Last month, I spent three hours debugging why our customer event pipeline was dropping records. Three hours. It turned out an upstream API had changed its response format by one field. One field. A junior engineer could have caught it in five minutes if they’d been looking. The problem is, nobody was looking because we were too busy dealing with five other fire hoses.
That’s when I started seriously investigating what people mean when they talk about AI agents managing data infrastructure.
The Real Problem We Face
Here’s what’s happening in most data organizations right now. You’ve got Airflow running hundreds of DAGs. Dbt is handling transformations. Spark is crunching the big stuff. Maybe you’re using Snowflake or BigQuery for your data warehouse. Everything’s connected in ways that seemed logical when you designed them, but now feels fragile.
Then something breaks. Always something. An API endpoint returns slightly different data. A column disappears. A table gets partitioned differently than expected. Your schema validation catches it and breaks the whole pipeline. Now someone’s got to wake up, figure out what changed, why it changed, and how to fix it. Then deploy that fix. Then test it. Then hope it doesn’t break something else downstream.
We’re running data platforms with the same mental model we used ten years ago, except the complexity has exploded. We have APIs we didn’t write that change without notice. We have cloud infrastructure that costs money proportional to how much we scan. We have compliance requirements that seem to change monthly. And we have roughly the same number of people managing it all as we did five years ago, maybe fewer.
The expensive solution is hiring more people. The realistic solution is getting the systems themselves smarter about handling common problems.
What Agentic AI Actually Means
There’s a lot of fuzzy language around AI agents. Some people use “agent” to mean any automated system. That’s not what I’m talking about.
An agentic AI system in data engineering is something that watches what’s happening in your pipelines and makes decisions about how to respond. It’s not running a predetermined script. It’s reasoning about situations. It’s identifying patterns. It’s suggesting solutions or implementing them directly.
The key difference between this and traditional automation is that automation says “if this condition, then do that.” An agent says “here’s what I’m observing, here’s what it probably means, here’s what might fix it.”
That distinction matters because data infrastructure is messy. Your API schemas aren’t always well-documented. Your data quality issues don’t fit into neat categories. Your cost problems aren’t simple. You need something that can think about problems in real time, not just execute predetermined response scripts.
Why Companies Are Actually Trying This
Two years ago, this was purely theoretical. Today, I’m seeing real implementations at mid-size companies doing interesting work.
A financial services company I know is running agents that watch their transaction processing pipelines. When something looks wrong with the data volume or the distribution of transaction types, the system flags it immediately. It doesn’t just alert an engineer. It pulls recent schema changes, recent code deployments, recent API documentation updates, and prepares a summary of what might have changed. An engineer reviews it and usually approves the fix within minutes instead of spending an hour investigating.
An e-commerce company is using agents to optimize their AWS spend for data pipelines. The system watches which queries scan the most data, recommends better partitioning strategies, identifies unneeded columns being scanned, and suggests compute tier changes. One engineer told me they went from a quarterly budget review meeting to a system that continuously optimizes spending. Their costs went down without any manual intervention.
A healthcare company is using agents to maintain compliance posture. They have regulations about how long different categories of data can be retained. They have requirements about access logging. They have encryption requirements. An agent continuously monitors whether they’re meeting all these requirements and flags violations before audits find them.
These aren’t science projects anymore. They’re in production. They’re solving actual problems people face.
How We’re Actually Using These Systems
From what I’ve seen and what I’ve tried, here are the things that genuinely work well.
Pipeline failure detection and alerting. This is the easiest place to start. An agent watches pipeline runs. When something fails, instead of just alerting “job failed,” it pulls logs, identifies what went wrong, pulls recent changes that might be relevant, and gives you context. I’ve used this and it genuinely saves time.
Schema change detection. When an upstream data source changes its structure, agents can catch it immediately. They can infer what the new schema looks like. They can identify which downstream systems depend on the old schema. They can suggest what needs to change or sometimes just change it automatically if the changes are safe.
Cost tracking and optimization. This is where I’ve seen the most impact. Agents monitor what’s running, what it’s costing, whether it could run cheaper. They spot inefficient queries. They identify stale datasets. They suggest reserved instances vs. on-demand. One agent I worked with literally saved us more than its cost in the first month.
Data quality monitoring. Beyond just running tests, agents can infer what good data looks like for specific domains and watch for anomalies. They don’t just tell you something’s wrong. They suggest what might be causing it.
Basic self-healing. Some things that break can be fixed automatically and safely. A task that failed due to a transient network error can be retried. A step in a pipeline that needs a dependency from a previous step can wait and retry. The system learns which failures are safe to retry automatically and which ones need human attention.
What Doesn’t Work Well Yet
I want to be honest about where this breaks down, because I’ve seen people get excited about agentic AI and then disappointed when reality doesn’t match the pitch.
Complex code generation. The system can look at a pattern and write simple transformations or queries. It cannot reliably generate complex business logic. I’ve seen proposed solutions where agents would write Spark jobs or Python transformations automatically. Most of the time the output needs heavy review. You save some time but not as much as you’d think. The human code review still takes longer than just having someone write it.
Handling completely novel situations. If your data infrastructure encounters a problem that’s outside what the system has learned from, it struggles. Agents are pattern matchers. They’re good at recognizing familiar situations and variations on familiar situations. Genuinely new problems still need human brains.
Making architectural decisions. Should you switch from batch to streaming? Should you move this workload to a different warehouse? Should you restructure your dimensional model? These require business judgment and deep domain knowledge. Agents are not there yet.
Security and permissions. If you give agents permission to modify pipelines and access data, you’re creating attack surface and compliance risk. The agents themselves need governance systems around them. This adds complexity.
Real Numbers From Places Doing This
I want to ground this in specifics. Here’s what I’ve actually heard from people running these systems.
One company said pipeline incident resolution time went from an average of 3.5 hours to 45 minutes. They weren’t reducing the number of incidents. They were just diagnosing and fixing them faster.
Another company quantified that they spent 22% less on AWS for their data platform over a year. They didn’t change their data volumes or workloads. The system just continuously optimized.
A third company said their data engineers went from spending 40% of their time on maintenance and operational firefighting to about 25%. That freed up capacity for building new pipelines and improving infrastructure.
Those are the kinds of results I’m hearing. Not game-changing, not replacing entire teams, but meaningful improvements that actually reduce costs and free up people to do higher-value work.
What You Actually Need to Make This Work
If you’re thinking about implementing agentic AI in your data infrastructure, don’t just turn it on. There are prerequisites.
You need good observability. You need comprehensive logging. You need clear data lineage. You need schema documentation that’s actually maintained. If you’re flying blind with poor monitoring and unclear data relationships, an agent won’t help you. It’ll just make different mistakes faster.
You need governance rules. What can the system do autonomously? What requires human approval? What should it never touch? Write these down as explicit policies. Make them hard constraints in the system, not guidelines that are sometimes ignored.
You need a human review process for important decisions. Especially early on. Let the system make suggestions and diagnose problems. Have engineers review before implementation. As the system proves itself reliable, you can expand what it does autonomously.
You need to measure what matters. Track incident resolution times. Track costs. Track data quality metrics. Track what percentage of system recommendations humans accept or reject. Use this data to improve the system.
You need to integrate it with your existing tools. The system has to work with your current orchestration platform, your warehouse, your monitoring, your alerting. It can’t be a separate system that requires manual handoffs to your existing infrastructure.
Where This Is Actually Headed
I think over the next few years a few things happen.
The major orchestration platforms add agentic capabilities. Airflow and dbt will integrate these kinds of features directly. You won’t need to bolt on separate systems.
Data engineers spend less time firefighting and more time building. That’s not revolutionary but it’s real improvement in how we work.
The companies that implement this early and well get a cost advantage and reliability advantage. That compounds. The gap between well-optimized and poorly-optimized data infrastructure grows.
We’ll see more sophisticated multi-agent systems that reason across pipelines instead of optimizing individual pipelines independently. That’s when you get bigger efficiency gains.
The barrier to entry for data engineering companies drops slightly. If operational maintenance is semi-automated, you can run data infrastructure with smaller teams. That has competitive implications.
The Honest Take
I’m not saying every company needs this tomorrow. What I am saying is that if you’re managing complex data infrastructure, you should understand what agentic AI can do in this space because it’s becoming a standard tool. And companies like Azilen Technologies are already helping teams adopt it responsibly.
It’s not magical. It requires integration effort. It requires governance thinking. It requires good data practices. But it solves real problems that data engineers actually face – slow incident response, inefficient resource usage, constant firefighting, and rising costs.
If any of those sound familiar, it’s worth exploring.
The companies that are doing this today are not betting on hype. They’re solving specific operational problems. That’s the indicator that this is moving from “interesting research” to “practical tool.”
Start small. Pick one specific problem in your data infrastructure. An Agentic AI in Data Engineering approach could help. Run a pilot. Measure the results. Expand if it works.
That’s the realistic path forward. Not a complete overhaul of your data platform. Just incremental improvement where you need it most.
