[aluzzardi]

Mendral co-founder here and author of the post.

This is an interesting approach. I definitely agree with the problem statement: if the LLM has to filter by error/fatal because of context window constraints, it will miss crucial information.

We took a different approach: we have a main agent (opus 4.6) dispatching "log research" jobs to sub agents (haiku 4.5 which is fast/cheap). The sub agent reads a whole bunch of logs and returns only the relevant parts to the parent agent.

This is exactly how coding agents (e.g. Claude Code) do it as well. Except instead of having sub agents use grep/read/tail, they use plain SQL.

[buryat]

yeah, I saw Claude Code doing lots of grepping/find and was curious if that approach might miss something in the log lines or if loading small portion of interesting log lines into the context could help. I find frequently that just looking at ERROR/WARN lines is not enough since some might not actually be errors and some other skipped log lines might have something to look into.

And I just wanted to try MCP tooling tbh hehe Took me 2 days to create this to be honest

[aluzzardi]

From our experience running this, we're seeing patterns like these:

- Opus agent wakes up when we detect an incident (e.g. CI broke on main)

- It looks at the big picture (e.g. which job broke) and makes a plan to investigate

- It dispatches narrowly focused tasks to Haiku sub agents (e.g. "extract the failing log patterns from commit XXX on job YYY ...")

- Sub agents use the equivalent of "tail", "grep", etc (using SQL) on a very narrow sub-set of logs (as directed by Opus) and return only relevant data (so they can interpret INFO logs as actually being the problem)

- Parent Opus agent correlates between sub agents. Can decide to spawn more sub agents to continue the investigation

It's no different than what I would do as a human, really. If there are terabytes of logs, I'm not going to read all of them: I'll make a plan, open a bunch of tabs and surface interesting bits.

[prescriptivist]

I have an agent system analyzing time series data periodically. What I've landed on is the tools themselves pre-process time series data, giving it more semantic meaning. AKA converting timestamps to human dates, additionally preprocessing it with statistical analysis, such as calculating current windows min/mean/max value for the series as well as a the same for a trailing window and surfacing those in the data. Also adding a volatility score, and doing things like collapsing runs of similar series that aren't particularly interesting from a volatility perspective and just trying to highlight anomalous series in the window in various ways.

This isn't anything new. It's not particularly technical or novel in any way, but it seems to work pretty well for identifying anomalies and comparing series over time horizons. It's even less token efficient on small windows than piping in a bunch of json, but it seems to be more effective from an analysis point of view.

The strange thing about it is that it involves fairly deterministic analysis before we even send the data to the LLM, so one might ask, what's the point if you're already doing analysis? The answer is that LLMs can actually find interesting patterns across a lot of well presented data, and they can pick up on patterns in a way that feels like they are cross-referencing many different time series and correlate signals in interesting ways. That's where the general purpose LLMs are helpful in my experience.

Breaking out analysis into sub-agents is a logical next step, we just haven't gotten there yet.

And yeah the goal is to approximate those of us engineers who are good at RCAs in the moment, who have instincts about the system and can juggle a bunch of tabs and cross reference the signals in them.

[selecsosi]

This was my approach when using agents to analyze HVAC IoT data doing anomaly detection / investigations and it similarly worked very well. Mix that with some context like install location, geographic features with some context / info on seasonality (like ASHRAE values for the regions), and some classification like (residential / commercial), the bot was quite able to deliver actual insights into problems vs creating a bunch of excess noise.

We also mixed in some GSA (https://arxiv.org/abs/2503.04104) steps during the analysis in the sub agents to further reduce hallucinations

[prescriptivist]

Glad to hear this. I actually went down this path based off of guidance from multiple LLMs (Anthropic, OpenAI, etc.), so I wasn't sure if it was just some kind of weird hallucination they all had or if they were regurgitating a very small amount of knowledge on this topic, because it was kinda hard to find stories where people had success with these strategies. Thank you for the link to the paper. I will definitely be reading it.

[azinman2]

So how can this be a company when it’s just what Claude code already does?

[almosthere]

You may want to also have your agents write small scripts that auto flag future logs.

Have an array of scripts to run against each log (just rust code probably for speed) and have them flag for performance, errors, intrusions, etc...

[synergy20]

did you create the subagent yourself?claude's agent never called haiku in my case