{"slug": "kalytera-tells-you-why-your-ai-agent-failed-not-just-that-it-did", "title": "Kalytera – tells you why your AI agent failed, not just that it did", "summary": "Kalytera, an AI agent observability platform, automatically evaluates every step of an agent's workflow in real time and surfaces plain-English root causes for failures, catching issues that sampled or after-the-fact analysis misses. The tool integrates with LangChain, CrewAI, AutoGen, or any custom framework via a single line of code, operates with under 5ms overhead, and is free for up to 10,000 sessions per month. Kalytera's failure taxonomy and evaluation methodology are published openly, and the company reports that most teams discover previously silent failures within the first hour of use.", "body_md": "Evaluate every step automatically, surface the patterns behind failures, and get a plain-English root cause — without digging through traces.\n\nOne line to add. Works with LangChain, CrewAI, AutoGen, or any custom framework. Free for 10K sessions/month.\n\nAI agents fail differently than normal software. You need a different kind of observability to catch it — one that watches every step, not just the final output.\n\n`kalytera.trace()`\n\nis fire-and-forget — under 5ms, never raises, never blocks. Nothing is sampled. If a failure mode exists in your traffic, you will see it.Most teams connect Kalytera and find failures within the first hour that had been running silently for days. After 30 days the picture gets sharper — not because the tool changed, but because the loss patterns have had time to emerge.\n\nOne line to add. Failure patterns surface in 30 seconds. Free for 10,000 sessions per month.\n\nPay for sessions evaluated. No seat licenses. No surprise bills. Free tier works forever — upgrade when you need more.\n\nThe failure taxonomy and evaluation methodology behind Kalytera are published openly. The methodology is research, not proprietary lock-in.\n\nKalytera evaluates every interaction at every step, in real time — not a sample, not after the fact — and tells you exactly what broke, where, and why.\n\nEnterprise AI agents run complex multi-step workflows. Unlike traditional software, agents think and act differently in every interaction — standard quality checks aren't built for that. Eval and observability platforms exist, but they use sampled data and after-the-fact analysis. They miss failures mid-workflow, including one-off failures that could be catastrophic.", "url": "https://wpnews.pro/news/kalytera-tells-you-why-your-ai-agent-failed-not-just-that-it-did", "canonical_source": "https://kalytera.dev/", "published_at": "2026-07-23 21:37:16+00:00", "updated_at": "2026-07-23 21:52:26.031992+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools"], "entities": ["Kalytera", "LangChain", "CrewAI", "AutoGen"], "alternates": {"html": "https://wpnews.pro/news/kalytera-tells-you-why-your-ai-agent-failed-not-just-that-it-did", "markdown": "https://wpnews.pro/news/kalytera-tells-you-why-your-ai-agent-failed-not-just-that-it-did.md", "text": "https://wpnews.pro/news/kalytera-tells-you-why-your-ai-agent-failed-not-just-that-it-did.txt", "jsonld": "https://wpnews.pro/news/kalytera-tells-you-why-your-ai-agent-failed-not-just-that-it-did.jsonld"}}