Why most AI development tools disappoint (and what we did about it)

There’s a pattern playing out across every industry right now. A company buys an AI tool. The demo was impressive. The first week feels like magic. By month two, the tool is gathering dust — or worse, creating work instead of saving it.

AI development tools are no exception. And after building one ourselves, we think we know why.

The model isn’t the problem

When an AI coding tool produces bad output, the instinct is to blame the model. It’s not smart enough. It hallucinated. It doesn’t understand Salesforce. But in most cases, the model is perfectly capable of doing the work — if it knows what work to do.

The real problem is context.

An AI model is only as good as the information it has when it starts working. Give it a vague request and access to a large codebase, and it will produce something that looks plausible but misses the point. Give it a precise problem description, a scoped plan, and clear constraints, and the output is dramatically better.

This isn’t a Salesforce-specific insight. It’s how large language models work, full stop. The quality of the output is determined by the quality of the input. The industry calls this “context engineering” — and it might be the most important factor in whether an AI tool actually delivers value.

The gap between “use AI” and “get results from AI”

Most AI development tools work like this: you describe what you want, the AI generates code, you review it. It’s a single step. Prompt in, code out.

That sounds efficient. In practice, it means the AI is doing the hardest part of software development — understanding the problem, deciding on an approach, and writing the implementation — all at once, with nothing but a natural language description to go on.

	Typical AI tool	Mayflai
Input	A prompt describing what you want	A groomed requirement, a technical plan, and org-specific context
Process	One shot: prompt → code	Four phases, each building context for the next
Validation	You review the output	The agent validates against your org continuously
Platform knowledge	General-purpose	Salesforce metadata model, deployment rules, and best practices built in
When errors happen	You fix them	The agent catches and fixes them before you see the result

Human developers don’t work this way either. A senior developer who jumps straight to coding without understanding the requirements or thinking through the approach isn’t being efficient — they’re being reckless. We wouldn’t accept that from a person. Why do we accept it from AI?

How Mayflai solves this

When we designed Mayflai, we didn’t start by asking “how do we build an AI that writes Salesforce code?” We started by asking “what would a developer need to know to do this well?”

The answer became our process. Every change in Mayflai moves through a series of phases, and each phase exists to build context for the next one:

Grooming — An AI agent works with you to understand what you’re trying to achieve. Not “write an Apex trigger” but why, for whom, under what constraints. The output is a structured, scoped description of the change — the kind of brief a senior developer would want before starting work.

Planning — A second AI agent takes that brief and produces a concrete technical plan. Which files need to change. What the changes look like. What dependencies exist. What could go wrong. Unlike general-purpose AI coding tools, this agent has deep knowledge of Salesforce’s metadata model, deployment quirks, and platform best practices baked in. It knows that deploying a custom field means deploying the parent object too. It knows which metadata types can’t be packaged together. It accounts for the things that only someone with real Salesforce experience would think to check — before they become deployment failures.

Building — Only now does the developer agent write code. But it’s not working from a vague prompt. It has a clear problem statement, a technical plan, and specific instructions. It knows what to build, where to build it, and what success looks like. And it doesn’t just write the code and hand it over — it continuously validates its changes against your actual Salesforce org, catches errors, and fixes them before you ever see the result. Each validation error is itself context: a real signal from your org about what works and what doesn’t. The builder learns from every failed check-only deployment and adjusts, turning Salesforce’s strictness into an advantage rather than an obstacle.

Deployment — You’re in charge. The build is written, validated, and all tests have passed. When you’re ready, press the button and your new functionality lands in your org. No handoffs, no waiting on someone else’s schedule.

Each phase is a checkpoint. You can review, adjust, or redirect before the next phase begins. The AI isn’t working autonomously in the dark — it’s working within a structure that keeps it focused and accurate.

Why this matters for your team

In early testing, changes that went through the full grooming-planning-building flow consistently deployed on the first attempt — while “just build it” prompts, even to the same model, routinely hit validation errors that required manual intervention.

The difference wasn’t the AI. It was the context.

Higher accuracy. When the AI knows exactly what to build, it builds the right thing more often. Fewer review cycles, fewer rejected changes, less time spent fixing AI-generated code.

Lower risk. Each phase is a checkpoint. A misunderstanding caught during grooming costs nothing. The same misunderstanding caught after deployment costs hours — or worse.

Predictable quality. The output quality doesn’t depend on how good your team is at writing prompts. The process itself creates the context. A junior admin using Mayflai gets the same structured input to the AI as a senior developer would.

Compounding value. Every completed change adds to the system’s understanding of your org — your naming conventions, your object model, the patterns your team uses. The tenth change is faster and more accurate than the first, because the context it builds on keeps growing.

The process is the product

The AI models will keep getting better. Every few months, a new model comes out that’s faster, cheaper, and more capable. But a better model with bad context will still produce bad results. And a good model with great context will produce great results.

That’s why we invested in the process, not just the model. Mayflai isn’t an AI that writes Salesforce code. It’s a system that ensures the AI has everything it needs to write the right Salesforce code.

The companies getting real value from AI aren’t the ones with the most advanced models. They’re the ones who’ve figured out how to give AI the right context at the right time. That’s what we built.