Monte Carlo announced the launch of Agent Observability, a capability that provides end-to-end visibility across the data + AI stack.
This enables teams to detect, triage, and resolve AI reliability issues in production, preventing costly data + AI downtime, preserving customer trust, and ensuring AI-powered products are accurate, relevant, and reliable.
With this release, Monte Carlo unifies observability across both data and AI stacks within a single platform, allowing teams to ensure the quality of agent inputs and outputs.
“For data and AI teams, reliability isn’t a ‘nice to have,’ it’s the foundation for building scalable, adopted, revenue-driving AI products,” said Barr Moses, co-founder and CEO of Monte Carlo. “When AI agents fail, the consequences are massive and long-standing: low adoption of costly and time-consuming work, erosion of customer trust, and a huge hit to the bottom line of the business. Point solutions to solve siloed problems simply won’t cut it anymore. Our customers need a unified approach to ensure their AI agents are behaving as they should, delivering trustworthy outputs, and driving real value.”
With this release, Monte Carlo breaks down silos and empowers teams to detect, triage, and resolve reliability issues from data ingestion and transformation to AI retrieval and response.
With Agent Observability, data + AI teams can detect poor AI outputs using LLM-as-judge or deterministic evaluations, as well as performance issues and failures. Users can set criteria for what “correct” AI output looks like, and get automatically alerted when agent responses underperform in production. The solution is highly customizable, allowing data + AI teams to monitor a diverse range of quality criteria, adapted to the requirements of each organization and use case.
Agent Observability also includes a suite of built-in low-code evaluations that address the most common factors impacting agents. These can detect when outputs become less relevant or less helpful to user queries, flag declines in clarity and readability, identify mismatches in language, and track whether tasks are being successfully completed.
With Agent Observability, teams can quickly uncover the root cause of performance or reliability issues, reducing downtime and keeping AI agents running smoothly. It tracks key signals like prompts, completions, user queries, latency, and errors, giving teams a clear view into how agents are performing in production.
All telemetry is stored within the customer’s existing data warehouse, lakehouse or lake, making it easier to connect poor outputs back to underlying issues. Sensitive tracing data never leaves the customer’s infrastructure, ensuring both transparency and security.
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