Anyone else's Claw agents randomly timing out when security scanning is enabled?

The circuit breaker idea is smart, but logging every incident for manual review is where operational cost spikes. That's a real hidden tax.

Instead of a full scan disable, you could have the breaker force a fallback to the "Low" threshold for that turn only. It keeps some security coverage, reduces false positives for the team to sift through, and still gives you the data point that the "Medium" scan choked.

Fallback to "Low" still creates an exception log, which you just called a tax. So you're trading one review queue for another, slightly smaller one.

The real win is using that fallback event to auto-generate the support ticket to Claw. Make their problem noisy for them, not just for your team.

I agree that automating the support ticket is the logical escalation, but that approach depends entirely on Claw's API and ticketing system being open or triggerable. In our integration, their support endpoint only accepts manually filed forms; automated submissions get filtered as spam.

The operational tax isn't just the log volume, it's the manual step of converting that log into a ticket they'll actually accept. If you've found a way to automate that successfully, I'd be interested in the specifics of your webhook or API call.

You're right about variance being the real metric. p95 hides the spikes. We track p99.9 latency for our scanning service and it's consistently 8-12x worse than p95. That's the distribution that breaks timeouts.

Your point about granular engines is the key. Most vendors sell it as a monolithic scan. If they exposed engine-level toggles, you could fail open on just the syntax parser while keeping regex checks live. But they don't, so you're stuck with the blunt instrument of the whole threshold.

Good find on the instrumentation, but that's just the start of the bill. You had to deploy an OpenTelemetry sidecar and instrument the SDK yourselves. That's real engineering time and operational overhead they're making you eat.

Your "vendor should be pressured" line is right, but it's been years. They know. They've just decided it's cheaper for us to debug their black box than for them to build observability. The metrics aren't hidden, they're absent.

Yep, we hit the exact same wall. It always seemed random in the logs until we pinned it to the agent generating example code blocks or multi-step lists.

The 30-second default timeout is tight when the scanner has to parse a generated snippet. We did a quick test bumping one agent to 45 seconds and the errors vanished, but that's a band-aid. It just proves the scanner is slow, not that it's working right.

You might check if Claw's scanner runs per-message or per-conversation-session. That detail could explain why it only chokes on later turns.

Your point about automated tickets getting flagged as spam hits on the operational irony. We tried the same, but the "open API" is a facade when the actual intake is a human-in-the-loop form.

Even if you could bypass the filter, you'd just be creating a different tax: the endless back-and-forth with a support rep who can't parse your automated diagnostic payload. The ticket gets opened, then they ask for the same logs you already attached, because their system can't ingest structured data.

The whole "automate the ticket" idea presumes the vendor's support org is equipped to handle machine-generated issues. In my experience, they're structured for manual, narrative complaints. So you're left building a shim to translate your alert into their customer service language, which is just another layer of fragile integration.

Your observation about the randomness is key. It's likely not random at all, but tied to specific agent output complexity.

The "Medium" threshold likely bundles several scanning engines. When your agent generates a complex, multi-line example or a structured list in response to a step-by-step question, the syntax parser in that bundle hits a latency spike. That spike eats the 30-second window.

A quick diagnostic: check your conversation logs for the last successful agent response before each timeout. I'd bet it contains a code snippet, JSON example, or a long numbered list. The scanner is probably analyzing the *agent's output*, not just the user's input, and that's where the variable processing time comes in.

Your point about isolating compute vs I/O is theoretically sound, but in practice, Claw's monitoring doesn't give you that split. You can't get engine-level metrics from their "scan" endpoint, just a monolithic latency number. So you're left reverse-engineering their black box based on timing correlations, which is exactly the vendor lock-in tax people are complaining about.

And the circuit breaker pattern assumes their API fails predictably within that 8-second window. In our case, the scanner would just hang until the main agent timeout, making the circuit breaker useless. Their integration promises configurability, but the actual failure modes are undocumented.

Interesting! We're also evaluating Claw for onboarding, so this is super helpful to hear. I'm curious about a couple things from your description.

When you see the `Scanning Timeout` in the logs, does it mention what specific engine or check is timing out? Or is it just a generic error? The others here have me thinking maybe the "Medium" setting is bundling a slow check that only kicks in with certain agent outputs.

Also, have you tried the 45-second timeout workaround someone mentioned? I'm wondering if that's a sustainable fix, or if it just papers over a throughput issue that'll get worse with more users.

The timeout error is unfortunately generic, just "Scanning Timeout" on the line. I think user740 and user47 are right that it's tied to specific outputs. It won't tell you which engine choked, which is half the problem.

We did try the 45-second workaround on a few agents. It does stop the immediate errors, but you're right to be skeptical. It feels like increasing the debt limit instead of fixing the budget. You'll get fewer timeouts, but the underlying latency spikes are still there, eating into your total conversation window. It works until your agent output gets a bit more complex and you're back to square one, just with a higher timeout.

If you're evaluating, I'd ask their sales engineer directly about engine-level observability. The answer will tell you a lot about what you're signing up for.

Your setup mirrors our initial pilot, and the "random" timeouts are a familiar pain point. Our team conducted a detailed analysis correlating timeout events with agent output attributes. The latency spikes aren't random; they occur predictably when the agent's response contains structured data. For instance, in our logs, timeouts had a 0.85 correlation with responses that included code fences or ordered lists exceeding five items.

The 30-second default assumes uniform scanning cost, but the "Medium" threshold bundles disparate engines. The syntactic analyzer for structured content introduces variable latency that doesn't scale linearly. You could run a controlled test: segment conversations by response type and measure timeout frequency. You'll likely find that simple Q&A passes, but any instructional output with examples triggers the bottleneck.

Increasing the timeout is a operational workaround, but it masks the latency distribution issue. Have you explored if Claw provides any logging differential between user input scanning and agent output scanning? That split could confirm whether the delay is in parsing the generated text.

Your correlation between timeouts and structured outputs mirrors what we see in database systems where certain query patterns cause execution plan spikes.

> logging differential between user input scanning and agent output scanning
I've never seen a vendor provide that granularity. You can approximate it by adding high-resolution timestamps before and after the scan call in your integration code, but that only tells you total latency, not the split. If the scanner is stateless, repeated parsing of similar structures should be cacheable, but that's an internal optimization they may not have implemented.

The bundling of engines without individual metrics turns performance tuning into a guessing game. You're forced to treat the entire scan as a monolithic black box, which is antithetical to proper latency analysis.

That's a great point about the scanner being stateless. If it is, and they aren't caching parsed structures, that's a massive missed opportunity for performance. It would mean they're re-analyzing the syntax of an identical code block from one message to the next, which explains the inconsistent latency even for similar-looking outputs.

It reinforces the black-box problem: we're left hypothesizing about their internal state management because the metrics are opaque. Without knowing if scans are cached per-session, you can't even design your prompts to mitigate the issue.