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We compared seven Braintrust alternatives on the criteria that matter once agents are live: session tracing, classifier coverage, real-time alerting, and full log coverage. Here's what we found.
Most startups find out their AI agent broke from a support ticket, not a dashboard. Here's what real production monitoring looks like and where standard observability stacks miss it.
We ran both platforms against real production agent data. Here's what we found about tracing depth, failure detection, and which tool actually gets you from alert to fix.
We tested six LLM observability platforms against real multi-step agent failures. Most tools look fine until your agent breaks silently at 2am and you can't tell why.
We analyzed 12 million production agent logs and 78% of failures were silent. Here's what that means for how you choose between Braintrust and Sentrial.
We compared Sentrial and Langfuse across tracing, automated evals, alerting, and debugging. Here's what the data tells engineering teams running production agents.
Most teams think tracing is done once spans appear in their dashboard. It's not. Here's what complete AI agent observability actually looks like in production, from instrumentation through automated evals to source-code debugging.
Most agent failures never throw an error. We went through 12 million logs and found 78% of failures are completely silent, here's what it takes to actually catch them.
We analyzed 12 million production logs and found 78% of LLM failures never triggered a single alert. Here's the full monitoring workflow that actually catches them.
Unit billing scales faster than most teams expect for agentic workloads, and picking the wrong retention tier is a mistake you won't notice until six weeks into production.
We've analyzed millions of agent logs and the pattern is the same everywhere: passing evals, silent production failures. Here's what evals actually catch, where they break down, and how to build a system that finds the failures before your users do.
We analyzed 12 million logs and found 78% of agent failures are silent. Here's how to run prompt A/B tests that catch them, not just latency spikes and rubric scores.
Most agent test suites are built for deterministic software. Here's how we run a two-layer regression system that catches the behavioral failures those suites miss entirely.
We analyzed 12 million agent logs and found 78% of failures never trigger an alert. Here's where Arize Phoenix and Braintrust each stop, and what fills the gap.
We analyzed 12 million agent logs and found 78% of failures produced zero error signals. Here's what traditional monitoring misses about AI observability, and how to actually catch it.
78% of AI agent failures never throw an error. Here's which monitoring tools are actually built to catch them, and which ones just swap one metrics dashboard for another.
Multiple billing meters run at once, and AI agent workloads activate combinations that infrastructure teams have never had to model. Here's how the math actually works.
We analyzed 12 million agent logs and found 78% of failures were completely silent, no crash, no error code, no latency spike. Here's what LLM observability actually requires to catch them.