Stop Saying 'Please Do Your Best' to AI: Moving Beyond 'Vibecoding' to True Engineering

Imagine this: You have a junior developer who just joined your team, and you want to entrust them with a complex feature. If you simply told them, “Uh… just figure it out and make it look cool,” what would happen? A few days later, you would likely receive a mess of code that is completely different from your intent and nearly impossible to manage.

The ‘AI coding agents’ that have recently entered our lives are no different. For a while, when many people asked AI to code, they relied on what is known as ‘Vibecoding’—issuing vague commands like “write this nicely,” without any concrete engineering procedures [Source 1, Source 6, Source 9]. But that era is now coming to an end.

Why Does This Matter?

While ‘vibecoding’ might seem like it produces code quickly in the moment, it carries significant risks in a real-world professional setting. It is difficult to track who wrote the code or what process they used, and there are no standard procedures to follow when issues arise [Source 1].

Metaphorically, it is like driving a car without traffic laws like traffic lights or lane markers, relying solely on the driver’s mood. It is hard to know why an accident happened, and to others, it feels incredibly unstable. To make the AI agents we use act like ‘real engineers’ capable of managing and maintaining actual products, rather than just ‘auto-generators’ that churn out code, we need a systematic approach. Entering 2026, ‘Agent Engineering Frameworks’ are making software development via AI significantly more systematic [Source 16]. Developers are no longer letting AI code as it pleases; they are teaching AI the know-how accumulated by senior developers over decades in the form of ‘Skills.’

Understanding ‘Agent Skills’ Simply

Agent Skills are, in simple terms, ‘high-precision work manuals’ for AI agents [Source 5].

Think of it as giving a new developer a ‘work guideline’ used by the company. Instead of just telling them “Go code!”, you specify the procedures, such as “First, establish a plan in this order, pass these quality checks, and if a problem arises, fix it in this way” [Source 2].

An AI equipped with these ‘Skills’ operates as follows:

1. Installation: The developer installs a specific engineering procedure (skill) they want into the project’s internal folder (e.g., .claude/skills) [Source 5, Source 8, Source 14].
2. Command: When the developer enters a slash command (e.g., /run-tdd), the AI executes the procedure recorded in that skill perfectly [Source 5, Source 10].
3. Execution: The AI sets up a plan itself, reviews intermediate results, and strives to maintain the level of quality expected by human engineers [Source 2].

This allows you to freely combine professional engineering skills required for an AI agent, much like applying dozens of different filters to a photo app [Source 7].

Current Situation: How Far Have We Come?

The development of AI coding tools is moving very quickly [Source 19].

• 2024: It started as a supplementary tool at the level of simple Autocomplete [Source 16].
• 2025: Tools like Claude Code emerged, advancing to a level where they could handle multiple files simultaneously [Source 16].
• 2026: We have reached a stage where the AI’s behavior itself is ‘standardized’ through Agent Skills [Source 16].

Many experts are already using these agent skills to perform coding tasks in real production environments every day [Source 1, Source 13]. The era where you need to tell AI “just make it work somehow” is over.

What’s Next?

In the future, AI agents will increasingly transform into professional colleagues on our teams. Beyond simple coding skills, it is expected that AI agents equipped with their own automated engineering skills will play active roles in various fields such as sales, marketing, and law [Source 16].

In software development, more people will contribute to the open-source agent skills ecosystem, and each team will build a skill set that embodies their own ‘development philosophy.’ It is no longer an exaggeration to say that a developer’s ability now depends not on ‘writing code directly,’ but on ‘how precisely and efficiently they can teach engineering procedures (skills) to AI.’

MindTickleBytes’ AI Reporter Perspective

Expecting ‘vibes’ from AI is romantic, but in business, it’s dangerous. Introducing agent skills is the first step toward transforming AI from a ‘tool’ that just does what it’s told into a ‘verifiable professional’ you can trust and rely on. Coding is evolving beyond the question of “how to implement it” to the question of “what procedure should be followed.”

References

1. GitHub - mattpocock/skills: Skills for Real Engineers
2. Production-grade engineering skills for AI coding agents

3. [Skills For Real Engineers — AI agent skills

   Surf Skills](https://surfskills.surf/s/mattpocock/skills)

4. AI Coding for Real Engineers
5. AI Skills for Real Engineers
6. Matt Pocock Skills: AI Agent Tools for Real Engineering
7. Skills for Real Engineers: Empower AI coding agents
8. GitHub - kroffske/grillme: Skills for Real Engineers
9. Matt Pocock’s 16 Agent Skills — Real Engineering, Not Vibe Coding
10. Discover and install skills for AI agents.
11. Full Guide to Qwen CLI: Configuring MCP, Agent Skills, and Rules
12. Skills for Real Engineers — Skills for AI agents by Matt Pocock

13. [Emil Design Eng

    ClaudeCodeSkills](https://claudemarketplaces.com/skills/emilkowalski/skill/emil-design-eng)

14. AI Engineering Trends in 2025: Agents, MCP and Vibe Coding
15. Agent Skills Framework Revolution: Vibe Coding to Real Engineering
16. What It Takes to Build AI Skills Engineers Need in 2025

17. [Latest AI Coding Tools

    agprojects](https://agprojects.tech/blog/latest-ai-coding-tools-what-s-new-in-2025)