Prompt engineering was briefly the face of the AI jobs boom.
In 2025, technology recruitment firm SPG Resourcing reported that UK job listings for AI prompt engineers had grown by 180% over the previous year.
It was an eye-catching figure that captured the mood of the first commercial wave of generative AI. Businesses were trying to understand how to talk to LLMs and turn early experiments into something useful.
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That requirement has not disappeared.
CEO & Co-Founder of SurrealDB.
With job site postings for specialist AI roles in the UK rising by 61% from last year according to PWC, it’s clear that good prompts still matter. But most of that new demand is for people who can apply AI inside a business, not just talk to a model.
Many organizations have moved past the first demo. They are now trying to build AI agents, retrieval-augmented generation (RAG) systems, and AI-enabled workflows that operate inside the business.
That creates a different skills gap.
This is the shift behind what I refer to as ‘context engineering’. Although that term is not universally used, the capability is becoming essential.
Companies that want useful AI agents and retrieval-augmented generation systems need people who can design the environment around the model, not just the prompt sent to it.
From better prompts to better context
Prompt engineering is about the instruction, while context engineering is about the world around that instruction.
A support agent does not only need a well-written prompt, it needs the right customer record, policy, product history, and permission boundary. Similarly, a developer agent needs the relevant code, tests, dependencies, and deployment constraints.
In both cases, output quality depends on context. Without it, the model is guessing from incomplete evidence. With too much of it, the system becomes noisy and difficult to govern. The job is to make context useful, current, and controlled.
That’s what makes context engineering distinct from prompt engineering.
Agents raise the stakes
The rise of AI agents makes this more urgent. A chatbot with poor context may give a weak answer, but that same poor context may result in another agent making a serious error.
Once an AI system can call tools, query business systems, maintain state, and act across several steps, context becomes an essential part of the production architecture. It decides what the agent can see, what it can do, and how much confidence the business can place in the outcome.
I’m reminded of a joke about boundary testing. A developer walks into a bar and orders a beer, then he orders five beers, then he orders 999,999,999,999 beers, then he orders -1 beer. The bartender blinks, but everything is okay. A user walks into the bar, asks where the bathroom is and the whole bar explodes.
The same principle applies to AI projects. The first prototype may work against a narrow set of examples, then become fragile when it meets real data. Customer information sits in one system, operational data in another, and important knowledge in documents and files. The agent is expected to reason across all of it, but the context layer has not been designed for that job.
A financial services team, for example, may need to connect CRM data, an existing data platform, and internal documents before an agent can answer accurately. The hard work is not only moving the data. It is shaping it so the agent can retrieve the right evidence and stay inside the right permission boundary.
The job title is still catching up
This creates an awkward hiring moment. The need for context engineering is becoming clearer, but the job title is still unsettled.
Some organizations may seek to specifically hire ‘context engineers’, but many will not. The capability is more likely to appear inside roles such as AI engineer, agent engineer, AI platform engineer, applied AI engineer, or data engineer. In other businesses, it will be a team responsibility shared across data, platform, security, and software engineering.
Leaders therefore need to hire for the work, not the label. A candidate does not need to have ‘context engineer’ on their CV to be useful. The better signal is whether they understand how data moves through systems, how permissions are enforced, and how a prototype becomes something reliable enough for production.
This also means the talent pool is wider than many companies assume. Machine learning expertise is valuable, but context engineering draws heavily on existing engineering disciplines. Data engineers understand pipelines and retrieval.
Platform engineers understand operational resilience. Security teams understand access control and auditability. Software engineers understand how to turn messy requirements into maintainable systems.
The best candidates may look like full-stack AI engineers. They do not need to be specialists in every model, database, or framework, but do need enough range to connect the model layer with the business systems around it.
The Kubernetes lesson and what leaders should do now
The shift has a parallel with the move to cloud-native architecture and Kubernetes. Many companies treated Kubernetes as something to install, then discovered that the harder work was changing how teams built and ran software.
AI creates a similar risk. Companies can buy tools and hire a handful of specialists, but still fail to change the engineering habits around them. Context engineering requires teams to think differently about everything from documentation, and data ownership, to access, testing, and accountability.
It also changes the culture of software development. Engineers are already using AI to write, review, and iterate code. That can improve productivity, but it does not remove responsibility. In areas where performance, reliability, or security matter, human judgement becomes even more important.
CTOs and CIOs should not wait for context engineering to become a mature hiring category. They should start identifying the capability now.
The first step is to examine where AI projects are failing. Is the model genuinely weak, or is the system retrieving poor context? Are permissions clear? Can the team explain why the agent produced a particular answer?
The second step is to build cross-functional teams. AI cannot sit apart from data, platform, security, and product. In many cases, the best approach will be to upskill existing engineers who already understand the organization’s systems.
The final step is cultural. Engineers need to become fluent in AI-assisted development while staying accountable for the systems they ship. Leaders need to make room for experimentation, but they also need clear standards for review, evaluation, and governance.
The model is not enough
AI hiring is changing because AI itself is moving into production. Models will continue to improve, and businesses will have many ways to access them. The harder advantage will come from knowing how to connect those models to the right business context.
Companies that understand this will build agents and RAG systems that are more useful, safer, and easier to govern. Companies that ignore it will keep blaming the model when the real weakness is the environment it has to work in.
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