As technology reshapes the workplace, professionals are re-evaluating which skills will matter most for their future. At the same time, the speed at which AI technology is evolving makes it harder to predict which ones will be most needed.
One such skill, prompt engineering—crafting instructions that guide GenAI to give desired results—seems to have lost some of its appeal due to advancements in large language model (LLM) services. However, the opposite is true. Whether using chatbots, chat features, or agentic AI systems or agents (semi-autonomous systems that can perceive, decide and act), being able to clearly converse with AI gives those with prompt engineering knowledge and AI literacy a distinct advantage inside and outside of the workplace.
Applying communication principles to AI
Engaging effectively with AI—be it a chatbot, virtual assistant, or automation system—has surprising similarities to effective human communication. A foundational framework, originally articulated by scholars in the 1950s, outlines seven essential communication traits: being complete, concise, considerate, concrete, courteous, clear, and correct. These traits, long applied to public relations and professional discourse, are becoming increasingly relevant in our interactions with intelligent systems.
While the medium is different, many of these framework principles are applicable to communicating with AI. One crucial capability is resource curation. It is the strategic management of all assets available to AI agents, including information, APIs, computational tools, and financial resources. With resource curation providing the groundwork, two sub-skills—context curation and intent framing—are proving essential for AI system interactions.
Context curation and intent framing: Core skills for the AI era
Context curation refers to the deliberate selection and structuring of relevant information to provide AI systems with the necessities to generate meaningful and accurate results. This can include data from both structured and unstructured sources, as well as documentation, examples, or historical interactions. Conversely, intent framing is about setting clear expectations for what an AI system should accomplish by defining the task, constraints, tone, and outcomes. These two skills—context curation and intent framing—ensure AI tools generate outputs aligned with user goals and broader organizational objectives.
Together, these skills help maximize the effectiveness and efficiency of AI applications across various contexts, including code generation, business process automation and strategic planning. As AI integrates into workflows, the ability to shape both the input and request becomes central to creating value and ensuring human oversight. To put it another way, organizations that control their flow of information and define output expectations will have greater control over their digital future.
Bringing it all together: Practical examples
As you consider the AI literacy skills you and your teams need to develop to master prompt engineering in the age of agentic AI, consider these examples:
1. A home improvement retailer is deciding if they should invite a service provider to participate in an RFP for a digital transformation project. They deploy AI agents to assess the provider's suitability and use the following steps to create their prompt.
- Context curation: The AI agents gather information from the provider's website, service offerings, case studies, industry articles, open web information, and past engagement history. This mix ensures a well-rounded and current understanding of the provider's capabilities.
- Intent framing: The agents use the gathered information to create the "intent" portion of their AI prompt: "Evaluate how well this provider's digital transformation services align with our project requirements. Highlight key strengths and any potential areas of concern. Ask for additional input if needed to improve the quality of your assessment."
2. A marketing strategist uses an AI agent to generate campaign content and audience insights.
- Context curation: The AI agent is fed curated inputs, such as past campaign performance data, customer personas, brand guidelines, and competitive intelligence from open sources. These inputs ensure the content generated aligns with the brand voice and current market dynamics.
- Intent framing: The strategist then defines the goal and sets clear expectations, such as tailoring messaging to a specific audience, adopting a persuasive tone and alignment with an upcoming product launch. These instructions guide the agent to produce content that is both on-brand and strategically useful.
3. A developer uses LangGraph (a framework used to build complex AI agents) to configure an AI agent ecosystem for automating customer support.
- Context curation: The AI agents are connected to prior support tickets, product FAQs, system documentation, and real-time data feeds. These curated resources and detailed contextual instructions ensure that responses are relevant and up to date.
- Intent framing: The developer sets agent behavior and output expectations, such as resolving issues on first contact, escalating complex issues to humans and maintaining a friendly tone. This framing helps align AI results with output quality standards.
Looking ahead: Preparing for agentic AI ecosystems
Over the next five years, enterprises will increasingly rely on agentic AI ecosystems, which are networks of AI agents operating semi-autonomously across departments, systems and workflows. For a CXO, this shift means rethinking ways to create, operationalize and scale value. Leaders who understand and champion context curation and intent framing will be better equipped to allocate resources wisely, safeguard enterprise decision quality, maintain strategic alignment across AI-enabled teams and most importantly, up their prompt engineering game.
For developers, fluency in context and intent means they will no longer just be writing code. Instead, they will be designing how intelligent agents interact, make decisions and learn. Moving from builders to alignment engineers, developers will become more responsible for ensuring agentic system results stay grounded in human goals.
A new form of literacy
Like the way we became comfortable using calculators and Google, context curation and intent framing skills are becoming the new digital literacy. Being able to ask the right questions, give the right background, and guide an intelligent agent toward a goal will be as fundamental as reading and writing, which is why I’m teaching these core AI literacy skills to my children.
Success in the era of agentic AI ecosystems will partly depend on the ability to master these skills, which will shape how your organization builds, guides and governs AI to stay aligned with human goals.
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