🚀 New from Cleanlab: Expert Guidance AI agents running multi-step workflows can fail in tiny, trust-breaking ways. Expert Guidance lets teams fix these behaviors with simple human feedback, instantly. ✈️In one airline workflow: 76% → 90% after only 13 guidance entries.

We're thrilled to join forces with @joinHandshake, where we'll be able to scale our team's pioneering work to inflect change with the world's leading AI labs. Hear more from our CEO and Co-founder, @cgnorthcutt, to learn about our next chapter.

Achieving 20% improvement in structured extraction tasks using @DSPyOSS and GEPA Building on a blog post from @CleanlabAI I wanted to see how quickly I could optimize a structured extraction task with DSPy GEPA In about 3 hours (mostly me getting in the way of claude code): - 22 percentage points over vanilla structured outputs - Ran 4 experiments in total - ~$3 total cost I tested 5 approaches incrementally: • OpenAI Baseline: 32.1% exact match • DSPy Baseline: 39.8% • DSPy BAML: 42.7% • DSPy GEPA: 53.8% • DSPy BAML GEPA: 54.4%

For anyone who cares about structured output benchmarks as much as I do, here's an early Christmas present 🎁 ! Pretty well thought out from the folks @CleanlabAI. Seems like I'll def be using it to compare LLMs using BAML and DSPy! github.com/cleanlab/structur…

Where Did $37B in Enterprise AI Spending Go? $19B → Applications (51%) $18B → Infrastructure (49%) Our report includes a snapshot of the Enterprise AI ecosystem, mapped across departmental, vertical AI, and infrastructure. Although coding captures more than half of departmental AI spend at $4 billion, the technology is gaining traction across many enterprise departments: IT operations tools ($700M), marketing platforms ($660M), customer success tools ($630 M). AI-native startups are rapidly emerging across every job function, capturing a meaningful share of the $7.3B spent on departmental AI in 2025. mnlo.vc/enterprise-ai-25

Which LLM is better for Structured Outputs / Data Extraction: Gemini-3-Pro or GPT-5? We ran popular benchmarks, but found their "ground truth" is full of errors. To enable reliable benchmarking, we've open-sourced 4 new Structured Outputs benchmarks with *verified* ground-truth

🚧 Even the best AI models still hallucinate. OpenAI’s recent paper on Why Language Models Hallucinate shows why this problem persists, especially in domain-specific settings. For teams implementing guardrails, we put together a short walkthrough: youtu.be/i_6fjKgboFg?si=aaAE…

AI pilots prove intelligence, but AI in production demands reliability. The best teams separate their stack early: 🧠 Core = how AI thinks 🛡️ Reliability = how it stays safe That’s how prototypes become products. 👉cleanlab.ai/blog/emerging-re…

AI agents won’t replace humans. Their real power comes when humans guide it. We just added Expert Answers to our platform: 👩‍🏫 SMEs fix AI mistakes right away 🔁 Fixes are reused across future queries 📈 Accuracy improves, “IDK” drops 10x Full blog: cleanlab.ai/blog/expert-answ…

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-ai…

📍 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/2…

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-sa…

🚨 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/