Moda
Moda provides an engineering platform that automatically analyzes every production AI agent trace to detect silent failures like hallucinations, lazy answers, and broken tool calls. The platform groups detected issues into ranked problems, converts them into regression evals, and lets teams replay historical conversations to validate fixes before deployment. It also offers plain-language custom signal creation and alerts via Slack, Linear, or email.
- Developer Tools
- Artificial Intelligence
- AI Agents
- Software Only
- Data & Analytics
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
AI agents running in production fail in complex, non-obvious ways that don't throw traditional errors—hallucinated data, broken tool calls, lazy answers, and frustrated users often go undetected. Teams lack visibility into these failures and have no systematic way to learn from them, causing the same mistakes to recur across prompts, tools, workflows, memory, and model layers.
Solution
Moda provides an engineering platform that automatically analyzes every production agent trace end to end to pinpoint failure causes. The system detects problems that never throw an error, such as fabricated data, failing tools, and hallucinations, while allowing teams to add custom checks in plain language. Detected issues are transformed into evals, so teams can validate fixes against real traces before deploying. Monitors track problem frequency and cohorts over time, while replays let teams test changes against historical conversations to confirm improvements. The platform also clusters similar failures to identify systemic issues, such as "order lookup times out under load" or "benign already-exists conflicts treated as failures." Moda reports measurable outcomes including reduced refund-related errors by over 80% and cutting retry loops by 96% in real-time deployments.
Target Audience
Engineering teams running AI agents in production who need visibility into agent failures and a systematic workflow for turning those failures into validated regression tests.
Features
- End-to-end trace monitoring that detects silent failures including hallucinations, lazy answers, frustrated users, and failing tools
- Plain-language custom signal creation for team-specific success and failure criteria
- Automatic conversion of detected production problems into regression evals for validating fixes against real traces
- Replay functionality that tests proposed changes against historical production conversations before deployment
- Failure clustering to group similar issues across large conversation volumes for systemic root-cause analysis
- Cohort analysis and tracked metrics comparing broken tool calls, latency, token usage, and success scores over time
- Slack, Linear, and email alerting for immediate problem notification