Most AI remains at the execution level, Once deployed to a real production cluster, facing complex resource dependencies and access restrictions, they struggle to provide truly decisive advice like human experts.
Recently, a Barron's report on Chaterm caught my attention, highlighting an interesting point: More than simple code completion, AI's greatest value in operations lies in assetizing the experience of senior engineers.
Barron's Perspective on Chaten from an SRE's Point of View
Having spent considerable time in cloud-native environments, SREs often have an instinctive wariness of new tools. We've seen too many hype stories claiming to "change the industry," but in reality, most AI remains at the execution level; they can only write scripts in demos. Once deployed to a real production cluster, facing complex resource dependencies and access restrictions, they struggle to provide truly decisive advice like human experts.
Recently, Barron's report on Chaten caught my attention, highlighting an interesting point: Compared to simple code completion, the greatest value of AI in operations lies in assetizing the experience of senior engineers.
This hits the nail on the head. The threshold for advanced operations is never about typing a few lines of commands, but about using experience to pinpoint the root cause when faced with ambiguous error messages. Writing scripts is merely the result; the logical deduction during troubleshooting is the most difficult part to replicate. If AI can reuse accumulated experience across the entire team, it is indeed far more meaningful than simply writing a few lines of code.
Following this line of thought, I'd like to discuss how operations agents can address several key pain points in real-world scenarios, from individual experience to team capabilities.
I. Operational Pain Points Often Begin with Vague Descriptions
In production environments, faults rarely begin with a clearly defined alert. More often, we face extremely vague, subjective descriptions: "Service response has slowed down," "The cluster feels off," "It seems different from yesterday."
This vagueness is the biggest headache for SRE engineers. Traditional monitoring tools have rigid logic; they can only tell you which metrics are abnormal, but they can't unravel the causal relationship behind the "feeling off." For example, when you see a screen full of "Connection refused" messages on the terminal, the traditional method is to rely on experience, guesswork, and elimination: checking network plugins, service topology, resource limits in Pods… This process can take half an hour, and just to determine a direction for troubleshooting.
The example of Chris using Challenger to quickly resolve Hadoop node faults, as mentioned in the Barron's report, essentially addresses the problem of moving from vague input to logical convergence. In complex architectures with deep component dependencies, a single dead node at a lower level can trigger hundreds or even thousands of errors at higher levels. The real breakthrough for AI tools lies not in taking over decision-making, but in using their awareness of environmental context to help us establish the first reasonable logical assumption in this ambiguous initial stage.
The most direct value of this capability is that it makes troubleshooting targeted. It runs the most painful "first mile" of troubleshooting for you, allowing you to skip the blind search phase and directly enter the core verification stage.
II. Experience-Based Assistance Superior to Fully Automated Black-Box Solutions
System maintenance (S&M) is undeniably a highly experience-dependent task. This experience includes, but is not limited to: domain-specific knowledge, previous experience with similar situations, previously written notes, and most importantly—knowing where to find relevant information. When encountering a system failure, we typically follow a fixed pattern to resolve the issue: for simple problems, we can often deduce the cause directly from the symptoms; for deeper failures, we need to check logs and monitoring, trying to find clues in the logs, and then check if there are any configuration/environment issues. Then, based on the information we've gathered, combined with our experience, we make reasonable guesses, and finally, we execute corresponding verification actions based on our guesses. For complex problems, this process is repeated multiple times until we find the true cause of the problem.
Breaking down this process reveals that many steps can be assisted by AI. For example, when we encounter the following system failure:
MySQL master-slave synchronization failure
When we delegate this task to AI, it knows to first check logs, configuration, network, etc., and then make corresponding guesses based on the information it collects. When the AI issues an execution command, it is then handed over to a human to determine whether to execute the corresponding operation. Currently, this is the most reasonable approach to AI-assisted operations and maintenance. Otherwise, the consequences might not only be "master-slave synchronization failure," but worse, the database instance could crash directly. What if the AI cannot find the cause of the problem? My answer is: (Try a different model) Try a few more times. Humans often cannot solve problems on the first try, let alone probabilistic models.
For a database beginner, this kind of AI assistance is even more valuable because beginners lack experience. Faced with the same failure as above, a novice may be completely at a loss. They may not even know where the logs/configuration are, let alone understand the log content and configuration items. In the past, they could only search for answers using tools like Google/ChatGPT. However, such investigations are extremely inefficient. First, when searching for answers externally, there's no contextual information. Google/ChatGPT doesn't know your system version, software version, or software configuration, so they can only provide common/general answers, which are unlikely to be applicable to the current situation. Second, humans need to process the retrieved answers to see if they match their current situation. This processing varies from person to person, and can be fast or slow. When we have a native operations and maintenance AI at hand, the problem becomes much simpler.
III. As an SRE, what I truly look forward to is how experience can be replicated.
Ultimately, no matter how tools change, the most core asset of an operations team remains the tacit experience that is difficult to standardize.
This knowledge is rarely fully documented in a wiki; it's more often ingrained in the muscle memory of veteran employees through trial and error. For example, why does scaling up an older cluster cause concurrency bottlenecks? Is CPU jitter in a certain service during the early morning normal? If these crucial contexts only exist in personal notes or the minds of a veteran employee, the team's troubleshooting efficiency will inevitably fluctuate drastically with personnel changes. This is why I focus on the Challenge team's knowledge base: integrating experience directly into the workflow is far more practical than creating a more aesthetically pleasing e-library.
Often, we consult the wiki not because we don't know how to write commands, but because the documentation is disconnected from the real-time environment. Documentation is static; it doesn't know your current kernel version, network topology, or specific error. Searching for "master-slave synchronization failure" might bring up a dozen different historical records from different years, requiring you to spend time checking them one by one. The logic of Challenger is to make the knowledge base aware of the terminal environment; it reads the current cluster status during the response. Based on context-filtered practical solutions, it saves a significant amount of time spent manually verifying the environment.
For newcomers, this reuse of experience is more like real-time risk interception. Senior engineers are reliable because they are highly sensitive to system limitations. For example, an older database is prone to table locking when executing CHECK TABLE. Newcomers can hardly avoid these details simply by reading security guidelines once. If this kind of experience is entered into the knowledge base, when a newcomer attempts to enter a high-risk command on the terminal, the system will proactively pop up a warning based on semantic matching: "Based on historical incident reviews, this operation is recommended to be performed during off-peak hours." Direct, hands-on experience transfer is more effective than any offline training.
More importantly, it frees up the main troubleshooting team from repetitive data collection work. At this stage, we don't need AI to teach how to write commands; instead, we need it to handle the tedious work of collecting information. Taking analyzing Java application memory overflows as an example, the standard procedure usually involves dumping the heap, examining GC logs, and comparing JVM parameters. If this logic is stored in a library, the next time a failure occurs, a single command is all it takes for the agent to automatically complete data collection and feature comparison. At this point, we can skip reviewing reports and directly make the final risk decision.
This model transforms experience into an inheritable team capability. A tool truly possesses long-term engineering value when it enables newcomers to avoid uncontrolled risks and allows key personnel to focus on core decisions.
IV. The Boundaries of SRE and AI Collaboration
Having discussed so much, it's not to say that AI will eventually take over operations. On the contrary, given the complexity of cloud-native environments, any black box tool claiming to offer "one-click automatic repair" is often extremely dangerous in a production environment.
The essence of operations is decision-making, and decisions require accountability. I've always believed that the most reasonable role of AI assistance is to help sift through the mess of error messages and collect data that would otherwise require manual searching. The final decision to press the execute button should always remain in human hands.
Finding suitable tools is a real reduction in workload for SREs. With systems now often containing thousands of microservices, relying solely on manual log analysis and experience-based troubleshooting is no longer sufficient to keep pace with business iterations. Instead of rejecting new technologies, it's better to delegate those tedious, repetitive tasks to AI. For example, when investigating network jitter, the Agent can automate full-link packet capture and comparison; or when an application startup anomaly occurs, it can aggregate log features from multiple replicas within seconds. With the right tools, this automation can save a significant amount of time searching for information.
This is also my intuitive impression of Chaterm: a good AI tool doesn't replace human decision-making; its value lies in providing you with a more comprehensive information context during operations. When the tool can understand the intent of the operation and synchronize the environmental state, operations and maintenance no longer rely on luck but become evidence-based reproduction and troubleshooting.
From this perspective, the benefit AI brings to operations and maintenance is actually making experience, a difficult-to-quantify asset, possible to be engineered for the first time.
V. Conclusion
Looking back, the most insightful point in Barron's report was that it didn't fall into the grand narrative of "AI changing the world." It was actually discussing a very real problem: when system complexity has already pushed humans to the brink, where should tools go? This "being pushed to the brink" doesn't mean monitoring is inaccurate or logs are lost; rather, it means the logical connections between massive amounts of fragmented information are now incredibly difficult to reconstruct manually. Previously, focusing on a few core metrics was enough to pinpoint faults; now, we're dealing with a network of tens of thousands of interwoven microservices, where even the slightest fluctuation in any node can be instantly drowned out by a cacophony of irrelevant error messages.
From the perspective of a frontline SRE engineer, what we need is never a black box that can take over everything, but rather the ability to link scattered troubleshooting clues to the current context in real time. Experience is valuable because experienced engineers know which data to retrieve in which scenarios and which anomalies are causally related. The current trend is to try to embed these troubleshooting intuitions, usually reserved for senior engineers, into the system through agents.
This means a fundamental shift in operational logic: we no longer need to become "human indexers," but rather return to logical judgment and verification.
The intervention of AI essentially automates those low-value, repetitive falsification processes. Agent tools like Chatterm don't make decisions for you, but they give you a holistic view when facing unfamiliar failures. Ultimately, we're not pursuing a smarter AI, but rather reshaping the relationship between humans and systems — returning technology to its original place as an extension of humanity, not a replacement.
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