Open-source AI agent monitoring platform. Latitude automatically detects all the ways your agents fail at scale, and gives your coding agent the tools to fix it.
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This looks like a great fit for teams running agents in production how does Latitude distinguish between an agent failing vs. just taking an unconventional but valid path to the goal?
Loved the idea and positioning! Congrats on the launch super excited to try it out
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Curious about the gives your coding agent the tools to fix it part does Latitude hand off a structured diagnosis to the coding agent or does it actually attempt the fix autonomously?
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How does this scale across thousands of concurrent agent runs? Is there a sampling approach or is every trace fully monitored?
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looks great! Congrats 👏
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What kinds of failure modes does it catch that traditional logging/observability tools typically miss?
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What does onboarding look like for an existing agent fleet is there meaningful setup required or is it closer to drop-in instrumentation?
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Have you tested this against multi agent systems where failures cascade across agents rather than staying contained to one? That seems like the harder problem.
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What's the security model for agent run data especially for teams whose agents touch sensitive internal systems or customer data?
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The clustering of conversations into discrete failure modes is the clever part. Most observability tools dump raw traces and leave you to find patterns yourself. We've spent time manually sifting logs to spot recurring failures. How does the automatic issue detection work? Does it use embedding clustering on trace outputs, or is there a rule-based approach?
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This looks like a great fit for teams running agents in production how does Latitude distinguish between an agent failing vs. just taking an unconventional but valid path to the goal?
EverTutor AI
Loved the idea and positioning! Congrats on the launch super excited to try it out
Curious about the gives your coding agent the tools to fix it part does Latitude hand off a structured diagnosis to the coding agent or does it actually attempt the fix autonomously?
How does this scale across thousands of concurrent agent runs? Is there a sampling approach or is every trace fully monitored?
What kinds of failure modes does it catch that traditional logging/observability tools typically miss?
What does onboarding look like for an existing agent fleet is there meaningful setup required or is it closer to drop-in instrumentation?
Have you tested this against multi agent systems where failures cascade across agents rather than staying contained to one? That seems like the harder problem.
What's the security model for agent run data especially for teams whose agents touch sensitive internal systems or customer data?
The clustering of conversations into discrete failure modes is the clever part. Most observability tools dump raw traces and leave you to find patterns yourself. We've spent time manually sifting logs to spot recurring failures. How does the automatic issue detection work? Does it use embedding clustering on trace outputs, or is there a rule-based approach?