GeneXus

GeneXus Blog | We invite you to read this article, where we explain how an AI agent interacts with the GeneXus KB, what mechanism makes that conversation between the world of LLMs and the world of GeneXus possible, what GeneXus for Agents is, what MCP is, what GeneXus MCP Server is, and more. Don’t miss it, and let us know what you think.

➡️ https://hubs.la/Q04hT04h0

The problem with prompt-based development that nobody mentions in the demos:
Language models generate code probabilistically. That means for the same prompt, they can produce different results in different runs.

In an enterprise system, this leads to:

✗ You can't validate what isn't deterministic
✗ System consistency falls entirely on the developer - manually
✗ The speed AI brings gets offset by the extra controls you need to add

AI speed means nothing if maintenance costs skyrocket or system reliability gets compromised.

This article breaks down the structural limitations of the prompt as a source of truth - and walks through an alternative that doesn't require extensive manual validation or pile up technical debt with every iteration.

Must-read if you're building (or evaluating) AI-powered systems 👇
https://hubs.la/Q04gNQX60

Every AI agent that touches your codebase is a risk.

Unless the system knows how to say no.

Most teams adopting AI for development are running into the same wall: the agent generates code fast, but nobody can guarantee it's consistent with the rest of the system. You review it manually. You fix the gaps. You wonder if it's actually saving you time.

GeneXus for Agents takes a fundamentally different approach.

Instead of letting AI agents loose on source code, it gives them access to the Knowledge Base - the single source of truth where business rules, data models, and relationships live, above any specific technology.

The agent reads the KB. Reasons over it. Proposes changes.
Then the GeneXus engine validates every single one before a line of code is generated.

If it's inconsistent? Rejected.
If it breaks a relationship? Rejected.
If it doesn't follow the system's conventions? Rejected.

The developer stays in control. The architecture stays intact. And the AI actually accelerates work instead of creating new problems to fix.

This isn't AI-assisted development with extra steps. It's a new model - where speed and control aren't a trade-off.

👉 Read the full breakdown of how GeneXus for Agents works - and why the KB changes everything for AI-driven development:
https://hubs.la/Q04gk3rb0