gaps?
Context isn’t a prompt. It’s a discipline. And for organisations that want to move beyond experimentation into production-grade agentic workflows, getting that discipline right is the difference between a system that works and one that confidently doesn’t.
The Human In The Loop Isn’t Disappearing, It’s Moving
There's a persistent concern in conversations about agentic AI that automation means removal; that as agents take on more, humans get squeezed out. It's an understandable worry, but it misreads what's actually happening. The human role isn't shrinking. It's shifting, and when done well, that shift is precisely what makes agentic AI more effective than the tools that came before it.
When using a basic AI tool, human involvement tends to cluster around the output: you review what it produced and decide whether it's good enough. That works when the task is contained, but it doesn't scale. With agentic AI, the more consequential human role moves upstream, such as designing the conditions under which the agent is likely to get things right across a much longer sequence of actions. Instead of just reviewing the output, you're defining the brief, the constraints, and the criteria for success before the agent starts. Output review doesn't disappear, but it becomes the exception rather than the default touchpoint.
The other lever is escalation and knowing in advance at what point the agent should stop and involve a person. Not every step needs a checkpoint, and over-supervising an agent undermines the efficiency it was introduced to create. But the right escalation design, applied selectively, is what allows an agent to operate with genuine autonomy across the rest of the workflow. A communication going to a customer. A decision that materially changes the scope or spend. A step that's difficult to reverse. Define those moments clearly, and everything in between can move faster.
Autonomy Is A Dial, Not A Switch
One of the most common mistakes organisations make when adopting agentic AI is treating autonomy as a binary. Either the agent is in control, or it isn’t. Either you trust it or you don’t. This framing makes the decision feel harder than it needs to be and often leads to two failure modes: over-supervision that removes the efficiency gains, or an uncalibrated leap that creates more risk than it reduces.
The more useful mental model is a dial. You start with the agent operating in a narrow, well-understood slice of a workflow, with rich context and clear checkpoints. As it demonstrates reliability, as you understand where it performs well and where it needs guidance, you extend the range. More autonomy over more of the process, as trust is built rather than assumed.
What makes that dial turn isn’t confidence in the technology. It’s the quality of two things: the context you’re providing, and the clarity of your escalation design. Richer context means fewer bad assumptions. Clearer escalation points mean fewer situations that are difficult to recover from. Together, they create the conditions for autonomy to grow in a controlled way.
Organisations that treat context-setting as a one-time setup task tend to plateau. The ones that treat it as an ongoing discipline by refining what the agent knows and tightening escalation triggers as they learn are the ones that keep making progress.
The Competitive Edge Is Human Judgment, Applied Earlier
The organisations that will extract the most value from agentic AI over the next few years won’t necessarily be the ones running the most powerful models. They’ll be the ones that have developed the organisational discipline to use them well. And that discipline starts with context.
Getting agentic AI right is, at its core, a human challenge. It requires clarity about what you’re trying to achieve, honesty about what the agent needs to know to achieve it, and deliberate thought about where human judgment still belongs in the process. None of that is technically difficult. But it does require organisations to articulate something they’ve rarely had to make explicit before: what does good look like, and how do we communicate that to a system that will act on it?
The capability is no longer the constraint. What separates organisations seeing real returns from those still in proof-of-concept is the rigour they’ve applied to the human side of the equation, not just on the technology.
As agents become more capable, that rigour will matter more, not less. The most valuable skill in an AI-enabled organisation won’t be knowing how to build an agent. It will be knowing what to tell one.
If these questions are ones your organisation is working through, we’d be happy to talk about how we approach them. Get in touch or explore our work here.
Gavin Squibb | Senior Software Engineer at Ntegra
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