Problem

AI agents fail silently in production: tools can error or time out, an agent can claim to have completed an action when it has not, and prompt injections, retrieval errors, or long conversations can conceal the real failure. The pain is difficult and expensive to diagnose because the “bug” is rarely one line of code; teams often resort to skimming transcripts and grepping logs. The economic stakes are unreliable customer experiences, wasted engineering and support effort, user frustration, and ultimately weaker retention and higher churn. The killer use case is a company operating a customer-facing or transactional agent that needs to identify failures in real usage—such as broken tool calls, missed handoffs, or previously unseen user intents—before customers discover them.

Product / Service

Moda is an observability and reliability layer for AI agents, delivered through an SDK, dashboard, and data API/CLI. Teams add a few lines of code to existing OpenAI, Anthropic, or framework calls; Moda captures the LLM calls in the background, clusters production conversations into a changing hierarchy of user intents, and surfaces behavior signals, tool failures, and emerging intents that the agent has not previously handled. It supports multiple model providers, so teams can switch models without changing the integration.

The product goes beyond log inspection through a trace-to-fix loop. It lets developers drill from a metric or alert into the exact messages and tool calls involved, diagnose whether a failure came from the prompt, tools, workflow, memory, model, or product logic, and turn recurring patterns into reviewable prompt changes, tool updates, workflow edits, verifier gates, evaluation cases, or reusable skills. Proposed improvements can then be replayed against historical production traces to measure quality, cost, and regressions before shipping, with the intended benefit of making every production interaction improve the agent.

Market

Moda competes in the AI-agent observability, monitoring, reliability, and broader LLM evaluation/tracing software market. Its closest alternatives include LangChain/LangSmith, Arize and Arize Phoenix, Braintrust, Langfuse, Fiddler, Galileo AI, and Opik by Comet. Moda’s positioning is differentiated toward discovery and continual improvement: rather than only tracing calls, it emphasizes clustering real conversations, detecting behavioral failures and novel intents, and validating concrete fixes against production traces.

The company appears to be an early-stage commercial venture rather than a scaled platform. Y Combinator lists it as an active Winter 2026 company founded in 2025, with a five-person team; PitchBook identifies it as private, seed-stage, reports $500,000 raised, and marks its seed deal as “Generating Revenue,” while leaving current revenue blank. Moda’s own materials invite prospects to book a demo and label the displayed product metrics as illustrative rather than customer averages. Accordingly, the public evidence supports early commercialization and institutional backing, but does not establish customer count, ARR, or enough information to call the company definitively pre-revenue.