Cleanlab @CleanlabAI
Cleanlab makes AI agents reliable. Detect issues, fix root causes, and apply guardrails for safe, accurate performance. cleanlab.ai San Francisco Joined October 2021-
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Launching an AI agent without human oversight is basically launching a rocket without mission control 🚀 Cool for a few minutes… until something breaks. 🕹️ It’s not the rocket that makes the mission succeed. It’s the control center. cleanlab.ai/blog/managing-…
📍 Live at @AIconference 2025 in San Francisco! Tomorrow, @cgnorthcutt is sharing practical strategies for building trustworthy customer-facing AI systems, and our team is around all day to connect. 👋 Stop by and geek out with us!
Most AI pilots in financial services never make it to production. The reason is simple: they can’t be trusted. Today, Cleanlab + @CorridorAI are fixing that by combining governance with real-time remediation so AI is finally safe to deploy at scale. 🔗 businesswire.com/news/home/2025…
AI safety is not a feature. It is infrastructure. AI agents are probabilistic, which means unpredictability is guaranteed. The 4 risk surfaces every team building AI agents must address: - Responses - Retrievals - Actions - Queries 👉 cleanlab.ai/blog/ai-agent-…
🚨 Next week at @AIconference in San Francisco: @cgnorthcutt will share practical strategies with guarantees for building customer-facing AI support agents you can actually trust. 🗓️ Sep 18 | 12:00–12:25 PM 👉 Don’t miss it. aiconference.com
Today's AI Agent architectures (ReAct, Plan-then-Act, etc) produce too many incorrect responses. Our new benchmark confirms this, evaluating 5 popular Agent architectures in multi-hop Question-Answering. We then added real-time trust scoring to each one, which reduced…
💡 Trust Scoring = More Reliable AI Agents AI engineer Gordon Lim's latest study shows that trust scoring reduces incorrect AI responses by up to 56% across popular agents like Act, ReAct, and PlanAct. 🔍 Explore the full study: medium.com/data-science-c…
Automate detection of unreliable LLM outputs by combining MLflow tracing with @CleanlabAI's Trustworthy Language Models (TLM). 🚀 This blog post covers: ✅ Setting up MLflow to capture complete LLM interactions, including system prompts. ✅ Retrieving traces and efficiently…
If your AI agent makes a mistake, Cleanlab can either provide a more reliable response or flag the case for human review. New tutorial: Add Cleanlab as a trust layer for any conversational agent. 👉help.cleanlab.ai/codex/tutorial…
Let's build a "Chat with your Code" RAG app using Qwen3-Coder:
AI agents don’t just fail from hallucinations. They fail when tool calls go wrong—wrong tool, bad input, skipped step. We dropped a new tutorial to score tool calls for trust so you can catch failures early, before they hit users. 👉 help.cleanlab.ai/tlm/tutorials/…
🤖 🛡️ Cleanlab Trust Scoring Cleanlab's powerful trust scoring system prevents AI hallucinations in customer support, seamlessly integrating with LangGraph to detect and block problematic responses before reaching users. Explore the technical implementation here:…
🤖 Building with @OpenAI’s Agents SDK? This new tutorial shows how to catch low-trust outputs before they reach customers. • Auto-handle incorrect AI responses • Prevent failures in multi-agent handoffs • Improve reliability without retraining 👉 help.cleanlab.ai/tlm/use-cases/…
@MITSTEX startup spotlight: @CleanLabAI MIT startup @CleanLab partners with @nvidia to tackle the biggest problem in Enterprise AI: outputs you can trust. Full story: developer.nvidia.com/blog/prevent-l…
How to build support agents that are safe, controllable, work, and keep you out of the news. Use @CleanlabAI directly integrated with @LangChainAI. Cleanlab is the most integrated and most accurate real-time safety/control layer for Agents/RAG/AI.
How to build support agents that are safe, controllable, work, and keep you out of the news. Use @CleanlabAI directly integrated with @LangChainAI. Cleanlab is the most integrated and most accurate real-time safety/control layer for Agents/RAG/AI.
🛑Prevent Hallucinated Responses Our integration with @CleanlabAI allows developers to catch agent failures in realtime To make this more concrete - they put together a blog and a tutorial showing how to do this for a Customer Support agent Blog: cleanlab.ai/blog/prevent-h…
We’re on a quest to make customer support chatbots more trustworthy. 🤖 Our new case study with @LangChainAI shows how to catch hallucinations and bad tool calls in real time using Cleanlab trust scores. LangGraph fallbacks make fixing them easy 👇 cleanlab.ai/blog/prevent-h…
Singapore Government just dropped the Responsible AI Playbook - not just talk, but actual technical guidance for deploying AI systems safely. Their key recommendations: - LLMs are like "Swiss Cheese" - full of unpredictable capability holes. - Guardrails for reliable LLM apps…
We asked the Databricks AI + Data Summit chatbot where the Cleanlab booth was. It replied: “I couldn’t find any information…” and spit out some code. 🤖💥 It's a good thing we’re here! We exist to make AI answers more trustworthy, even at AI conferences. 😎
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