We’ve Seen This Before: What Agentic AI Can Learn from the Early Web
How history’s biggest bottleneck shift from static web pages to CMSs reveals what’s next for agentic AI.
In the early 2000s, websites were hand-built.
Every change such as a headline, a hero image, a form update, required a developer.
Marketing wanted to update a campaign page? Open a ticket.
Product wanted to test new copy? Add it to the next sprint.
Engineering was the bottleneck for creativity.
And that meant innovation moved slowly.
Then, something changed.
2000–2005: The Rise of the CMS
Content Management Systems (CMSs) like early WordPress, Joomla, and Drupal broke the bottleneck.
For the first time, non-technical teams could publish copy changes without waiting in a developer queue.
WordPress, in particular, democratized the web.
It turned website management into something (almost) anyone could do.
What was once buried in PHP files became a clean interface and an “Update” button.
It did require a “Wordpress Developer” or at least someone mildly technical, but it still gave visibility, control, and accountability to people outside engineering.
2005–2015: Design Joins the Revolution
As CMSs matured, a new wave of tools emerged.
Squarespace simplified web publishing for small businesses.
Wix and Weebly made drag-and-drop design accessible to anyone.
By 2015, Webflow pushed the boundary even further by blending design, content, and code into one fluid system.
This freed engineering so they could focus on the hard problems and finally gave the marketing team “design” freedom.
2015–2023: Scale & Optimization Era
As web tooling matured, so did expectations.
Analytics platforms like Mixpanel gave teams data on what was working.
A/B testing tools like Optimizely made iteration measurable.
Landing page products like Unbounce & Leadpages made scale possible.
Suddenly, website updates weren’t just flat experiences. Non-technical teams were making rapid changes based on performance data, and publishing hundreds of landing page experiments.
This gave autonomy to the domain experts to own the web experience and ensure it was educating and converting effectively.
The Pattern Repeats
In the early web, content was locked inside code.
Today, insight is locked inside telemetry.
If you want to know what your agent is doing for users, you need to sift through inference logs.
If you want to measure ROI, you need data scientists to decode telemetry.
If you want to change behavior, it’s back to the codebase.
Meanwhile, business teams are asking simple, high-impact questions:
Is our agent improving customer experience?
What’s the cost per successful task?
Is performance trending up or down?
Back then, the solution was the CMS.
Now, it’s time for the Agent Management Layer: a shared system that turns messy inference data into metrics everyone can see and act on.
The companies that build or adopt that layer first will move faster, make smarter decisions, and deliver a better agent experience.
The Next Chapter
Every major technology shift follows the same pattern:
Creation: Engineers build it.
Democratization: Tools make it accessible.
Optimization: Metrics make it measurable.
We’re entering the second stage for agents where visibility and alignment will matter more than raw capability.
Brixo’s Bet
At Brixo, we’re betting on the same curve that reshaped the web.
We translate agent telemetry (prompts, retries, outcomes) into clear business metrics: cost, ROI, sentiment, and performance.
Think of it as the CMS moment for agentic AI.
Just like WordPress made content visible, Brixo makes agent behavior understandable.
Just like Webflow unified design and deployment, Brixo unifies telemetry and business impact.
When visibility catches up to capability, everything changes.