Update README.md

README.md

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 ## Organization Description
 
-**[AutoBench](https://autobench.org/)** is the premier LLM evaluation and routing infra for the Agentic Era. We are dedicated to solving the LLM evaluation crisis by moving the industry beyond static, domain-rigid, easily gameable benchmarks and building the next generation LLM-based API routers for the agentic era. 
+**[AutoBench](https://autobench.org/)** is the premier LLM evaluation and routing infra for the Agentic Era. This is not just about LLM benchmarking, but real-time, AI-trained LLM routing for agents (delivering up to 90% inference cost savings).
 
-Pioneering the **"Collective-LLM-as-a-Judge"** methodology, AutoBench uses massive pools of LLMs to dynamically generate tasks, execute multi-turn workflows, and granularly evaluate performance across the AI ecosystem. Today, AutoBench provides fully automated, highly correlated, and strictly un-gameable benchmarking. Furthermore, we leverage the massive synthetic execution datasets generated by our benchmarks to train next-generation **Agentic LLM Routers**, helping agent developers and enterprises optimize for both absolute quality and unit economics.
+We are solving the LLM evaluation crisis by moving the industry beyond static, domain-rigid, and easily gameable benchmarks. AutoBench uses massive pools of LLMs to dynamically generate tasks, execute multi-turn workflows, and granularly evaluate LLM performance. Our benchmarks correlate 80-90% with industry standards, but they remain strictly un-gameable, unbiased, granular, flexible. At a fraction of the cost.
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+And that is just the beginning. We leverage the massive synthetic datasets generated by our benchmarks to train **next-gen Agentic LLM Routers**, helping agentic frameworks optimize for both quality and economics.
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+Our vision sees AutoBench as the essential, universal layer that will soon intermediate between all AI agents and underlying LLMs.
 
 ## The AutoBench Ecosystem
 
 ### 1. AutoBench Agentic (Latest Evolution)
 Current agentic benchmarks (like Gval-AA or Terminal-bench) are static, allowing models to simply "train to the test." AutoBench Agentic fundamentally changes this by dropping LLMs into dynamically generated, multi-turn **Agentic Virtual Environments**.
-* **Technical Complexity:** Our infrastructure deterministically injects business-flavored personas into a native Universal Intermediate Representation (UIR). We inject stateful "memory lines" of previous workflow failures, and force models to navigate complex native JSON `tools[]` arrays filled with randomly injected "distractor" tools.
+* **Technical Complexity:** Our infrastructure combines deterministic procedures and LLM generation to build complex business-flavored agentic task payloads via a native Universal Intermediate Representation (UIR). We inject stateful "memory lines" of previous workflow failures, and force models to navigate complex native JSON `tools[]` arrays filled with randomly injected "distractor" tools.
 * **10 Granular Task Types:** We evaluate true orchestration under pressure, measuring specific capabilities like *Adaptive Replanning*, *Parameter Complexity*, *Single Tool Call*, and *Failure Recovery*.
 * **Cost vs. Performance Tracking:** Tracks exact P99 Latency and strict USD/run costs to help developers define their efficiency frontier.
 
-### 2. Agentic LLM Routing
+### 2. Agentic LLM Routing (alpha)
 Benchmarking is just the first step. AutoBench uses the millions of execution traces and granular performance data generated by our runs to train dynamic **Agentic LLM Routers**. Instead of passive gateways or superficial semantic heuristic routers, AutoBench empowers active pipeline optimization—routing complex edge cases to frontier models and standard tasks to open-weight models, saving agent applications and enterprises up to 90% in API costs.
 
 ### 3. AutoBench 2.0 & Domain Benchmarks