Evaluating autonomous failure prevention for AI agents on the leading customer service AI benchmark.

Automated Hallucination Correction for AI Agents: A Case Study on Tau²-Bench

Once your AI agents are live, the hard part begins: keeping them reliable. Cleanlab’s new Expert Guidance feature shows how non-engineers can teach AI systems to think and act better instantly, in natural language.

Expert Guidance: Teaching Your AI How to Behave

AI agents succeed when teams separate the Core and Reliability stacks. The Core drives differentiation through architectures, prompts, tools, and context. Reliability ensures trust with guardrails, monitoring, and validations. Learn why the split matters and how top teams deliver agents that are both innovative and dependable.

The Emerging Reliability Layer in the Modern AI Agent Stack

AI agents often give wrong, IDK, or unhelpful answers that frustrate users. Expert Answers let nontechnical SMEs instantly fix these cases, making your AI more helpful without waiting for engineers.

Expert Answers: The Easiest Way to Improve Your AI Agent

Existing Structured Outputs datasets are unreliable, so we created four new ones.

LLM Structured Output Benchmarks are Riddled with Mistakes

From guardrails to remediation, people keep AI agents aligned in production. Discover the oversight roles, levels of involvement, and steps engineering leaders can take to scale responsibly.

Managing AI Agents in Production: The Role of People

AI agents are moving into enterprise workflows, but unpredictability remains at every step. Leaders must understand four risk surfaces and how to contain them with layered safety systems.

AI Agent Safety: Managing Unpredictability at Scale

Using AgentLite to study how much LLM trust scoring can reduce incorrect responses from popular agentic frameworks: Act, ReAct (zero/few shot), PlanAct, PlanReAct.

Benchmarking real-time trust scoring across five AI Agent architectures

A case study on a reliable Customer Support Agent built with LangGraph and automated trustworthiness scoring

Prevent Hallucinated Responses from any AI Agent

A comprehensive benchmark of evaluation models to automatically catch incorrect responses across five RAG applications.

Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?

See results from using the Trustworthy Language Model to: detect hallucinations/errors from the o1 model and improve its response accuracy.

OpenAI's o1 surpassed using the Trustworthy Language Model

Evaluating state-of-the-art tools to automatically catch incorrect responses from a RAG system.

Benchmarking Hallucination Detection Methods in RAG

Announcing Cleanlab's Trustworthy Language Model. TLM overcomes hallucinations, the biggest barrier to productionizing GenAI, by adding a trust score to every LLM output.

Overcoming Hallucinations with Cleanlab

Even advanced AI models still hallucinate, producing confident but wrong answers that can harm trust and compliance. Cleanlab’s trustworthiness guardrails, powered by the Trustworthy Language Model (TLM), block inaccurate responses in real time and deliver safe fallback or expert-verified answers to keep AI systems reliable in production.

Preventing AI Mistakes in Production: Inside Cleanlab’s Guardrails

How enterprises can use LLMs to reliably catch compliance violations like GDPR from log files.

Safeguard Customer Data via Log Compliance Monitoring with the Trustworthy Language Model

Benchmarking LLM trustworthiness scoring mechanisms to improve LLM abstention and response-generation.

Automatically Reduce Incorrect LLM Responses across OpenAI's SimpleQA Benchmark via Trustworthiness Scoring

Demonstrating how the Trustworthy Language Model system can produce better responses from a wide variety of LLMs

Automatically boost the accuracy of any LLM, without changing your prompts or the model

Ensure reliable answers in Retrieval-Augmented Generation, while also ensuring that latency and compute costs do not exceed the processing needed to accurately respond to complex queries.

Reliable Agentic RAG with LLM Trustworthiness Estimates

TLM Lite allows you to generate high-quality responses using advanced LLMs while employing smaller models for fast and cost-effective trustworthiness scoring.

TLM Lite: High-Quality LLM Responses with Efficient Trust Scores

Benchmarking hallucination detection via the Trustworthy Language Model, with the newest models from OpenAI and Anthropic.

Automatically detecting LLM hallucinations with models like GPT-4o and Claude

Generate AI, not headaches. Automate heterogenous data source curation with Cleanlab document support.

Curate large scale document collections - Cleanlab

Don’t Let Your Messy Documents Run You RAG-Ged. Announcing Document Curation in Cleanlab Studio

A personal perspective on the importance of clean data as Cleanlab announces $30M in funding to bring automated data curation to enterprise AI.

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