WebDevPro #122 Rise of LLM Powered agents
Real-world insights for sharper web dev decisions
As the year winds down, there was one idea we kept coming back to and wanted to leave you with before the break.
This is a short bonus note to close out the year, touching on a shift many teams are starting to notice, even if they’re not naming it yet.
The quieter shift around AI agents
Much of the conversation around AI agents still focuses on how they help teams work faster. That part is useful, but it may also be the least interesting aspect of what’s changing.
A quieter shift is happening in how products are observed and operated once they are live.
Most products do not fail in obvious ways. Things change gradually. A metric softens, user behaviour shifts slightly, or performance drifts away from what once felt normal. These changes are rarely dramatic, but they are easy to miss when attention is split and teams are busy moving forward.
What agents are beginning to do well is take on that background attention. They sit alongside live systems, watch behaviour continuously, and surface changes that genuinely matter. This is not about adding more dashboards or alerts. It is about maintaining a steady sense of awareness without relying on someone remembering to check at the right time.
From chatbots to agentic systems
Over the past couple of years, large language models (LLM) have reshaped how we think about AI. As their capabilities expanded, many teams started running into the same limitations when trying to use them in real systems:
They lack long-term memory and lose context over time
They wait for prompts instead of operating autonomously
They cannot naturally interact with live systems, APIs, or data sources
Agentic systems emerged to bridge this gap.
Instead of treating AI as something you talk to, agents are designed as systems that can observe, reason, and act over time. At a high level, this usually means combining an LLM with a few additional capabilities:
Memory, so context carries across days or weeks
Planning, to break down goals into smaller steps
Tool use, to interact with APIs, databases, and external services
The result is software that is less reactive and more continuous in how it operates.
Where this shows up in practice
This approach is already appearing in newer tools, especially around monitoring and operations. One example is Fload, which applies agent-based thinking to continuous product monitoring.
Fload connects to app store data, revenue signals, and usage behaviour to maintain an evolving picture of how an app is performing over time. Instead of relying on periodic reviews or manual checks, the system focuses on noticing when patterns drift, not just when something breaks.
In practice, this means:
Watching performance continuously instead of weekly or monthly
Holding context about what “normal” looks like for a product
Highlighting changes that indicate early risk or opportunity
Reducing the need for constant manual monitoring
While Fload is built for mobile apps, the underlying idea is broadly relevant. As systems grow more complex, the cost of missed signals increases, and continuous attention becomes harder to sustain manually.
A pattern worth paying attention to
For developers, none of this should feel unfamiliar. We already care about logs, monitoring, and observability. What agents add is continuity. They help systems remember, notice slow changes, and surface context before small issues compound into larger ones.
This shift, from periodic checking to continuous watching, is likely to influence how we design products, pipelines, and operational workflows in the years ahead.
If you’d like to see one real-world implementation of this idea, you can explore Fload here.
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That’s it from us for the year.
Thank you for reading, replying, and sharing WebDevPro through 2025. We’ll be back in January with fresh issues and new ideas. Until then, take a proper break.
Cheers!
Editor-in-chief,
Kinnari Chohan