Are AI Agents Prioritising Form Over Function?

The Cost of Prioritising Form Over Function

We’re living through a surge in AI-powered tools and digital agents. They look sleek, sound intelligent, and promise to transform how we work. But here’s the problem: many of these solutions are designed to impress rather than deliver.

Behind the glossy interfaces, critical functionality is often overlooked leading to inefficiency, inaccuracy, and unnecessary risk. If you’ve felt that AI isn’t living up to its promise, you’re not alone. Let’s unpack why.

Takeaways

• The three dominant AI architecture styles and their trade-offs

• A cautionary scenario of form-first agent design

• Why JEDAI takes a different approach, built around functionality and trust

• What a future-ready AI architecture really looks like
  

The Problem with AI That Looks Good (But Works Poorly)

A foundational idea in design by architect Louis H. Sullivan is that form follows function. In other words, design should serve a purpose, not just please the eye. But in today’s AI landscape, we’re seeing that principle flipped on its head.

Many agent-based AI models are being built for maximum market appeal, showcasing impressive demos and slick UX. Yet when tested in real-world settings, these models can fall short overpromising and underdelivering where it matters most.

Three AI Architecture Approaches: What’s Under the Hood?

Let’s simplify the landscape. Most enterprise AI solutions today fall into one of these three categories:

1. Agent-Based Architectures: These mimic traditional apps, relying on external large language models (like OpenAI or Gemini) and cloud-based logic. They’re quick to build, but highly dependent on external systems leading to recurring costs, data exposure, and accuracy issues.

   
   

2. Context Process Architectures: Here, data is structured and contextual, reducing hallucinations and improving trust. It’s a slower build, but results in higher-quality output and better long-term control.

   
   

3. Federated AI and AI-as-a-Service Models: Often repackaged versions of agent models, these promise decentralisation or ease of access. In reality, they often carry similar concerns around data privacy, scalability, and clarity.

A Counter-Scenario: When Form Takes Over Imagine a company that fully commits to agent-based models, driven by their visual appeal and ease of integration. Initially, everything looks promising. But soon:

• Cloud usage bills rise steeply with every AI call

• Sensitive data travels outside organisational boundaries

• Teams begin to distrust outputs due to hallucinated responses

• Compliance teams struggle with audit gaps

• Security risks escalate without clear traceability

  
  

It’s a scenario we’ve seen play outand one we actively designed against.

Why We Built JEDAI Differently

Back in 2021, our team recognised these risks early. Rather than building yet another reactive agent, we designed JEDAI: a structured, privacy-first AI architecture tailored to cybersecurity and operational resilience.

A modern and elegant JEDAI logo features an off-white infinity loop within a circle, set against a vibrant purple and blue aurora borealis background.

Our approach was validated through a proof of concept in 2022, and supported through a #DASA grant that helped us bring a working MVP to life.

JEDAI is built around four key pillars:

• Data Privacy by Design: Sensitive data stays within your boundaries reducing external dependencies and risk.

• Traceable Forensics: Audit trails are baked in, not bolted on. This supports compliance, trust, and accountability.

• Resource Efficiency: With our ‘Just Enough’ principle, JEDAI avoids data overload and focuses only on what matters.

• Fewer Hallucinations & Better Accuracy: Contextual intelligence mean more reliable answers and less noise.

JEDAI's form is in the context process architecture model and the function is to think and act like a team of cybersecurity experts!

The Future Is Function-First: In enterprise settings, it’s not enough for AI to be impressive. It has to work flawlessly, securely, and in a way that teams can trust. That’s the core philosophy behind JEDAI.

We’re not chasing hype. We’re building for clarity, control, and confidence.

In the upcoming articles, I’ll explore:

• Why current models can't explain themselves and the first principles

• Why rushing into agent-based models can create chaotic, hard-to-maintain ecosystems

• How JEDAI’s architecture reflects a deeper shift in how AI should be designed

• Practical lessons from real-world deployments

Stay tuned and if you’re rethinking how AI fits into your organisation, we’re always up for a conversation.