diff --git a/python/packages/autogen-core/docs/src/user-guide/core-concepts/architecture.md b/python/packages/autogen-core/docs/src/user-guide/core-concepts/architecture.md
index df2c9af95a..e3580f385e 100644
--- a/python/packages/autogen-core/docs/src/user-guide/core-concepts/architecture.md
+++ b/python/packages/autogen-core/docs/src/user-guide/core-concepts/architecture.md
@@ -1,32 +1,31 @@
-# AGNext Architectures
+# Agent Runtime Environments
 
-AGNext is a framework for building multi-agent applications with AI agents.
-At the foundation level, it provides a runtime envionment to facilitate
-communication between agents, manage their identities and lifecycles,
+At the foundation level, the framework provides a _runtime envionment_, which facilitates
+communication between agents, manages their identities and lifecycles,
 and enforce security and privacy boundaries.
 
-AGNext supports two types of runtime architectures: standalone and distributed.
-Both architectures provide a common set of APIs for building multi-agent applications,
-so you can switch between them with no change to your agent implementation.
-Each architecture can also have different implementations.
+It supports two types of runtime environment: *standalone* and *distributed*.
+Both types provide a common set of APIs for building multi-agent applications,
+so you can switch between them without changing your agent implementation.
+Each type can also have multiple implementations.
 
 ## Standalone Agent Runtime
 
 Standalone runtime is suitable for single-process applications where all agents
 are implemented in the same programming language and running in the same process.
-In Python API, an example of standalone runtime is the {py:class}`~autogen_core.application.SingleThreadedAgentRuntime`.
+In the Python API, an example of standalone runtime is the {py:class}`~autogen_core.application.SingleThreadedAgentRuntime`.
 
-The following diagram shows the standalone runtime architecture of AGNext.
+The following diagram shows the standalone runtime in the framework.
 
-![AGNext Standalone Runtime](agnext-architecture-standalone.svg)
+![Standalone Runtime](agnext-architecture-standalone.svg)
 
-Agents communicate via messages through the runtime. The runtime manages
-the lifecycle of agents.
+Here, agents communicate via messages through the runtime, and the runtime manages
+the _lifecycle_ of agents.
 
 Developers can build agents quickly by using the provided components including
-routed agent, AI model clients, tools for AI models, code execution sandboxes,
+_routed agent_, AI model _clients_, tools for AI models, code execution sandboxes,
 model context stores, and more.
-They can also implement their own from scratch, or use other libraries.
+They can also implement their own agents from scratch, or use other libraries.
 
 ## Distributed Agent Runtime
 
@@ -34,17 +33,17 @@ Distributed runtime is suitable for multi-process applications where agents
 may be implemented in different programming languages and running on different
 machines.
 
-![AGNext Distributed Runtime](agnext-architecture-distributed.svg)
+![Distributed Runtime](agnext-architecture-distributed.svg)
 
 A distributed runtime, as shown in the diagram above,
-consists of a host servicer and multiple workers.
+consists of a _host servicer_ and multiple _workers_.
 The host servicer facilitates communication between agents across workers
 and maintains the states of connections.
-The workers run agents and communicate with the host servicer via gateways.
+The workers run agents and communicate with the host servicer via _gateways_.
 They advertise to the host servicer the agents they run and manage the agents' lifecycles.
 
 Agents work the same way as in the standalone runtime so that developers can
-switch between the two runtime architectures with no change to their agent implementation.
+switch between the two runtime types with no change to their agent implementation.
 
 
 ## API Layers