Kalytera

Kalytera is an LLM evaluation platform that monitors AI agent performance in production environments with complete session coverage rather than sampling. Unlike traditional evaluation tools that analyze a subset of interactions or require manual review, Kalytera evaluates every step of every agent session in real time and identifies failure patterns as they occur. The platform surfaces plain-English root cause explanations rather than opaque numerical scores, making it possible to understand why an agent failed without digging through logs or reconstructing conversation flows manually.

The monitoring system scores interactions step by step across four quality dimensions. Accuracy measures factual correctness given available context on a zero to one scale. Goal alignment tracks whether the agent addressed the user's actual needs rather than related but different objectives. Decision quality assesses the soundness of reasoning and tool selection choices. Completeness checks whether responses fully resolved the request. Each dimension gets scored at every step rather than just at conversation end, which catches mid-workflow failures that aggregate session metrics would miss entirely. The default pass threshold sits at 0.70 overall score but remains configurable per dashboard.

Pattern detection groups recurring failures automatically using loss pattern analysis. When multiple sessions fail in similar ways, Kalytera clusters them together and generates explanations describing the common cause. Patterns get ranked by frequency, first detection date, and week-over-week trend with flags for worsening conditions. This transforms debugging from reviewing individual conversations to understanding systemic issues. The platform identifies seven specific failure types across agent deployments covering wrong answers, tool failures, goal drift, incomplete responses, hallucinations, context loss, and infinite loops.

Industry-specific evaluation weights recognize that different domains have different quality requirements. Healthcare agents receive accuracy weights of 0.50 and goal alignment at 0.30. Coding assistants weight accuracy at 0.50 with decision quality at 0.20. Retail and customer service agents prioritize goal alignment at 0.40 and completeness at 0.25. Marketing agents emphasize goal alignment at 0.45 and decision quality at 0.20. The default profile balances accuracy and goal alignment at 0.35 each. These weights apply automatically based on agent categorization so scores reflect genuine quality within each context.

Integration requires minimal code changes through the Python SDK. After installing via pip and configuring an API key, developers add a decorator or manual trace call to existing agent code. The decorator approach wraps functions with no other changes needed, while manual tracing provides more granular control over session and step identification. Framework support covers LangChain, CrewAI, AutoGen, and custom implementations. The service operates in a fire-and-forget mode that returns trace calls in under five milliseconds, never blocks agent execution, never raises exceptions, and automatically retries failed sends from a local queue.

Dashboard views present information at three levels. The agent overview shows seven-day quality trends, pass rates, active failure counts, and top failure types. The failure feed displays real-time individual failures and clustered patterns with root cause explanations. Interaction detail provides full step-by-step traces with per-step scoring and failure highlights. Scores appear in the dashboard within thirty seconds of interaction completion.

Pricing scales with session volume across four tiers. A free tier covers ten thousand sessions monthly with all core features and no credit card required. The Starter tier at forty-nine dollars monthly handles fifty thousand sessions with custom quality weights, multiple API keys, unlimited team members, and email support. Growth at one hundred forty-nine dollars monthly supports two hundred thousand sessions with priority support and JSON export. Enterprise arrangements accommodate higher volumes with Kubernetes VPC deployment, SAML SSO, RBAC, SOC 2 compliance, BAA availability, and dedicated SLA. The evaluation methodology is published openly through a research agenda covering topics from production quality taxonomy to causal inference for business impact. For teams operating AI agents in production who need visibility into quality beyond aggregate success rates, Kalytera provides the observability layer that catches problems before users report them.