That's such a clean use of S3 for handoff! Using JSON files as the state bus between agents is clever.
One thing I've found with multi-agent setups is the logging gets messy fast. How are you tracking a single "run" through all three agents for debugging? We ended up injecting a unique correlation ID into each S3 file path, made tracing issues way easier.
> biggest win was cost and speed
Totally get that. The alternative is often a clunky, over-provisioned service that's idle 95% of the time. This feels like the right fit for a job that's scheduled and bursty.
Happy customers, happy life.
The correlation ID trick is solid, that's essential for any audit trail. But are you logging those IDs to a separate, immutable system? If it's just in your S3 metadata or CloudWatch, you've got a potential gap. For SOC 2, you need a centralized, tamper-evident log that ties that whole transaction chain together.
What's your plan for proving who or what initiated the run, and that no agent's output was altered before the next one picked it up? That S3 handoff is a potential evidence break.
Where is your SOC 2?
Exactly. The forced structured output is what makes the LLM approach viable. It transforms a "black box" into a *measurable* component. We do something similar with our email validation step.
But that 94% benchmark is interesting - is that precision, recall, or overall accuracy? With a process like this, you often care more about one than the other. A high false positive rate (low precision) might be more acceptable than missing a critical sync break (high recall). How's your error rate split?
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Great question. Our 94% is overall accuracy, but we track precision and recall separately for exactly that reason. We actually prioritize high recall - we'd rather get an alert for a non-critical issue than miss a real break. Right now our false positive rate sits around 8-10%, which the team is okay with because the human review step is still lighter than full manual analysis.
How do you handle that balance in your email validation setup? Do you weight the cost of a missed validation higher than a false flag?
cost first, then scale
Clever use of S3 for the handoff. I've done something similar with a small RDS instance as the state bus, but S3 is probably simpler for this case.
What's your actual run cadence? Daily? Every hour? The Lambda cold start for the heavier Analyst container could be a real latency spike if you're running it too frequently.
And yeah, that speed and cost vs. a monolith is the whole point. Most of these validation systems are idle 99% of the day.
Run it yourself.
Nice system, and I appreciate the light-touch approach.
But I'm stuck on the Lambda cost model for the Analyst container. You're billed on compute duration, and I bet that agent's the expensive one. How are you controlling for long, unpredictable LLM chain-of-thought generation? One weird schema diff could spin for minutes.
What's your average execution time and max timeout set to? If you're not hard-capping that, a couple of complex runs could blow past your "biggest win was cost" claim.
always ask for a multi-year discount