How to Catch Silent Failures in AI Agents in Production
TL;DR
- Error and uptime monitoring catches failed tool calls. It misses an agent that finishes the run and gives the wrong answer.
- Raindrop catches those failures three ways. Signals track behaviors you describe. Issues are shifts in behavior that Issue Detection finds without rules or thresholds. Stumbles are single conversations where the agent got it wrong.
- Traces with tool calls and their results show how the agent reached a wrong answer. Triage Agent reads them for you in Slack, the web app, or over MCP.
- Braintrust, Langfuse, Arize AI, and LangSmith all trace and evaluate agents. The difference is how much of the failure-finding you set up yourself.
Why agents fail silently while infrastructure looks healthy
Monitoring built for web services watches exceptions, latency, and availability. An agent can make a bad decision without touching any of them.
Take a support agent asked whether a customer qualifies for a refund. It fetches the customer's invoice instead of the refund policy, decides the invoice settles the question, and tells the customer no. The tool call returned a 200. The response took two seconds. Every dashboard is green, and the customer got the wrong answer.
That is a silent failure: the run completes, nothing errors, and the outcome is wrong. Silent failures usually follow a change that never throws. A model swap changes how the agent reads requests. A prompt edit changes which tool it reaches for. A retrieval change alters which documents it treats as evidence. You find out when someone reads the outputs or a customer complains.
Signals, Issues, and Stumbles
Raindrop has three separate ways to catch a failure. Each answers a different question, so keep them apart.
Signals: behaviors you already know to watch
A Signal is a behavior you describe in plain language, like "the agent promises a refund without checking eligibility" or "the user is frustrated." Raindrop drafts a classifier from your description and shows you what it matches in your real events. You label examples until the matches look right, then save. From then on the Signal labels every new event and backfills a sampled 90 days, so you get a rate over time and the conversations behind it. Signal Alerts post to Slack when a Signal crosses a threshold you set.
A Signal only finds what you described. That is the point of it: you know this failure exists and want to count it.
Self Diagnostics is a related option. The SDK gives your agent a tool to report problems it recognizes, like a package that won't install or a doc it can't find, and those reports show up as Signals.
Issues: shifts you didn't define
Issue Detection needs no rules, thresholds, or eval sets. Raindrop forecasts normal behavior for each metric it monitors, such as tool errors, latency, and Signal rates, and flags deviations: a tool erroring more than it used to, a failure spreading across users, a regression after a deploy. An investigator reads the underlying traces before Raindrop publishes an Issue. Each Issue has a severity, affected user and event counts, a trend, and the events that show it.
Say a deploy breaks the order-lookup tool for one account type. No Signal covers it, and you never wrote an alert for it. Issue Detection sees that tool's error rate climb above its forecast and publishes an Issue with the affected users.
Stumbles: one conversation that went wrong
A Stumble is a single flagged conversation where the agent caused a bad outcome. It shows what happened, why Raindrop flagged it, and a timeline of the key moments. A Similar panel lists other Stumbles with the same pattern, which is how a one-off points you to something bigger.
The refund denial above is a Stumble case. One wrong answer doesn't move a metric, so it won't become an Issue. If the customer pushes back or the agent clearly didn't do what was asked, Raindrop can flag that conversation as a Stumble.
| You set up | Catches | Example | |
|---|---|---|---|
| Signals | A description and a few labels | A known behavior, on every new event | Agent promises a refund without checking eligibility |
| Issues | Nothing | Shifts in behavior across many runs | A tool's error rate climbs after a deploy |
| Stumbles | Nothing | One conversation with a bad outcome | A customer wrongly denied a refund pushes back |
Signals are on every plan. Issue Detection and Stumbles are on Pro.
From detection to diagnosis
An Issue or a Stumble tells you something went wrong. The trace tells you why. If your instrumentation records tool calls and their results, you can see what the agent saw before it answered. For the refund agent, the trace shows whether retrieval returned an outdated policy or returned the current one and the agent ignored it. Those need different fixes.
Triage Agent does that reading for you. Ask it in Slack, in the web app, or from a coding agent over MCP. It searches the related events, compares the affected conversations, counts affected users, and tells you what to fix. Check its answer against the traces before you change a prompt, a retrieval source, or a tool. Over MCP, your coding agent can pull the Issue and its traces and write the fix itself.
What to ask before you buy
Give every vendor the same real failed run, ideally one that finished without errors.
- Can you reconstruct the wrong answer? Ask them to show the tool calls, the retrieved material, and the step where the run went wrong.
- What do you find without a rule? Ask how the platform would surface a failure you never wrote an eval or alert for, and what triggers it.
- How do you get from one run to a pattern? Ask them to group related runs and show the evidence behind the grouping.
- What runs on every event, and what is sampled? Capturing a trace, scoring it, and alerting on it are three different things. Ask about each one.
How Raindrop compares with Braintrust, Langfuse, Arize AI, and LangSmith
All five platforms trace agent runs. They differ in what finds the failures you didn't predict. Details are from each vendor's docs and pricing pages as of November 2026.
| Platform | How failures surface | Investigation | Hosting |
|---|---|---|---|
| Raindrop | Signals for behaviors you define. Issue Detection and Stumbles find failures without rules or thresholds | Triage Agent reads the traces in Slack, the web app, or over MCP | Raindrop Cloud, with more deployment options on Enterprise |
| Braintrust | Scorers you configure on production logs and datasets: LLM-as-a-judge, autoevals, code, or human review | Inspect logs, add failures to datasets, and run experiments against them | SaaS, bring-your-own-cloud, or self-hosted |
| Langfuse | Evaluations you configure. LLM-as-a-judge runs on your own provider key | Trace view. You investigate recurring failures yourself | Free open-source self-hosting or Langfuse Cloud |
| Arize AI | Online evals on traces, offline evals on datasets, and a monthly quota of "Signal" issues (10 on Free, 25 on Pro) | Trace view and the Alyx assistant | Arize AX cloud. Self-hosting is Enterprise only. Phoenix is open source |
| LangSmith | Online evaluators and automation rules you configure, plus Insights reports that group usage patterns and failure modes | Trace view. Insights jobs take up to 30 minutes and need a model in your workspace | SaaS. Self-hosted and hybrid are Enterprise only |
Raindrop fits when an agent can finish a run and still fail the user, and you want those failures found without writing every check first. Ask to see a real Issue, the events behind it, and the evidence Triage Agent used. To test the fix, Simulations (early access) replays production scenarios against the pull request before merge.
Braintrust fits when evaluating changes before release drives the decision and you already keep evaluation datasets, or want to build them from production failures. Ask how it surfaces a production failure that none of your scorers check for.
Langfuse fits when you need open-source tracing you can host yourself. Budget for configuring evaluations, investigating recurring failures, and running the deployment.
Arize AI fits when you run evaluations across many models and frameworks. Ask how its Signal issues find failures you didn't write evals for, and what they cost at your trace volume.
LangSmith fits when you build on LangChain or LangGraph, though it works without them. Insights covers periodic reviews of failure patterns. If you need to hear about a failure while it is happening, test that specifically.
FAQs
Will Signals and Issue Detection alert me to every failure? No. A Signal catches only the behavior you described. Issue Detection catches shifts that move a metric, so a single wrong answer won't become an Issue, though Raindrop may flag it as a Stumble. In Settings, you choose which Issues, Stumbles, and Signal Alerts go to Slack or email, including a minimum Issue severity. No tool catches every wrong answer.
What counts as a silent failure rather than a tool error? A tool error is a timeout or a rejected request that your code can log. A silent failure is a run that completes without errors and still gives the wrong answer, like an agent that summarizes the wrong documents as if they answered the question.
How is grouping different from searching logs? Log search finds records that match terms you already know. Issue Detection groups the events behind a shift in behavior, and the Similar panel groups Stumbles that share a pattern, so you can read related failures without knowing what to search for.
Does Raindrop check every event? A saved Signal labels every new event. The 90-day backfill that runs when you create a Signal is sampled to stay within budget. Issue Detection runs on the traces you send, with no eval set to write first.
Which plans include this? Signals and Triage Agent are on every plan, including the free Hobby plan (1 custom Signal and 15 Triage Agent messages a month). Issue Detection and Stumbles are on Pro, which is $299 a month plus event usage with a 14-day trial and includes 10 custom Signals and 500 Triage Agent messages. See current plans.
How to evaluate your shortlist
Give each vendor the same failed run. Ask them to reconstruct its tool calls, show how they would catch similar wrong outcomes across runs, and explain what would trigger a notification. With Raindrop, define a Signal for a refund failure you already know about, then look at what Issues and Stumbles surface that the Signal didn't. Read the events behind each one before you decide whether the detection is useful for your agent. Get started for free.