Update README.md

README.md

@@ -11,15 +11,18 @@ The Arch Family.
 
 The Arch family of LLMs are designed to fast and efficient LLMs for common scenarios in agentic application worloads - helping developers stay focused on higher level objectives
 of their agents. These scenario include fast agent routing and hand-off, tools calls for common agentic scenarios to improve speed, guadrails and input/output validation of prompts. 
-The Arch family of LLMs also serve as the core intelligence for [Arch Gateway](https://github.com/katanemo/archgw) (an AI-native proxy server for prompts). 
+The Arch family of LLMs also serve as the core intelligence for [Arch](https://github.com/katanemo/archgw) (an AI-native proxy server for agents). 
 
 Current
 
 1. Arch-Guard: a fast and efficient model for jailbreak attempts; improves performance over Meta Prompt Guard
-2. Arch-Function-Chat: A state-of-the-art (SOTA) function calling model also trained to chat - especially useful in scenarios where the model must clarify and refine inputs from the user,
-accurately deterime user's downstream intent, and manage decision making in long-form context and complext user interactions. Achieving performance on par with GPT-4. 
+2. Arch-Router: a compact, 1.5B model for LLM routing. A preference-aligned routing model that guides LLM selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing) – offering a practical mechanism to encode preferences in routing decision
+3. Arch-Agent: Designed to power sophisticated multi-step and multi-turn workflows, Arch-Agent excels at handling complex, multi-step tasks that require intelligent tool selection, adaptive planning, and seamless integration with external APIs and services.
 
 History
 
+2. Arch-Function-Chat: A state-of-the-art (SOTA) function calling model also trained to chat - especially useful in scenarios where the model must clarify and refine inputs from the user,
+accurately deterime user's downstream intent, and manage decision making in long-form context and complext user interactions. Achieving performance on par with GPT-4. 
 1. Arch-Function: State-of-the-art (SOTA) function calling models designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts. 
-Achieving performance on par with GPT-4. 
+Achieving performance on par with GPT-4.
+