Crafting the exact phrasing of instructions for AI, called prompt engineering, became popular because early AI work looked like a wording problem. A clearer instruction often produced a better answer, so prompts felt like the main advantage. That changed with AI agents, systems that can take steps and use tools by themselves. Innovation strategist Shawn Kanungo argues that prompt engineering is dead in an agentic era, because the context around the work is the stronger input.
A prompt still starts the task, but context shapes the work that follows. A new hire works the same way, becoming useful once they understand the company’s goals, internal documents, current work and expected processes. AI agents need the same kind of onboarding, and weak onboarding creates generic work, extra review and slower delivery.
Giving AI the right background is called context engineering. A 2025 paper says an AI model’s performance is shaped by the information it is given while in use. That turns AI improvement into a management problem. The question for a company becomes what the model needs to know before it begins. That includes company documents, examples of good work, internal rules, customer context, approval limits and the steps people already follow.
This becomes practical in Agent Skills, which the AI company Anthropic describes as folders of instructions, scripts (small programs) and resources that an agent can load when needed. Its engineering team says real work requires knowledge of procedures and the organisation, and compares building a skill to creating an onboarding guide for a new hire. Like a standard operating procedure, a skill tells the agent what to do, which sources to use and which steps to follow. It also marks where the guardrails sit and when the work should stop.
So a company with messy documents and unclear workflows will get weak AI performance, even with better prompts. A company with clean context and clear skills will get more useful work from the same tools. Its best process becomes a shared asset that teams reuse. When an agent makes a mistake, the fix goes back into the skill as a better example, a clearer rule, a missing document or a tighter process.
