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Enterprise remediation used to be easier. Not easy, but easier.
If something broke, you were often trying to figure out which Linux or Windows host to log into. In a monolith, everything was effectively in one place.
The fix might be ugly: turn off the component, patch it, bring it back online. But the shape of the problem was easier to see.
Modern environments changed the trade-off. They’re more API-driven, more scalable, and easier to automate in some ways. They’re also more unique. One environment is deep in a cloud provider. Another is split across cloud and on-prem. Another is container-based and lives in Kubernetes.
The pressure to automate remediation makes sense. There are more systems to check, more signals to correlate, and more ways for manual response to slow down. But self-healing infrastructure doesn’t mean handing production control to an agent and hoping the guardrails hold.
It starts with knowing which problems are predictable enough to automate safely.
Not every production issue is a good candidate for automated remediation. The best candidates are common, predictable, and controlled through configuration.
In web apps and services, many failures are not novel. The same habits show up again in a new application stack. A Java or .NET service starts to take on more load. Garbage collection cannot move fast enough. Heap size is too small. A web app calls a database through a connection pool, but the pool only allows 10 active connections at a time.
That helps control load to the database, but it also creates a bottleneck when traffic rises.
If the system detects that the connection pool is too small, the fix may be to increase the available connections from 10 to 20 or higher, with an upper limit. If the issue is memory pressure, the fix may be a configuration change. The action has to be bounded.
Not every issue belongs in the same category. A connection pool leak is different. If a thread finishes using a database connection but never returns it to the pool, that is not something to fix by scaling a system or changing a configuration setting. That requires code changes.
This is where teams need a simple Remediation Safety Test.
Code can run the automation. Humans should own the decision.
The question teams start asking is: “Why can’t an agent just monitor the environment, diagnose the issue, and remediate it?”
It makes sense. Teams are told to do more with less, and nobody wants engineers pulled back online at 2 a.m. to work through a production issue if a system can monitor and remediate issues.
Meanwhile, finding and fixing issues is time-consuming. When there’s an outage, someone has to start with whatever signal they trust first. Maybe that’s logs. Maybe it’s network monitoring. If the answer isn’t there, the team keeps widening the search until they understand where the problem is coming from.
Then, they still have to test the fix and prove the system is healthy again.
Using agents could, in theory, give them their time back by automating the whole process. They give the agent access to monitoring and automation platforms. Tell it to keep the environment healthy. Maybe they put guardrails around what it can touch.
That’s the impression these systems can create.
What teams underestimate is that the agent can stay within the access it was granted and still cause significant damage.
Agentic remediation becomes dangerous when the agent decides on the fix rather than following a path someone has already defined.
Hallucination and prompt injection are part of the risk, but they’re the far end of the spectrum. The more practical risk is misinterpretation of intent.
The agent takes the instruction, works inside the access it was given, and makes decisions that may seem logical to the system but don’t match what the human meant.
Even if you give it a step-wise function to follow, it can still decide another step fits the job.
For example, PocketOS was using a Cursor agent on what should have been a staging task. The agent hit a credential mismatch. Instead of stopping, it tried to solve the problem itself.
It found an API token in another file. That token had broader access than it should have. The agent used it to call Railway’s API and deleted the production volume. In about nine seconds, it took out the production database and the volume-level backups stored with it.
Customers lost access to reservations, customer records, and vehicle assignments.
The agent didn’t need to break out of the system. There was no malicious intent. It had sufficient access to make a destructive call, and the workflow allowed it to act without human intervention.
LLMs and agentic systems are useful when they integrate information. They can take event-driven data and give an engineer something to read, question, and push back on.
Sometimes you look at the output and say, “Yeah, that makes sense.” Other times you say, “No, that doesn’t fit the environment. Try again.”
Where things get out of hand is when detection, diagnosis, and execution are lumped together. A machine with partial context can make a diagnostic decision and execute it. Sometimes the context window is too small. Sometimes the information is misleading. Sometimes it’s just wrong.
Dynatrace and Ansible give you a safer split.
Davis, inside Dynatrace, is not an LLM. It’s a machine-learning and statistical-analysis system. Dynatrace agents collect telemetry and line-by-line code introspection, then Davis looks for anomalies against the baseline. If a system normally runs at one throughput rate and starts moving more slowly, Davis looks across the segments and points to where the abnormal behavior appears to originate.
Davis Chat (Dynatrace Assist) is LLM-backed. It takes the information Dynatrace has collected and helps the end user sift through it.
Davis produces the problem card or event. Davis Chat helps explain what happened and what remediation may make sense.
And Dynatrace workflow automation acts on the event. A problem card can trigger a workflow that sends a Teams message, creates a ServiceNow ticket, or calls a webhook into Red Hat Ansible Automation Platform.
Ansible Automation Platform is where the execution path lives. If Dynatrace detects an exhausted thread pool, a memory pressure event, or a database connection pool issue, the workflow can kick off a prebuilt Ansible job for that event type.
The first job can check the host and verify that what Dynatrace sees from above matches what Ansible sees below. This gives you a second opportunity to validate before changing the system.
If the issue checks out, another Ansible job can handle the remediation. For production-impacting changes, the workflow can still stop and ask a human to approve the job before it runs.
Dynatrace and Ansible Automation Platform give enterprise teams a defined, auditable remediation path. Arctiq operationalizes the stack—standing up Ansible Automation Platform and EDA instances, defining remediation use cases, building playbooks, and configuring the integration between Dynatrace and Ansible.
The result is automated remediation with human gates where it matters.