Moduna

Production AI agents generate thousands of conversations that disappear into logs. Moduna instruments the agent once — the vendor describes a Python SDK integration that takes a framework name, app name, and API key — and continuously clusters those conversations into intent groups, ranked by unresolved demand, failure rate, and downstream impact. The output is a dashboard that tells product teams which workflows to add, which handoffs are missing, and which intents the agent handles confidently but unsuccessfully.

The differentiating claim is blind-spot detection: catching cases where the agent appears to succeed by conventional trace metrics but the user never finished the job. Clarification loops, confident non-answers, missing escalation paths — Moduna surfaces these as a ranked list of improvement opportunities rather than raw log noise. The vendor’s dashboard example shows 14 blind spots identified from 12,400 conversations, with a 27% average non-resolution rate surfaced across 38 intent clusters.

This tool fits product and agent teams who have already shipped an agent and need evidence for roadmap prioritization that goes beyond support ticket volume. It does not fit teams looking for real-time error alerting, infrastructure monitoring, or automated agent correction — Moduna produces structured insight, not intervention. Teams that need to act on a failure within seconds will find the analysis-to-decision loop too slow; teams that run weekly roadmap reviews will find the ranked opportunity output directly actionable.

Integration targets LangChain explicitly in the vendor’s code sample, which means teams running other frameworks should verify SDK support before committing. The platform is paid-only with no self-hosted option, so all production conversation data routes through Moduna’s infrastructure — a consideration for teams under strict data residency requirements.