Cleanlab makes AI agents reliable. Detect issues, fix root causes, and apply guardrails for safe, accurate performance.
San Francisco
Joined October 2021
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🚀 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.
News: @joinHandshake acquires @CleanlabAI!
This "ten-year old job marketplace" has quietly become a top human data lab for AI--building an AI research org, acquiring top AI talent, and advancing Cleanlab tech and research to lead data foundations for frontier AI.
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Cleanlab retweeted
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%
Cleanlab retweeted
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…
Cleanlab retweeted
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
Cleanlab retweeted
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
We discovered how to cut the failure rate of any AI agent on Tau²-Bench, the #1 benchmark for customer service AI.
Agents often fail in multi-turn, tool-use tasks due to a single bad LLM output (reasoning slip, hallucinated fact, misunderstanding, wrong tool call, etc). We introduce an automated LLM trust scoring + message revision pipeline that mitigates this brittleness and keeps agents on the rails.
Benchmarks show that our approach remains effective across all Tau²-Bench domains (Telecom, Retail, Airline) and different LLMs -- cutting agent failure rates up to 50%.
This pipeline can used to automatically make any agent more reliable.
Extensive benchmarks here: cleanlab.ai/blog/tau-bench/
👉 Full announcement here: cleanlab.ai/blog/expert-guid…
The reality: We’re moving from hype to hardening, building the reliability layer AI needs.
🔍 Read the full Cleanlab report → cleanlab.ai/ai-agents-in-pro…
📰 @Computerworld feature → computerworld.com/article/40…
🚧 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…
ALT AI Agent Risk Surfaces
🚨 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/
