Why Your AI Coding Agent Keeps Getting It Wrong

We have been running AI-powered development projects for a while now — for clients in recruitment, financial services, and enterprise software.

The pattern we see most often is not "the AI got it wrong."

It is "we did not tell it clearly enough what we wanted."

Here is the uncomfortable truth: the biggest bottleneck in AI-assisted development is not the model, the context window, or the tooling. It is the human giving the instructions.

A real example

Tell an AI agent: "Add a feature to manage items from the backoffice." It will write something.

It will look fine.

Then you will find it inserts duplicates, has no access control, and talks to the wrong system — because none of that was in the spec.

The agent had to guess. Some guesses are right. Some are not.

What is changing across the industry

The entire ecosystem — GitHub Spec Kit, OpenAI Symphony, Claude Code plan mode — is converging on one idea: write a proper spec first, then let the agent implement it.

It is called Spec Driven Development (SDD). Four steps:

• Specify what you want (functional, not technical)
• Plan how to build it (architecture, data models, constraints)
• Break it into small, ordered tasks
• Implement one task at a time with the agent

What we are seeing at Evangelist Apps

We have applied this across Dynamics 365, mobile, and full-stack AI projects.

The quality jump is real — especially for complex multi-domain changes where "obvious" business logic is not obvious to an AI agent at all.

The honest trade-off: SDD uses more tokens upfront. For a quick bug fix, it is overkill. For anything production-grade — it pays for itself.

If your AI development results feel inconsistent, the fix usually is not a better model. It is a better spec.

Happy to share what is working for us — drop a comment or message me directly.

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