Moda provides a continual learning layer for AI agents, automatically converting production agent traces into validated improvements. By detecting issues like policy drift, intent gaps, and tool errors, it patches prompts, updates schemas, and adds workflow guards, reducing friction and error rates across deployed agents.
Funding
Funding not disclosed
Founders
Product
Problem
AI-powered conversational agents often encounter production failures due to intent drift, schema changes, prompt ambiguities, and inefficient workflow loops, leading to high retry rates and manual maintenance overhead. These issues reduce user satisfaction and increase operational costs for businesses that rely on automated support and transaction flows.
Solution
Moda provides a continual learning layer that automatically ingests production agent trace data, diagnoses failure root causes, and generates validated policy, prompt, and tool updates. The platform clusters failed runs, identifies the underlying cause—such as stale policies, schema mismatches, or loop inefficiencies—and applies targeted fixes like prompt patches, model retraining, or escalation gate insertion. Updates are validated against a test set before being shipped to live agents, ensuring reliability without manual intervention. By closing the feedback loop in near real‑time, Moda reduces retry rates by up to 96% and shortens agent paths, delivering more consistent and frictionless user experiences.
Target Audience
Primary customers are product and engineering teams that deploy AI conversational agents for customer support, finance, or e‑commerce workflows and need automated maintenance of agent performance at scale.
Features
- Automated trace ingestion and clustering to surface failure patterns across intents, tools, and workflows
- Root‑cause analysis that attributes errors to prompts, tool schemas, memory notes, workflow loops, or model drift
- Continuous policy and prompt patching with confidence scoring and validation against an evaluation set
- Schema drift detection and automatic relearning of new argument shapes for integrated tools
- Loop detection and automatic insertion of escalation-to-human gates to break inefficient cycles
- Real‑time dashboards showing intent F1 improvements, retry reductions, and newly learned signals