GeneXus

A prompt is not a source of truth. It's text that AI interprets in the moment - with no context of the full system, no access to past decisions, no guarantee of consistency.

For Mission-Critical Systems, that's a real problem.
Read the full article 🔗 https://hubs.la/Q04k55XH0

Generating code with AI in seconds? Impressive. But what happens next? Speed without structure creates technical debt, security gaps, and systems that age poorly - fast. That's exactly what GeneXus solves in the era of agentic development.

While LLMs suggest, GeneXus validates. While AI accelerates, GeneXus structures. While others improvise bridges, GeneXus has spent 30 years being the reliable bridge between what you have and what's coming.

With GeneXus for Agents, agentic development is no longer a promise - it's a concrete reality.

Want to know why GeneXus is more relevant than ever in the AI era? 🔗 https://hubs.la/Q04k4xNZ0

¿Sabes qué es y cómo funciona GeneXus MCP Server? El GeneXus MCP Server es la capa que hace posible que un agente externo, usando cualquier LLM, pueda participar en el ciclo de desarrollo de un sistema GeneXus con la misma confiabilidad y control que si lo estuviera haciendo un desarrollador desde el IDE.

Te explicamos todo en este link: https://hubs.la/Q04jfkJV0

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

GeneXus News | GeneXus Partners and Distributors from around the world gathered in Montevideo (Uruguay) to define the roadmap toward the agentic era ➡️ https://hubs.la/Q04hPNpW0

The event featured a strong lineup of GeneXus and Globant leaders who addressed agentic transformation from multiple dimensions: product, technology, commercialization, and adoption.

The GeneXus KB is the structured representation of years of learning about how your business works: which rules apply, how entities relate to each other, which processes are critical, and which exceptions exist.

It is the kind of knowledge that takes years to build and, if it falls into the wrong hands, can be reproduced or used by third parties.

If you are evaluating how to connect your KB with Generative AI, or if someone on your team is already doing it, you should know exactly where your data is going and who has access to it once it leaves your infrastructure.

In this article, we explain the risks and everything you need to know about who you are giving your GeneXus Knowledge Base (KB) to.
➡️ https://hubs.la/Q04hNNb-0

New Blog Post: The Apollo 13 Lesson in the Agentic Era
➡️ https://hubs.la/Q04hy2SG0

AI doesn't fail because it's bad at writing code. It fails because it doesn't understand your system.

When an AI agent works across thousands of scattered files, outdated documentation, and tribal knowledge... the result is inconsistent code, silent bugs, and technical debt disguised as productivity.

GeneXus solved this decades ago, without knowing it was solving it for the AI era.

The Knowledge Base (KB) is a single source of truth that lives above the code. Agents read it, reason over it, propose changes - and the GeneXus engine validates before generating. Never the other way around.

The result? Agents that understand your system with the same depth as a senior developer. No hallucinations. No inconsistencies bleeding into the code.

Read the full article where we dive deep into what a Knowledge Base (KB) is and why it's a game-changer in the AI era 👇 🔗
https://hubs.la/Q04h6dMf0

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