Updated Dapr Agents docs to align with recent quickstart changes (#4987)

* Updated Dapr Agents docs to align with recent quickstart changes

Signed-off-by: Bilgin Ibryam <[email protected]>

* Update daprdocs/content/en/developing-ai/dapr-agents/dapr-agents-getting-started.md

Co-authored-by: Mark Fussell <[email protected]>
Signed-off-by: Bilgin Ibryam <[email protected]>

* Address feedback from PR

Signed-off-by: Bilgin Ibryam <[email protected]>

* Update daprdocs/content/en/developing-ai/dapr-agents/dapr-agents-getting-started.md

Co-authored-by: Marc Duiker <[email protected]>
Signed-off-by: Bilgin Ibryam <[email protected]>

---------

Signed-off-by: Bilgin Ibryam <[email protected]>
Co-authored-by: Mark Fussell <[email protected]>
Co-authored-by: Marc Duiker <[email protected]>

Author: 3 people

daprdocs/content/en/developing-ai/dapr-agents/_index.md

@@ -10,5 +10,5 @@ aliases:
 
 ###  What is Dapr Agents?
 
-Dapr Agents is a framework for building LLM-powered autonomous agentic applications using Dapr's distributed systems capabilities. It provides tools for creating AI agents that can execute tasks, make decisions, and collaborate through workflows, while leveraging Dapr's state management, messaging, and observability features for reliable execution at scale.
+Dapr Agents is a Python framework for building LLM-powered autonomous agentic applications using Dapr's distributed systems capabilities. It provides tools for creating AI agents that can execute durable tasks, make decisions, and collaborate through workflows, while leveraging Dapr's state management, messaging, and observability features for reliable execution at scale.
 

daprdocs/content/en/developing-ai/dapr-agents/dapr-agents-core-concepts.md

@@ -103,15 +103,15 @@ This example demonstrates creating a workflow-backed agent that runs autonomousl
 
 In Summary:
 
-| Agent Type      | Memory Type             | Execution                 | Interaction Mode             |
-|-----------------|-------------------------|---------------------------|------------------------------|
-| `Agent`         | In-memory or Persistent | Ephemeral                 | Synchronous / Conversational |
-| `Durable Agent` | In-memory or Persistent | Durable (Workflow-backed) | Asynchronous / Headless      |
+| Agent Type      | Memory Type             | Execution | Interaction Mode         |
+|-----------------|-------------------------|-----------|--------------------------|
+| `Agent`         | In-memory or Persistent | Ephemeral | Embedded                 |
+| `Durable Agent` | Persistent | Durable   | PubSub / HTTP / Embedded |
 
 
 - Regular `Agent`: Interaction is synchronous—you send conversational prompts and receive responses immediately. The conversation can be stored in memory or persisted, but the execution is ephemeral and does not survive restarts.
 
-- `DurableAgent` (Workflow-backed): Interaction is asynchronous—you trigger the agent once, and it runs autonomously in the background until completion. The conversation state can also be in memory or persisted, but the execution is durable and can resume across failures or restarts.
+- `DurableAgent` (Workflow-backed): Interaction is asynchronous—you trigger the agent once, and it runs autonomously in the background until completion. The conversation state and the execution are persisted  and can resume across failures or restarts.
 
 
 ## Core Agent Features
@@ -248,7 +248,7 @@ travel_planner = DurableAgent(
 | `ConversationDaprStateMemory` | Dapr State Store | ✅ | Query | Production |
 
 
-### Agent Services
+### Agent Runner
 
 `AgentRunner` wires DurableAgents into three complementary hosting modes: