Traditional observability tools hook into your infrastructure, grabbing logs, traces and metrics. In theory, if you collect enough data, you will know what is happening in your system.
Except that is not really how it works.
The problem is that these tools only monitor a small subset of what is a large, complex and connected system. They tend to ignore critical signals like application behavior, system configurations or interactions between internal tools.
This creates blind spots that make debugging slow, tedious and ultimately harder than it needs to be. In the short term, it puts significant strain on the engineering team, but in the long term, it has major implications for the user experience and overall customer satisfaction.
Fixing this starts with understanding why it is such a widespread problem in the first place.
The Two Biggest Causes of Hidden Failures
First, some systems function as internal systems of record, and they tend to gate the experience a user receives. There could be certain rows in a database that a customer depends on or that someone manually configures. There may also be internal tools, like a config management system or a feature flag, which determine what a user sees.
Then, there are offline processing jobs — some sync data daily, while others run weekly. If something breaks in one of those jobs, you might not notice right away because things still look fine on the surface. There could be a cache that the offline job updates, so as long as that cache is valid, everything appears normal.
But once the cache expires and the offline job has not refreshed it, that is when the problem finally surfaces. This can sometimes happen weeks later. By that point, it is already way too late.
Second, there are external changes that can impact the system’s health. To me, ‘external’ could mean something outside of my team. For example, I might be on a feature team, but I depend on an infrastructure team to maintain my systems. Let us say they pushed a Kubernetes update. That update directly impacts my system’s health, whether I was aware of the change or not.
External can also mean dependencies outside my company. For example, if I rely on Twilio for notifications and Twilio goes down, I’m down too.
In both these instances, it becomes challenging to narrow down where and when an issue has arisen, and oftentimes, the customer has already been experiencing the negative impact for weeks.
Scaling the 10x Engineer
If you think about every high-functioning team, there is almost always that one 10x engineer who can pinpoint issues and root causes the moment they occur. They see something go wrong in production and immediately point out, “Ah, this is what happened.”
But if you peel back the layers, it is not magic — it is context. They have been involved in design reviews as features were being developed. They have seen the pull requests (PRs) as they were being shipped. They stayed tuned to changes happening around them, be it from external teams or external systems.
They also spent time looking at dashboards — not just to catch bad patterns, but to understand what a good day looks like. When something breaks, they do not have to start from nothing. They already know, ‘Oh, this person made a change here — this could be the cause.’
It is not always about raw skills, but about having the full picture.
And that is where agentic AI comes in.
How Agentic AI Can Replicate Deep Context
Agentic AI has mostly been discussed in the context of a chat UI, but its power extends far beyond that use case.
While traditional ML models are deterministic (if this, then that), agentic AI uses reasoning to understand its environment and create a plan based on the best possible path.
In this context, agentic AI would be able to tap into your logs, traces and metrics, but it would also:
- Tap into internal systems of record (config management, feature flags, offline processing jobs).
- Track infrastructure changes (Kubernetes updates, dependency shifts).
- Correlate external factors (third-party service outages, API failures).
It would then use reasoning to identify the most likely culprit for the issue and alert the engineering team, directing them to the exact location of the problem. So, when something breaks, it has every bit of context that a 10x engineer would. And if you can replicate what a 10x engineer does — their ability to instantly connect the dots, recognize patterns and diagnose issues —you can make that expertise available to every team, all the time. It is like having that 10x engineer always present, ready to surface the right insights exactly when they are needed.
The Takeaway
Observability has always been framed as a data collection problem — just grab more logs, traces and metrics, and you will have the answers. But that is not how real-world debugging works. The real issue is not a lack of data; it is a lack of context.
Agentic AI solves this by connecting the dots the way an experienced engineer would — pulling in the right signals, reasoning through the noise and surfacing answers before teams waste hours digging.
The future of observability isn’t just about collecting more data; it is about truly understanding what is happening at the most granular level and fixing issues before they cost you your biggest customers.
