Agent Experience provides an Agent Experience Management (AXM) platform that helps enterprises design, measure, manage, and improve AI agents to reduce hallucinations and unreliable outputs. By focusing on token context limits, novelty handling, and proven failure modes, the infrastructure aims to make AI behavior more trustworthy for large‑scale business applications.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying large language models face frequent hallucinations and unreliable outputs, especially when using extensive context windows or novel domain data, leading to mistrust and operational risk.
Solution
Agent Experience offers an Agent Experience Management (AXM) platform that enables organizations to design, measure, manage, and improve AI agent interactions with a focus on reducing hallucinations. The system provides continuous monitoring of context size and data novelty to detect elevated failure risk. It supplies metrics and dashboards that quantify trustworthiness, allowing teams to apply zero‑trust controls and corrective actions. By integrating these tools into existing AI pipelines, enterprises can proactively mitigate AI‑generated falsehoods and maintain reliable performance at scale.
Target Audience
Primary customers are large enterprises and AI product teams that deploy LLM‑based agents in customer‑facing or internal automation workflows.
Features
- Real‑time monitoring of token context length with alerts when accuracy thresholds are breached
- Novelty detection engine that flags inputs outside the model’s training distribution
- Trustworthiness metrics suite quantifying hallucination rates and confidence scores
- Automated remediation workflows for adjusting prompts, truncating context, or routing to human review
- Dashboard and API for integrating AXM insights into existing AI deployment and observability stacks