Quotient AI
@QuotientAI
We’re building tools to catch agent mistakes and automatically fix them. Join our test kitchen: https://discord.gg/YeJzANpntv
Join us next week for AI Builders Night an informal demo + networking night with some of Boston’s top AI builders and practitioners. 🍕🥂 Food + drink provided.
“Ship and ship sooner than you feel you're ready. You'll learn so much more from your agent being in production than you ever could in dev.” - @julianeagu, Quotient AI Julia shared why real-world deployment is your best teacher for AI agents.
can every AI agent get better, and better, and better.. automatically? humans learn from their environments, so why wouldn't agents? nurture is built in production shared a first glimpse of what's possible @lisbonai_
jet lagged and about to demo some never seen before alpha we *just* cooked
jet lagged and about to demo some never seen before alpha we *just* cooked
hands on the best conference venue I’ve seen - tucked away in a forest outside of the city @lisbonai_
We obsess over structure. Every pattern we uncover in chaos unlocks new potential.
Qdrant just launched an in-depth course on mastering production-grade vector search, and we’re part of it! We teamed up to show how to analyze and debug AI systems using @QuotientAI + @qdrant_engine. Our session covers real-world AI monitoring workflows w/ hallucination…
Most agent failures are silent: wrong tools, bad parameters, and subtle breakdowns in how agents use them. @jxnlco and myself break down how to trace, detect and fix tool-call errors in AI agents in our next @mavenhq course. We’ll go deep on evaluators like limbic-tool-use,…
It was a fantastic experience for @evilmartians to build the open source agent observability library for brilliant @QuotientAI and for all products that help people build agentic workflows. More apps are gonna need this, and Quotient with Martians are leading the way!
Agentic traces contain the complete story of an agent’s process: how it plans, reasons, chooses tools, and reacts when things go wrong. But making sense of those traces is slow and frustrating without the right UX. We’re changing that. AgentPrism is now live in @QuotientAI: a…
Agentic traces contain perfect information about an agent’s behavior with every plan, action, and retry. But that information gets lost in a sea of JSON. So we built AgentPrism: open source React components that turn traces into visual diagrams for debugging AI agents. You can…
Agentic traces contain the complete story of an agent’s process: how it plans, reasons, chooses tools, and reacts when things go wrong. But making sense of those traces is slow and frustrating without the right UX. We’re changing that. AgentPrism is now live in @QuotientAI: a…
We benchmarked how well open language models handle tool calls and found some clear patterns: - 1 in 6 calls use the wrong tool - 2–3% have parameter name mismatches - 1–2% pass values in the wrong format Most tool use issues come from unclear schemas, overlapping tool names, or…
AI agents are the security threat of the century: they already execute billions of actions per day, often with near-untethered access to the web, data, and APIs. In adversarial tests, it only takes tens of queries before many agents start going off-prompt, violating policy, or…
this explains why our kimi-k2 tool call evals on together looked off. crazy variance on that metric alone
How to ship reliable agents faster with @AgnoAgi + @QuotientAI Agno makes it super simple to build up an agent. But as soon as it gets to real users, cracks can show up: hallucinated answers, wrong tool calls, or irrelevant context. That’s where Quotient comes in. We provide…
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