Moda provides a continual learning layer that automatically converts production AI agent interaction traces into validated updates, improving agent performance without manual intervention. By detecting issues such as policy drift, intent gaps, and tool errors, Moda patches prompts, relearns schemas, and adds workflow guards—evidenced by reductions in refund error rates and eliminated loop sessions—across deployed agents.
Funding
Funding not disclosed
Founders
Product
Problem
AI conversational agents often encounter production failures due to intent drift, schema changes, prompt ambiguities, and inefficient workflow loops, leading to high retry rates, user frustration, and costly manual maintenance.
Solution
Moda provides a continual learning layer that ingests production agent trace data, automatically diagnoses failure root causes, and generates validated policy, prompt, and tool updates. The platform detects intent drift, schema mismatches, and loop inefficiencies, then patches prompts, retrains models, and inserts escalation gates without human intervention. Updated policies are tested against a validation set before deployment, ensuring reliability. By closing the feedback loop in near real‑time, Moda reduces retry rates by up to 96% and shortens agent paths, improving overall agent performance and reducing operational friction.
Target Audience
Moda targets product and engineering teams that operate large‑scale conversational AI agents, such as customer support bots, fintech assistants, and e‑commerce chat interfaces.
Features
- Automated trace ingestion and clustering to identify failure families (e.g., refund, policy, billing) with confidence scores
- Root‑cause analysis across prompts, tools, workflow, memory, and model layers
- Continuous validation pipeline that tests generated fixes before shipping to live agents
- Auto‑patching of prompts and policies, and automatic schema relearning for tool arguments
- Dynamic insertion of escalation gates to break inefficient loops and reduce retries
- Real‑time monitoring dashboard showing intent F1 improvements, friction metrics, and newly learned signals