Just like calculators, computers, or internet, AI is a tool. It can be used for many things, but the creator must use their brain and give it a brain.

Right now everyone is using it for everything. More than any tool in the past. And that includes dumb people doing useless things, lazy people doing too many things, smart people distracted by the shiny parts and ignoring the technical details, plus a lot of noise. And, hopefully, some smart people quietly doing good things.

Agents are a big part of the future but not the only tool. We still need humans, and automated workflows and plain old logic. When the dust settles and the trash is thrown out, we will see what we have for the future.

AI is undoubtedly essential for the future of software development, but without proper understanding and strategy, it can easily turn into a cost rather than a benefit. It’s critical to weigh the expected value against the actual cost—because investing in technology without clear goals often leads to added complexity instead of simplification.

Exactly, first and foremost, in today's AI landscape, we need to master the tool and make the most of it for the right scenario. It's no use having a Ferrari or Lamborghini if the operator is a developer of a mass-market car with only basic commands. The first mistake could ruin everything!

Great points—thanks for sharing this. I definitely agree with the idea that AI is adding complexity before it reduces it. While agentic approaches are accelerating development, the real challenge I’m seeing is exactly what you called out: orchestration, governance, testing, and long-term maintenance once these agents are running in production.

The emphasis on enterprise developers becoming more valuable also resonates. AI can generate code, but designing resilient, secure, and observable systems—and deciding where and how agents should act—still requires strong architectural thinking.

Looking forward to hearing more from Woodson and the team on how OutSystems is helping teams manage this complexity and turn it into a real productivity advantage. The webinar should be a great discussion.

Woodson's former company, SalesForce, also saw the shadow side of AI:

Recently, senior executives at Salesforce have admitted, both internally and publicly, that they massively overestimated AI’s capabilities. They have found that AI simply can’t cope with the complex nature of customer service and totally fails at nuanced issues, escalations, and long-tail customer problems. They even say that it has caused a marked decline in service quality and far more complaints.

But the problems go far deeper than that.

Both employees and executives have said that the company is wasting countless resources on firefighting to stabilise operations since the mass AI layoff. Employees have to spend so much time stepping in to correct the wildly wrong AI-generated responses that AI is wasting more time than it saves. In other words, this AI reduces productivity, not increases it.

For more light reading, go here.

Si usamos la IA como un atajo sin entender la arquitectura subyacente, terminaremos con sistemas complejos que nadie sabe mantener. Como bien mencionas, un desarrollador experimentado es el filtro indispensable: la IA genera el código, pero el desarrollador debe garantizar la sostenibilidad y la lógica de negocio.

Hoy empezamos el 2026! feliz año. 

 

Very much agree with this perspective, especially the point that AI increases complexity before it reduces it.

What we are already seeing in real projects is that AI accelerates build activities, but it shifts the bottleneck downstream, particularly into testing, governance, security reviews, and operational readiness. Without strong orchestration, clear guardrails, and lifecycle visibility, the gains in speed can quickly be offset by increased risk and rework.

The emphasis on enterprise developers becoming more valuable also resonates strongly. The skill is no longer just about writing code, but about designing systems where AI agents are observable, auditable, and safely integrated into existing enterprise processes.

From a delivery standpoint, platforms that can embed AI within a governed, end-to-end development and operations model will be key to turning agentic applications into sustainable enterprise assets rather than isolated experiments.

Looking forward to the webinar and the discussion on how teams can practically balance speed, control, and accountability as agentic architectures mature.

Agree with the POV, now a days we are using AI , but the use case of particular AI should be well analyzed in order to get benefit from it.

Hi All,
I fully agree with this perspective, especially for those of us building new agentic applications on OutSystems. We’re already seeing that while AI accelerates development, it initially adds complexity downstream—testing, governance, security, and lifecycle management all become more critical as build speed increases. The emphasis on orchestration and oversight really resonates.

 Running agents in production isn’t just about prompts or models; it’s about having the right guardrails, observability, and coordination across systems so agents behave reliably over time—something enterprise platforms must support by design. Most importantly, this reinforces why enterprise developers become more valuable, not less. AI can generate code, but it can’t replace the architectural judgment needed to design resilient, scalable, and compliant systems. 

Developers who understand how to architect and govern AI agents can dramatically increase their productivity while focusing on higher‑value work—and those skills will only become more rare and in demand.

Thanks,

Saicharan