There’s a lot of talk right now about AI’s impact on software development. Many developers are worried about declining code quality or even being replaced. But let’s be honest: AI itself isn’t the problem — it’s how most developers are using it.
Common Misuse of AI in Development
Here’s what I see happening far too often:
- Developers paste large prompts into an LLM, expecting perfect, production-ready code.
- They get mediocre results and blame the AI.
- They fail to understand the very real technical limits of these systems.
But this isn’t a tooling issue — it’s a misunderstanding of the technology.
The science is clear:
- LLMs struggle with long prompts and lose accuracy in the middle of large contexts (Lost in the Middle).
- Single-shot prompting is inefficient for anything non-trivial.
- Long chained dialogues often degrade performance across turns (Multi-turn degradation).
What’s Actually Required: A New Mindset
The solution isn’t to stop using AI, but to stop misusing it.
We need to shift our thinking:
👉 The future isn’t about writing the next PHP class by hand.
👉 It’s about designing workflows and orchestration layers — building agents that build software for us.
In short: we should be building factories that build factories.
That’s what it means to become an AI Agent Developer:
- Not throwing prompts at a black box.
- Carefully designing task decomposition.
- Creating specialized, reusable micro-agents for well-scoped problems.
A Starting Point for New AI Agent Developers
If you’re just starting your journey into this world, here’s what I recommend:
✅ Learn the technical limits:
Understand why long contexts, single-shot prompts, and chained dialogue degrade output quality.
✅ Break problems into smaller pieces:
Agentic development means orchestrating small, specialized, auditable agents.
✅ Design workflows, not just prompts:
An AI Agent Developer is closer to a workflow architect than a traditional coder.
✅ Get educated properly:
Don’t rely on casual experimentation. Read the literature, study architectures like micro-agent systems (Micro-Agent reference), and learn how others are successfully building agentic systems.
✅ Stop blaming AI for bad results:
AI gives poor output when we give it poor input and poor architecture.
Where We’re Headed
AI won’t replace developers — but we’re already transitioning into a new kind of craft. We’re becoming AI workflow engineers and agent designers, especially in domains like eCommerce.
The core takeaway is simple:
Fix the input, not the output. Design correctly, and AI will work exactly the way we want it to.
That’s the next frontier.