Provably @GetProvably
Making agentic workflows hallucination and poison free powered by our verifiable databases and cryptography tools. provably.ai Switzerland Joined April 2024-
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anthropic.com/research/multi… This is fantastic read. 1. agents struggle to coordinate with each other especially for common resources. 2. they don't know how to detect when they are being lied to. And as usual Anthropic wants to solve it through more learning..
SourceryKit detected 100% of the covered deterministic grounding errors in this study. Next, we will test how much accuracy agents can recover through answer healing and targeted retries. Read the study: provably.ai/blogs/The-Agen…
We believe agents should capture evidence natively as they work. SourceryKit captures tool calls in a state of the art verifiable database, allowing agents to evaluate themselves and downstream agents to independently verify their answers.
We spent 9 weeks testing agent reliability during software tool use. Nearly 60% of claims accepted as correct could not be backed by evidence from recorded tool calls. The answer looked right. The evidence wasn't. 🧵
AI is going collaborative. In the next 6 months all we are gonna talk about is verifiability (for safety, security and accuracy), protocols and crazy new use cases.
The main thing in AI 2.0 - which Buzz is triggering with collaborative AI - is verifiability. Bolting on & releasing shortly. Then whenever another AI does a task it, it can prove it really did what claimed it did. It saves you needing access to the “actual” Terminal / end point.
Three months ago, we decided to work on one of the really big problems in AI: Agent reliability in tool calling.
SourceryKit is available now. It is a Python SDK for runtime verification of agent tool calls. You can verify the tool response while the workflow is still running, before the error spreads. Get started in minutes at provably.ai with pip install sourcerykit Check our launch blog: provably.ai/blogs/Runtime-…
Our view: The future is agents that can verify their own work, catch errors, and heal while the workflow is still running. As agents get smarter, this feedback loop will help them adapt and continuously learn from new workloads, powered by verifiable data from each other. It is time to take the training wheels off.
SourceryKit in early adoption: - detects 100% of agent tool-call errors. - improves accuracy by up to 50% in some models. - works across single-agent and multi-agent workflows. This is why we are building runtime verification for production agents.
All that scary marketing has blown up in their faces? - this means frontier models in rest of the world should get access to customers and data they wouldn’t normally get. - the lead they built could turn out to be temporary. Open source gets 80% of the tokens but only 20% of the revenue. OS struggles to access new data and improve as fast as frontier and closed source.
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of
Personal update: We found a way for agents to deliver more accurate and reliable results - at runtime. Here’s a 35 second sneak peek of an agent answering a user question, fetching data from backend systems, and verifying its own response at runtime.
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