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We evaluated several hallucination detection methods on OpenAI's recently released SimpleQA benchmark. RELAI agents detected over 76% of GPT-4o's hallucinations with just a 5% false positive rate. Even more impressively, RELAI detected nearly 1/3 of GPT-4o's hallucinations with

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Honored to have contributed to the House Bipartisan Task Force on Artificial Intelligence's report. Learn more about it here: science.house.gov/press-releases… Access the full report: speaker.gov/wp-content/upl… I hope this effort contributes to advancing AI that is reliable, safe, and

Congratulations to RELAI's founder & CEO for receiving the #PECASE award!

We're hiring a backend developer! Apply here: forms.gle/AWVQZd59TNLtED…

🚀 Meet our Data Agents! 📅 Want your own custom benchmarks? Book a demo here: calendly.com/d/crx2-k7b-pcm

Vote! Quantitative results on RELAI leaderboard will be released soon! ⌛

Our leaderboard is now live! Check it out at: relai.ai Let us know if you want to add your model or data to our leaderboard: calendly.com/d/crx2-k7b-pcm

Our legal reasoning benchmark is live! Check it out! Let us know if you want to add your model or data to our leaderboard: calendly.com/d/crx2-k7b-pcm

Looking forward to the Agentic AI Summit in Berkeley! If you’re working on agentic systems and interested in deep AI agent optimization, let’s chat!

How good (or bad) is GPT-5 — and does it matter for you? I’ve been seeing a lot of posts lately debating the quality of GPT-5’s responses. I tried a few of the examples people mentioned. Here’s one from my own experiment (screenshot attached): I asked GPT-5 to solve a simple

Introducing Maestro: the holistic optimizer for AI agents. Maestro optimizes the agent graph and tunes prompts/models/tools, fixing agent failure modes that prompt-only or RL weight tuning can’t touch. Maestro outperforms leading prompt optimizers (e.g., MIPROv2, GEPA) on

Prompt Tuning ≠ System Tuning. Most AI agent failures are structural; we keep the agent graph frozen (modules & info flow), then wonder why agents hallucinate, misroute tools, or break guidelines. Meet Maestro: the first joint graph + config optimizer for AI agents. It

let’s talk instruction-following: In prod, “did it follow the spec?” matters more than vibes. IFBench is a challenging benchmark to check whether agents/models obey unseen output/format constraints (length windows, HTML/Markdown rules, sectioning, etc.). That’s a real

🎃 Here’s a sweet Halloween treat from RELAI: We built an AI agent that maps the best trick-or-treat route for you—optimized for time, distance, candy variety, and real walking paths. 👉 Try it free: platform.relai.ai/halloween Built at RELAI.ai, where we ship

🚀 Build AI agents that actually work — in just 2 hours! We’re launching Reliable AI Agent Sprints—free, fully virtual sessions to build practical, reliable agentic solutions. This isn’t a flashy demo contest; we’ll design, simulate, evaluate, and optimize real agents,

One notebook to: "build -> simulate -> evaluate -> optimize" your agentic RAG! 🔗 Notebook: colab.research.google.com/drive/1N9l0PhO…

A notebook showing how to optimize AI agents using the user feedback: