diff --git a/src/backend/app.py b/src/backend/app.py
index 35e4e47af..2cf7d6a6b 100644
--- a/src/backend/app.py
+++ b/src/backend/app.py
@@ -3,9 +3,14 @@
 
 from contextlib import asynccontextmanager
 
+from common.config.app_config import config
+
+# Configure logging levels FIRST, before any logging calls
+logging.basicConfig(level=getattr(logging, config.AZURE_BASIC_LOGGING_LEVEL.upper(), logging.INFO))
+
 
 from azure.monitor.opentelemetry import configure_azure_monitor
-from common.config.app_config import config
+# from common.config.app_config import config
 from common.models.messages_af import UserLanguage
 
 # FastAPI imports
@@ -61,7 +66,7 @@ async def lifespan(app: FastAPI):
     )
 
 # Configure logging levels from environment variables
-logging.basicConfig(level=getattr(logging, config.AZURE_BASIC_LOGGING_LEVEL.upper(), logging.INFO))
+# logging.basicConfig(level=getattr(logging, config.AZURE_BASIC_LOGGING_LEVEL.upper(), logging.INFO))
 
 # Configure Azure package logging levels
 azure_level = getattr(logging, config.AZURE_PACKAGE_LOGGING_LEVEL.upper(), logging.WARNING)
@@ -73,6 +78,8 @@ async def lifespan(app: FastAPI):
 
 logging.getLogger("opentelemetry.sdk").setLevel(logging.ERROR)
 
+logging.getLogger("azure.core.pipeline.policies.http_logging_policy").setLevel(logging.WARNING)
+
 # Initialize the FastAPI app
 app = FastAPI(lifespan=lifespan)
 
diff --git a/src/backend/common/utils/utils_af.py b/src/backend/common/utils/utils_af.py
index 2d1dd794e..a22212144 100644
--- a/src/backend/common/utils/utils_af.py
+++ b/src/backend/common/utils/utils_af.py
@@ -88,9 +88,15 @@ async def create_RAI_agent(
     )
 
     model_deployment_name = config.AZURE_OPENAI_RAI_DEPLOYMENT_NAME
-    team.team_id = "rai_team"  # Use a fixed team ID for RAI agent
-    team.name = "RAI Team"
-    team.description = "Team responsible for Responsible AI checks"
+
+    # Create a copy to avoid mutating the caller's team config.
+    # The original team object is reused later (e.g., for orchestration init),
+    # so mutating it would corrupt the real team name/id.
+    rai_team = team.model_copy()
+    rai_team.team_id = "rai_team"
+    rai_team.name = "RAI Team"
+    rai_team.description = "Team responsible for Responsible AI checks"
+
     agent = FoundryAgentTemplate(
         agent_name=agent_name,
         agent_description=agent_description,
@@ -101,7 +107,7 @@ async def create_RAI_agent(
         project_endpoint=config.AZURE_AI_PROJECT_ENDPOINT,
         mcp_config=None,
         search_config=None,
-        team_config=team,
+        team_config=rai_team,
         memory_store=memory_store,
     )
 
diff --git a/src/backend/common/utils/utils_agents.py b/src/backend/common/utils/utils_agents.py
index 1e164f89c..c679f9f62 100644
--- a/src/backend/common/utils/utils_agents.py
+++ b/src/backend/common/utils/utils_agents.py
@@ -1,11 +1,6 @@
 
-import logging
 import secrets
 import string
-from typing import Optional
-
-from common.database.database_base import DatabaseBase
-from common.models.messages_af import TeamConfiguration
 
 
 def generate_assistant_id(prefix: str = "asst_", length: int = 24) -> str:
@@ -21,22 +16,3 @@ def generate_assistant_id(prefix: str = "asst_", length: int = 24) -> str:
     # cryptographically strong randomness
     random_part = "".join(secrets.choice(alphabet) for _ in range(length))
     return f"{prefix}{random_part}"
-
-
-async def get_database_team_agent_id(
-    memory_store: DatabaseBase, team_config: TeamConfiguration, agent_name: str
-) -> Optional[str]:
-    """Retrieve existing team agent from database, if any."""
-    agent_id = None
-    try:
-        currentAgent = await memory_store.get_team_agent(
-            team_id=team_config.team_id, agent_name=agent_name
-        )
-        if currentAgent and currentAgent.agent_foundry_id:
-            agent_id = currentAgent.agent_foundry_id
-
-    except (
-        Exception
-    ) as ex:  # Consider narrowing this to specific exceptions if possible
-        logging.error("Failed to initialize Get database team agent: %s", ex)
-    return agent_id
diff --git a/src/backend/pyproject.toml b/src/backend/pyproject.toml
index be05a724f..06dcf2ba6 100644
--- a/src/backend/pyproject.toml
+++ b/src/backend/pyproject.toml
@@ -7,38 +7,37 @@ requires-python = ">=3.11"
 dependencies = [
     "azure-ai-evaluation==1.11.0",
     "azure-ai-inference==1.0.0b9",
-    "azure-ai-projects==1.0.0",
-    "azure-ai-agents==1.2.0b5",
+    "azure-ai-projects==2.0.0b3",
     "azure-cosmos==4.9.0",
     "azure-identity==1.24.0",
     "azure-monitor-events-extension==0.1.0",
-    "azure-monitor-opentelemetry==1.7.0",
+    "azure-monitor-opentelemetry==1.8.5",
     "azure-search-documents==11.5.3",
     "fastapi==0.116.1",
-    "openai==1.105.0",
-    "opentelemetry-api==1.36.0",
-    "opentelemetry-exporter-otlp-proto-grpc==1.36.0",
-    "opentelemetry-exporter-otlp-proto-http==1.36.0",
-    "opentelemetry-instrumentation-fastapi==0.57b0",
+    "openai==2.16.0",
+    "opentelemetry-api==1.39.0",
+    "opentelemetry-exporter-otlp-proto-grpc==1.39.0",
+    "opentelemetry-exporter-otlp-proto-http==1.39.0",
+    "opentelemetry-instrumentation-fastapi==0.60b0",
     "opentelemetry-instrumentation-openai==0.46.2",
-    "opentelemetry-sdk==1.36.0",
+    "opentelemetry-sdk==1.39.0",
     "pytest==8.4.1",
     "pytest-asyncio==0.24.0",
     "pytest-cov==5.0.0",
     "python-dotenv==1.1.1",
     "python-multipart==0.0.22",
-    "semantic-kernel==1.39.4",
     "uvicorn==0.35.0",
     "pylint-pydantic==0.3.5",
     "pexpect==4.9.0",
     "mcp==1.26.0",
     "werkzeug==3.1.5",
     "azure-core==1.38.0",
-    "agent-framework>=1.0.0b251105",
+    "agent-framework-azure-ai==1.0.0b260130",
+    "agent-framework-core==1.0.0b260130",
     "urllib3==2.6.3",
     "protobuf==5.29.6",
     "cryptography==46.0.5",
     "aiohttp==3.13.3",
     "pyasn1==0.6.2",
     "nltk==3.9.3",
-]
+]
\ No newline at end of file
diff --git a/src/backend/uv.lock b/src/backend/uv.lock
index 1a678fb49..e9065f1c6 100644
--- a/src/backend/uv.lock
+++ b/src/backend/uv.lock
@@ -7,101 +7,9 @@ resolution-markers = [
     "python_full_version < '3.12'",
 ]
 
-[[package]]
-name = "a2a-sdk"
-version = "0.3.11"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "google-api-core" },
-    { name = "httpx" },
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-    { name = "protobuf" },
-    { name = "pydantic" },
-]
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-]
-
-[[package]]
-name = "ag-ui-protocol"
-version = "0.1.10"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "pydantic" },
-]
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-
-[[package]]
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-dependencies = [
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-    { name = "agent-framework-ag-ui" },
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-    { name = "agent-framework-azure-ai" },
-    { name = "agent-framework-chatkit" },
-    { name = "agent-framework-copilotstudio" },
-    { name = "agent-framework-core" },
-    { name = "agent-framework-devui" },
-    { name = "agent-framework-lab" },
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 [[package]]
 name = "agent-framework-azure-ai"
-version = "1.0.0b251108"
+version = "1.0.0b260130"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -109,43 +17,20 @@ dependencies = [
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     { name = "azure-ai-projects" },
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 [[package]]
 name = "agent-framework-core"
-version = "1.0.0b251108"
+version = "1.0.0b260130"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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     { name = "mcp", extra = ["ws"] },
     { name = "openai" },
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-    { name = "opentelemetry-exporter-otlp-proto-grpc" },
     { name = "opentelemetry-sdk" },
     { name = "opentelemetry-semantic-conventions-ai" },
     { name = "packaging" },
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-
 [[package]]
 name = "websockets"
 version = "15.0.1"
diff --git a/src/backend/v4/api/router.py b/src/backend/v4/api/router.py
index 43f3f9f2f..d9a8e7c10 100644
--- a/src/backend/v4/api/router.py
+++ b/src/backend/v4/api/router.py
@@ -303,6 +303,17 @@ async def process_request(
         )
         await memory_store.add_plan(plan)
 
+        # Ensure orchestration is initialized before running
+        # Force rebuild for each new task since Magentic workflows cannot be reused after completion
+        team_service = TeamService(memory_store)
+        await OrchestrationManager.get_current_or_new_orchestration(
+            user_id=user_id,
+            team_config=team,
+            team_switched=False,
+            team_service=team_service,
+            force_rebuild=True,  # Always rebuild workflow for new tasks
+        )
+
         track_event_if_configured(
             "PlanCreated",
             {
diff --git a/src/backend/v4/callbacks/response_handlers.py b/src/backend/v4/callbacks/response_handlers.py
index 297c8814b..0a817ef94 100644
--- a/src/backend/v4/callbacks/response_handlers.py
+++ b/src/backend/v4/callbacks/response_handlers.py
@@ -9,9 +9,6 @@
 from typing import Any
 
 from agent_framework import ChatMessage
-# Removed: from agent_framework._content import FunctionCallContent  (does not exist)
-
-from agent_framework._workflows._magentic import AgentRunResponseUpdate  # Streaming update type from workflows
 
 from v4.config.settings import connection_config
 from v4.models.messages import (
@@ -111,26 +108,31 @@ def agent_response_callback(
 
 async def streaming_agent_response_callback(
     agent_id: str,
-    update: AgentRunResponseUpdate,
+    update,  # Streaming update object (e.g. AgentResponseUpdate, ChatMessage)
     is_final: bool,
     user_id: str | None = None,
 ) -> None:
     """
-    Streaming callback for incremental agent output (AgentRunResponseUpdate).
+    Streaming callback for incremental agent output.
     """
     if not user_id:
         return
 
     try:
+        # Handle various streaming update object shapes
         chunk_text = getattr(update, "text", None)
-        if not chunk_text:
-            contents = getattr(update, "contents", []) or []
-            collected = []
-            for item in contents:
-                txt = getattr(item, "text", None)
-                if txt:
-                    collected.append(str(txt))
-            chunk_text = "".join(collected) if collected else ""
+
+        # If text is None, don't fall back to str(update) as that would show object repr
+        # Just skip if there's no actual text content
+        if chunk_text is None:
+            # Check if update is a ChatMessage
+            if isinstance(update, ChatMessage):
+                chunk_text = update.text or ""
+            elif hasattr(update, "content"):
+                chunk_text = str(update.content) if update.content else ""
+            else:
+                # Skip if no text content available
+                return
 
         cleaned = clean_citations(chunk_text or "")
 
diff --git a/src/backend/v4/magentic_agents/common/lifecycle.py b/src/backend/v4/magentic_agents/common/lifecycle.py
index bd9382ff2..b38e31eed 100644
--- a/src/backend/v4/magentic_agents/common/lifecycle.py
+++ b/src/backend/v4/magentic_agents/common/lifecycle.py
@@ -10,15 +10,14 @@
     MCPStreamableHTTPTool,
 )
 
-# from agent_framework.azure import AzureAIAgentClient
-from agent_framework_azure_ai import AzureAIAgentClient
+# from agent_framework.azure import AzureAIClient
+from agent_framework_azure_ai import AzureAIClient
 from azure.ai.agents.aio import AgentsClient
 from azure.identity.aio import DefaultAzureCredential
 from common.database.database_base import DatabaseBase
-from common.models.messages_af import CurrentTeamAgent, TeamConfiguration
+from common.models.messages_af import TeamConfiguration
 from common.utils.utils_agents import (
     generate_assistant_id,
-    get_database_team_agent_id,
 )
 from v4.common.services.team_service import TeamService
 from v4.config.agent_registry import agent_registry
@@ -51,7 +50,7 @@ def __init__(
         self._agent: ChatAgent | None = None
         self.team_service: TeamService | None = team_service
         self.team_config: TeamConfiguration | None = team_config
-        self.client: Optional[AzureAIAgentClient] = None
+        self.client: Optional[AgentsClient] = None
         self.project_endpoint = project_endpoint
         self.creds: Optional[DefaultAzureCredential] = None
         self.memory_store: Optional[DatabaseBase] = memory_store
@@ -105,7 +104,7 @@ async def close(self) -> None:
             # Attempt to close the underlying agent/client if it exposes close()
             if self._agent and hasattr(self._agent, "close"):
                 try:
-                    await self._agent.close()  # AzureAIAgentClient has async close
+                    await self._agent.close()  # AzureAIClient has async close
                 except Exception as exc:
                     # Best-effort close; log failure but continue teardown
                     self.logger.warning(
@@ -148,179 +147,40 @@ async def _after_open(self) -> None:
         """Subclasses must build self._agent here."""
         raise NotImplementedError
 
-    def get_chat_client(self, chat_client) -> AzureAIAgentClient:
-        """Return the underlying ChatClientProtocol (AzureAIAgentClient)."""
-        if chat_client:
-            return chat_client
+    def get_chat_client(self) -> AzureAIClient:
+        """Return the underlying ChatClientProtocol (AzureAIClient).
+
+        Uses agent_name with use_latest_version=True to get the latest agent version.
+        Agent reuse is handled automatically by the SDK via agent_name.
+        """
         if (
             self._agent
             and self._agent.chat_client
-            and self._agent.chat_client.agent_id is not None
         ):
             return self._agent.chat_client  # type: ignore
-        chat_client = AzureAIAgentClient(
+        chat_client = AzureAIClient(
             project_endpoint=self.project_endpoint,
+            agent_name=self.agent_name,
             model_deployment_name=self.model_deployment_name,
-            async_credential=self.creds,
+            credential=self.creds,
+            use_latest_version=True,
         )
         self.logger.info(
-            "Created new AzureAIAgentClient for  get chat client",
-            extra={"agent_id": chat_client.agent_id},
+            "Created new AzureAIClient (agent_name=%s, use_latest_version=True)",
+            self.agent_name,
         )
         return chat_client
 
-    async def resolve_agent_id(self, agent_id: str) -> Optional[str]:
-        """Resolve agent ID via Projects SDK first (for RAI agents), fallback to AgentsClient.
+    def get_agent_id(self) -> str:
+        """Generate a local agent ID for the ChatAgent wrapper.
 
-        Args:
-            agent_id: The agent ID to resolve
-
-        Returns:
-            The resolved agent ID if found, None otherwise
+        The new AzureAIClient identifies agents by name (not ID) on the server side.
+        This ID is only used locally for the ChatAgent wrapper instance.
         """
-        # Try Projects SDK first (RAI agents were created via project_client)
-        try:
-            if self.project_client:
-                agent = await self.project_client.agents.get_agent(agent_id)
-                if agent and agent.id:
-                    self.logger.info(
-                        "RAI.AgentReuseSuccess: Resolved agent via Projects SDK (id=%s)",
-                        agent.id,
-                    )
-                    return agent.id
-        except Exception as ex:
-            self.logger.warning(
-                "RAI.AgentReuseMiss: Projects SDK get_agent failed (reason=ProjectsGetFailed, id=%s): %s",
-                agent_id,
-                ex,
-            )
-
-        # Fallback via AgentsClient (endpoint)
-        try:
-            if self.client:
-                agent = await self.client.get_agent(agent_id=agent_id)
-                if agent and agent.id:
-                    self.logger.info(
-                        "RAI.AgentReuseSuccess: Resolved agent via AgentsClient (id=%s)",
-                        agent.id,
-                    )
-                    return agent.id
-        except Exception as ex:
-            self.logger.warning(
-                "RAI.AgentReuseMiss: AgentsClient get_agent failed (reason=EndpointGetFailed, id=%s): %s",
-                agent_id,
-                ex,
-            )
-
-        self.logger.error(
-            "RAI.AgentReuseMiss: Agent ID not resolvable via any client (reason=ClientMismatch, id=%s)",
-            agent_id,
-        )
-        return None
-
-    def get_agent_id(self, chat_client) -> str:
-        """Return the underlying agent ID."""
-        if chat_client and chat_client.agent_id is not None:
-            return chat_client.agent_id
-        if (
-            self._agent
-            and self._agent.chat_client
-            and self._agent.chat_client.agent_id is not None
-        ):
-            return self._agent.chat_client.agent_id  # type: ignore
         id = generate_assistant_id()
         self.logger.info("Generated new agent ID: %s", id)
         return id
 
-    async def get_database_team_agent(self) -> Optional[AzureAIAgentClient]:
-        """Retrieve existing team agent from database, if any."""
-        chat_client = None
-        try:
-            agent_id = await get_database_team_agent_id(
-                self.memory_store, self.team_config, self.agent_name
-            )
-
-            if not agent_id:
-                self.logger.info(
-                    "RAI reuse: no stored agent id (agent_name=%s)", self.agent_name
-                )
-                return None
-
-            # Use resolve_agent_id to try Projects SDK first, then AgentsClient
-            resolved = await self.resolve_agent_id(agent_id)
-            if not resolved:
-                self.logger.error(
-                    "RAI.AgentReuseMiss: stored id %s not resolvable (agent_name=%s)",
-                    agent_id,
-                    self.agent_name,
-                )
-                return None
-
-            # Create client with resolved ID, preferring project_client for RAI agents
-            if self.agent_name == "RAIAgent" and self.project_client:
-                chat_client = AzureAIAgentClient(
-                    project_client=self.project_client,
-                    agent_id=resolved,
-                    async_credential=self.creds,
-                )
-                self.logger.info(
-                    "RAI.AgentReuseSuccess: Created AzureAIAgentClient via Projects SDK (id=%s)",
-                    resolved,
-                )
-            else:
-                chat_client = AzureAIAgentClient(
-                    project_endpoint=self.project_endpoint,
-                    agent_id=resolved,
-                    model_deployment_name=self.model_deployment_name,
-                    async_credential=self.creds,
-                )
-                self.logger.info(
-                    "Created AzureAIAgentClient via endpoint (id=%s)", resolved
-                )
-
-        except Exception as ex:
-            self.logger.error(
-                "Failed to initialize Get database team agent (agent_name=%s): %s",
-                self.agent_name,
-                ex,
-            )
-        return chat_client
-
-    async def save_database_team_agent(self) -> None:
-        """Save current team agent to database (only if truly new or changed)."""
-        try:
-            if self._agent.id is None:
-                self.logger.error("Cannot save database team agent: agent_id is None")
-                return
-
-            # Check if stored ID matches current ID
-            stored_id = await get_database_team_agent_id(
-                self.memory_store, self.team_config, self.agent_name
-            )
-            if stored_id == self._agent.chat_client.agent_id:
-                self.logger.info(
-                    "RAI reuse: id unchanged (id=%s); skip save.", self._agent.id
-                )
-                return
-
-            currentAgent = CurrentTeamAgent(
-                team_id=self.team_config.team_id,
-                team_name=self.team_config.name,
-                agent_name=self.agent_name,
-                agent_foundry_id=self._agent.chat_client.agent_id,
-                agent_description=self.agent_description,
-                agent_instructions=self.agent_instructions,
-            )
-            await self.memory_store.add_team_agent(currentAgent)
-            self.logger.info(
-                "Saved team agent to database (agent_name=%s, id=%s)",
-                self.agent_name,
-                self._agent.id,
-            )
-
-        except Exception as ex:
-            self.logger.error("Failed to save database: %s", ex)
-
     async def _prepare_mcp_tool(self) -> None:
         """Translate MCPConfig to a HostedMCPTool (agent_framework construct)."""
         if not self.mcp_cfg:
@@ -339,10 +199,10 @@ async def _prepare_mcp_tool(self) -> None:
 
 class AzureAgentBase(MCPEnabledBase):
     """
-    Extends MCPEnabledBase with Azure credential + AzureAIAgentClient contexts.
+    Extends MCPEnabledBase with Azure credential + AzureAIClient contexts.
     Subclasses:
       - create or attach an Azure AI Agent definition
-      - instantiate an AzureAIAgentClient and assign to self._agent
+      - instantiate an AzureAIClient and assign to self._agent
       - optionally register themselves via agent_registry
     """
 
diff --git a/src/backend/v4/magentic_agents/foundry_agent.py b/src/backend/v4/magentic_agents/foundry_agent.py
index df6221699..38fd0cc6b 100644
--- a/src/backend/v4/magentic_agents/foundry_agent.py
+++ b/src/backend/v4/magentic_agents/foundry_agent.py
@@ -6,8 +6,13 @@
 from agent_framework import (ChatAgent, ChatMessage, HostedCodeInterpreterTool,
                              Role)
 from agent_framework_azure_ai import \
-    AzureAIAgentClient  # Provided by agent_framework
-from azure.ai.projects.models import ConnectionType
+    AzureAIClient  # Provided by agent_framework
+from azure.ai.projects.models import (
+    PromptAgentDefinition,
+    AzureAISearchAgentTool,
+    AzureAISearchToolResource,
+    AISearchIndexResource,
+)
 from common.config.app_config import config
 from common.database.database_base import DatabaseBase
 from common.models.messages_af import TeamConfiguration
@@ -76,7 +81,6 @@ def _is_azure_search_requested(self) -> bool:
         if not self.search:
             return False
         # Minimal heuristic: presence of required attributes
-
         has_index = hasattr(self.search, "index_name") and bool(self.search.index_name)
         if has_index:
             self.logger.info(
@@ -111,30 +115,37 @@ async def _collect_tools(self) -> List:
     # -------------------------
     # Azure Search helper
     # -------------------------
-    async def _create_azure_search_enabled_client(self, chatClient=None) -> Optional[AzureAIAgentClient]:
+    async def _create_azure_search_enabled_client(self) -> Optional[AzureAIClient]:
         """
-        Create a server-side Azure AI agent with Azure AI Search raw tool.
+        Create a server-side Azure AI agent with Azure AI Search tool using create_version.
+
+        This uses the AIProjectClient.agents.create_version() approach with:
+        - PromptAgentDefinition for agent configuration
+        - AzureAISearchAgentTool with AzureAISearchToolResource for search capability
+        - AISearchIndexResource for index configuration with project_connection_id
 
         Requirements:
-          - An Azure AI Project Connection (type=AZURE_AI_SEARCH) that contains either:
-              a) API key + endpoint, OR
-              b) Managed Identity (RBAC enabled on the Search service with Search Service Contributor + Search Index Data Reader).
+          - An Azure AI Project Connection for Azure AI Search
           - search_config.index_name must exist in the Search service.
-
+          - search_config.connection_name should match the AI Project connection name
 
         Returns:
-            AzureAIAgentClient | None
+            AzureAIClient | None
         """
-        if chatClient:
-            return chatClient
-
         if not self.search:
             self.logger.error("Search configuration missing.")
             return None
 
-        desired_connection_name = getattr(self.search, "connection_name", None)
+        # Get connection name - this is used as project_connection_id in create_version
+        connection_name = getattr(self.search, "connection_name", None)
+        if not connection_name:
+            # Fallback to environment variable
+            connection_name = config.AZURE_AI_SEARCH_CONNECTION_NAME
+            self.logger.info("Using connection_name from environment: %s", connection_name)
+
         index_name = getattr(self.search, "index_name", "")
         query_type = getattr(self.search, "search_query_type", "simple")
+        top_k = getattr(self.search, "top_k", 5)
 
         if not index_name:
             self.logger.error(
@@ -142,79 +153,74 @@ async def _create_azure_search_enabled_client(self, chatClient=None) -> Optional
             )
             return None
 
-        resolved_connection_id = None
-
-        try:
-            async for connection in self.project_client.connections.list():
-                if connection.type == ConnectionType.AZURE_AI_SEARCH:
-
-                    if (
-                        desired_connection_name
-                        and connection.name == desired_connection_name
-                    ):
-                        resolved_connection_id = connection.id
-                        break
-                    # Fallback: if no specific connection requested and none resolved yet, take the first
-                    if not desired_connection_name and not resolved_connection_id:
-                        resolved_connection_id = connection.id
-                        # Do not break yet; we log but allow chance to find a name match later. If not, this stays.
-
-            if not resolved_connection_id:
-                self.logger.error(
-                    "No Azure AI Search connection resolved. " "connection_name=%s",
-                    desired_connection_name,
-                )
-            #  return None
-
-            self.logger.info(
-                "Using Azure AI Search connection (id=%s, requested_name=%s).",
-                resolved_connection_id,
-                desired_connection_name,
+        if not connection_name:
+            self.logger.error(
+                "connection_name not provided; aborting Azure Search path."
             )
-        except Exception as ex:
-            self.logger.error("Failed to enumerate connections: %s", ex)
             return None
 
-        # Create agent with raw tool
+        self.logger.info(
+            "Creating Azure AI Search agent with create_version: connection_name=%s, index=%s, query_type=%s, top_k=%s",
+            connection_name,
+            index_name,
+            query_type,
+            top_k,
+        )
+
+        # Create agent using create_version with PromptAgentDefinition and AzureAISearchAgentTool
+        # This approach matches the Knowledge Mining Solution Accelerator pattern
         try:
-            azure_agent = await self.client.create_agent(
-                model=self.model_deployment_name,
-                name=self.agent_name,
-                instructions=(
-                    f"{self.agent_instructions} "
-                    "Always use the Azure AI Search tool and configured index for knowledge retrieval."
+            enhanced_instructions = (
+                f"{self.agent_instructions} "
+                "Always use the Azure AI Search tool and configured index for knowledge retrieval."
+            )
+
+            azure_agent = await self.project_client.agents.create_version(
+                agent_name=self.agent_name,  # Use original name
+                definition=PromptAgentDefinition(
+                    model=self.model_deployment_name,
+                    instructions=enhanced_instructions,
+                    tools=[
+                        AzureAISearchAgentTool(
+                            azure_ai_search=AzureAISearchToolResource(
+                                indexes=[
+                                    AISearchIndexResource(
+                                        project_connection_id=connection_name,
+                                        index_name=index_name,
+                                        query_type=query_type,
+                                        top_k=top_k,
+                                    )
+                                ]
+                            )
+                        )
+                    ],
                 ),
-                tools=[{"type": "azure_ai_search"}],
-                tool_resources={
-                    "azure_ai_search": {
-                        "indexes": [
-                            {
-                                "index_connection_id": resolved_connection_id,
-                                "index_name": index_name,
-                                "query_type": query_type,
-                            }
-                        ]
-                    }
-                },
             )
+
             self._azure_server_agent_id = azure_agent.id
+
             self.logger.info(
-                "Created Azure server agent with Azure AI Search tool (agent_id=%s, index=%s, query_type=%s).",
+                "Created Azure AI Search agent via create_version (name=%s, id=%s, version=%s).",
+                azure_agent.name,
                 azure_agent.id,
-                index_name,
-                query_type,
+                azure_agent.version,
             )
 
-            chat_client = AzureAIAgentClient(
-                project_client=self.project_client,
-                agent_id=azure_agent.id,
-                async_credential=self.creds,
+            # Wrap in AzureAIClient using agent_name and agent_version (NOT agent_id)
+            # AzureAIClient constructor: agent_name, agent_version, project_endpoint, credential
+            chat_client = AzureAIClient(
+                project_endpoint=self.project_endpoint,
+                agent_name=azure_agent.name,
+                agent_version=azure_agent.version,  # Use the specific version we just created
+                model_deployment_name=self.model_deployment_name,
+                credential=self.creds,
             )
             return chat_client
+
         except Exception as ex:
             self.logger.error(
-                "Failed to create Azure Search enabled agent (connection_id=%s, index=%s): %s",
-                resolved_connection_id,
+                "Failed to create Azure Search enabled agent via create_version (connection=%s, index=%s): %s",
+                connection_name,
                 index_name,
                 ex,
             )
@@ -233,14 +239,14 @@ async def _after_open(self) -> None:
             temp = 0.1
 
         try:
-            chatClient = await self.get_database_team_agent()
-
             if self._use_azure_search:
                 # Azure Search mode (skip MCP + Code Interpreter due to incompatibility)
                 self.logger.info(
-                    "Initializing agent in Azure AI Search mode (exclusive)."
+                    "Initializing agent '%s' in Azure AI Search mode (exclusive) with index=%s.",
+                    self.agent_name,
+                    getattr(self.search, "index_name", "N/A") if self.search else "N/A"
                 )
-                chat_client = await self._create_azure_search_enabled_client(chatClient)
+                chat_client = await self._create_azure_search_enabled_client()
                 if not chat_client:
                     raise RuntimeError(
                         "Azure AI Search mode requested but setup failed."
@@ -248,22 +254,23 @@ async def _after_open(self) -> None:
 
                 # In Azure Search raw tool path, tools/tool_choice are handled server-side.
                 self._agent = ChatAgent(
-                    id=self.get_agent_id(chat_client),
-                    chat_client=self.get_chat_client(chat_client),
+                    id=self.get_agent_id(),
+                    chat_client=chat_client,
                     instructions=self.agent_instructions,
                     name=self.agent_name,
                     description=self.agent_description,
                     tool_choice="required",  # Force usage
                     temperature=temp,
                     model_id=self.model_deployment_name,
+                    default_options={"store": False},  # Client-managed conversation to avoid stale tool call IDs across rounds
                 )
             else:
-                # use MCP path
+                # MCP path (also used by RAI agent which has no tools)
                 self.logger.info("Initializing agent in MCP mode.")
                 tools = await self._collect_tools()
                 self._agent = ChatAgent(
-                    id=self.get_agent_id(chatClient),
-                    chat_client=self.get_chat_client(chatClient),
+                    id=self.get_agent_id(),
+                    chat_client=self.get_chat_client(),
                     instructions=self.agent_instructions,
                     name=self.agent_name,
                     description=self.agent_description,
@@ -271,6 +278,7 @@ async def _after_open(self) -> None:
                     tool_choice="auto" if tools else "none",
                     temperature=temp,
                     model_id=self.model_deployment_name,
+                    default_options={"store": False},  # Client-managed conversation to avoid stale tool call IDs across rounds
                 )
             self.logger.info("Initialized ChatAgent '%s'", self.agent_name)
 
@@ -299,12 +307,7 @@ async def invoke(self, prompt: str):
 
         messages = [ChatMessage(role=Role.USER, text=prompt)]
 
-        agent_saved = False
         async for update in self._agent.run_stream(messages):
-            # Save agent ID only once on first update (agent ID won't change during streaming)
-            if not agent_saved and self._agent.chat_client.agent_id:
-                await self.save_database_team_agent()
-                agent_saved = True
             yield update
 
     # -------------------------
diff --git a/src/backend/v4/magentic_agents/models/agent_models.py b/src/backend/v4/magentic_agents/models/agent_models.py
index 5c6a3f2f1..4e10270fa 100644
--- a/src/backend/v4/magentic_agents/models/agent_models.py
+++ b/src/backend/v4/magentic_agents/models/agent_models.py
@@ -43,6 +43,8 @@ class SearchConfig:
     connection_name: str | None = None
     endpoint: str | None = None
     index_name: str | None = None
+    search_query_type: str = "simple"  # Options: "simple", "vector_simple", "vector", "semantic", "hybrid"
+    top_k: int = 5  # Number of results to return
 
     @classmethod
     def from_env(cls, index_name: str) -> "SearchConfig":
@@ -58,5 +60,7 @@ def from_env(cls, index_name: str) -> "SearchConfig":
         return cls(
             connection_name=connection_name,
             endpoint=endpoint,
-            index_name=index_name
+            index_name=index_name,
+            search_query_type="simple",  # Use simple query type (keyword search)
+            top_k=5
         )
diff --git a/src/backend/v4/magentic_agents/proxy_agent.py b/src/backend/v4/magentic_agents/proxy_agent.py
index a6ba9d3c4..79a84492b 100644
--- a/src/backend/v4/magentic_agents/proxy_agent.py
+++ b/src/backend/v4/magentic_agents/proxy_agent.py
@@ -16,13 +16,12 @@
 from typing import Any, AsyncIterable
 
 from agent_framework import (
-    AgentRunResponse,
-    AgentRunResponseUpdate,
+    AgentResponse,
+    AgentResponseUpdate,
     BaseAgent,
     ChatMessage,
     Role,
-    TextContent,
-    UsageContent,
+    Content,
     UsageDetails,
     AgentThread,
 )
@@ -88,7 +87,7 @@ async def run(
         *,
         thread: AgentThread | None = None,
         **kwargs: Any,
-    ) -> AgentRunResponse:
+    ) -> AgentResponse:
         """
         Get complete clarification response (non-streaming).
 
@@ -98,7 +97,7 @@ async def run(
             kwargs: Additional keyword arguments
 
         Returns:
-            AgentRunResponse with the clarification
+            AgentResponse with the clarification
         """
         # Collect all streaming updates
         response_messages: list[ChatMessage] = []
@@ -113,7 +112,7 @@ async def run(
                     )
                 )
 
-        return AgentRunResponse(
+        return AgentResponse(
             messages=response_messages,
             response_id=response_id,
         )
@@ -124,7 +123,7 @@ def run_stream(
         *,
         thread: AgentThread | None = None,
         **kwargs: Any,
-    ) -> AsyncIterable[AgentRunResponseUpdate]:
+    ) -> AsyncIterable[AgentResponseUpdate]:
         """
         Stream clarification process with human interaction.
 
@@ -143,7 +142,7 @@ async def _invoke_stream_internal(
         messages: str | ChatMessage | list[str] | list[ChatMessage] | None,
         thread: AgentThread | None,
         **kwargs: Any,
-    ) -> AsyncIterable[AgentRunResponseUpdate]:
+    ) -> AsyncIterable[AgentResponseUpdate]:
         """
         Internal streaming implementation.
 
@@ -205,9 +204,10 @@ async def _invoke_stream_internal(
         message_id = str(uuid.uuid4())
 
         # Yield final assistant text update with explicit text content
-        text_update = AgentRunResponseUpdate(
+        # New API: use Content.from_text() or pass text directly to AgentResponseUpdate
+        text_update = AgentResponseUpdate(
             role=Role.ASSISTANT,
-            contents=[TextContent(text=synthetic_reply)],
+            text=synthetic_reply,  # New API accepts text directly
             author_name=self.name,
             response_id=response_id,
             message_id=message_id,
@@ -218,10 +218,10 @@ async def _invoke_stream_internal(
 
         # Yield synthetic usage update for consistency
         # Use same message_id to indicate this is part of the same message
-        usage_update = AgentRunResponseUpdate(
+        usage_update = AgentResponseUpdate(
             role=Role.ASSISTANT,
             contents=[
-                UsageContent(
+                Content.from_usage(
                     UsageDetails(
                         input_token_count=len(message_text.split()),
                         output_token_count=len(synthetic_reply.split()),
diff --git a/src/backend/v4/orchestration/human_approval_manager.py b/src/backend/v4/orchestration/human_approval_manager.py
index 2a3ab5be7..7f33c9ac4 100644
--- a/src/backend/v4/orchestration/human_approval_manager.py
+++ b/src/backend/v4/orchestration/human_approval_manager.py
@@ -34,11 +34,12 @@ class HumanApprovalMagenticManager(StandardMagenticManager):
     magentic_plan: Optional[MPlan] = None
     current_user_id: str  # populated in __init__
 
-    def __init__(self, user_id: str, *args, **kwargs):
+    def __init__(self, user_id: str, agent, *args, **kwargs):
         """
         Initialize the HumanApprovalMagenticManager.
         Args:
             user_id: ID of the user to associate with this orchestration instance.
+            agent: The manager ChatAgent for orchestration (required by new API).
             *args: Additional positional arguments for the parent StandardMagenticManager.
             **kwargs: Additional keyword arguments for the parent StandardMagenticManager.
         """
@@ -54,6 +55,9 @@ def __init__(self, user_id: str, *args, **kwargs):
 to be taken. If a step involves multiple actions, separate them into distinct steps with an agent included in each step.
 If the step is taken by an agent that is not part of the team, such as the MagenticManager, please always list the MagenticManager as the agent for that step. At any time, if more information is needed from the user, use the ProxyAgent to request this information.
 
+CRITICAL: Each agent should only be called ONCE to perform their task. Do NOT call the same agent multiple times.
+After an agent has provided their response, move on to the next agent in the plan.
+
 Here is an example of a well-structured plan:
 - **EnhancedResearchAgent** to gather authoritative data on the latest industry trends and best practices in employee onboarding
 - **EnhancedResearchAgent** to gather authoritative data on Innovative onboarding techniques that enhance new hire engagement and retention.
@@ -61,6 +65,13 @@ def __init__(self, user_id: str, *args, **kwargs):
 - **DocumentCreationAgent** to draft a comprehensive onboarding plan that includes a checklist of resources and materials needed for effective onboarding.
 - **ProxyAgent** to review the drafted onboarding plan for clarity and completeness.
 - **MagenticManager** to finalize the onboarding plan and prepare it for presentation to stakeholders.
+"""
+
+        # Add progress ledger prompt to prevent re-calling agents
+        progress_append = """
+CRITICAL RULE: DO NOT call the same agent more than once unless absolutely necessary.
+If an agent has already provided a response, consider their task COMPLETE and move to the next agent.
+Only re-call an agent if their previous response was explicitly an error or failure.
 """
 
         final_append = """
@@ -75,8 +86,13 @@ def __init__(self, user_id: str, *args, **kwargs):
         )
         kwargs["final_answer_prompt"] = ORCHESTRATOR_FINAL_ANSWER_PROMPT + final_append
 
+        # Override progress ledger prompt to discourage re-calling agents
+        from agent_framework._workflows._magentic import ORCHESTRATOR_PROGRESS_LEDGER_PROMPT
+        kwargs["progress_ledger_prompt"] = ORCHESTRATOR_PROGRESS_LEDGER_PROMPT + progress_append
+
         self.current_user_id = user_id
-        super().__init__(*args, **kwargs)
+        # New API: StandardMagenticManager takes agent as first positional argument
+        super().__init__(agent, *args, **kwargs)
 
     async def plan(self, magentic_context: MagenticContext) -> Any:
         """
diff --git a/src/backend/v4/orchestration/orchestration_manager.py b/src/backend/v4/orchestration/orchestration_manager.py
index e105a34cb..d38748d83 100644
--- a/src/backend/v4/orchestration/orchestration_manager.py
+++ b/src/backend/v4/orchestration/orchestration_manager.py
@@ -2,20 +2,24 @@
 
 import asyncio
 import logging
+import re
 import uuid
 from typing import List, Optional
 
 # agent_framework imports
-from agent_framework_azure_ai import AzureAIAgentClient
+from agent_framework_azure_ai import AzureAIClient
 from agent_framework import (
+    ChatAgent,
     ChatMessage,
     WorkflowOutputEvent,
     MagenticBuilder,
     InMemoryCheckpointStorage,
-    MagenticOrchestratorMessageEvent,
-    MagenticAgentDeltaEvent,
-    MagenticAgentMessageEvent,
-    MagenticFinalResultEvent,
+    AgentRunUpdateEvent,
+    GroupChatRequestSentEvent,
+    GroupChatResponseReceivedEvent,
+    ExecutorCompletedEvent,
+    MagenticOrchestratorEvent,
+    MagenticProgressLedger,
 )
 
 from common.config.app_config import config
@@ -43,6 +47,53 @@ def __init__(self):
         self.user_id: Optional[str] = None
         self.logger = self.__class__.logger
 
+    def _extract_response_text(self, data) -> str:
+        """
+        Extract text content from various agent_framework response types.
+
+        Handles:
+        - ChatMessage: Extract .text
+        - AgentResponse: Extract .text
+        - AgentExecutorResponse: Extract from agent_response.text or full_conversation[-1].text
+        - List of any of the above
+        """
+        if data is None:
+            return ""
+
+        # Direct ChatMessage
+        if isinstance(data, ChatMessage):
+            return data.text or ""
+
+        # Has .text attribute directly (AgentResponse, etc.)
+        if hasattr(data, "text") and data.text:
+            return data.text
+
+        # AgentExecutorResponse - has agent_response and full_conversation
+        if hasattr(data, "agent_response"):
+            # Try to get text from agent_response first
+            agent_resp = data.agent_response
+            if agent_resp and hasattr(agent_resp, "text") and agent_resp.text:
+                return agent_resp.text
+            # Fallback to last message in full_conversation
+            if hasattr(data, "full_conversation") and data.full_conversation:
+                last_msg = data.full_conversation[-1]
+                if isinstance(last_msg, ChatMessage) and last_msg.text:
+                    return last_msg.text
+
+        # List of items - could be AgentExecutorResponse, ChatMessage, etc.
+        if isinstance(data, list) and len(data) > 0:
+            texts = []
+            for item in data:
+                # Recursively extract from each item
+                item_text = self._extract_response_text(item)
+                if item_text:
+                    texts.append(item_text)
+            if texts:
+                # Return the last non-empty response (most recent)
+                return texts[-1]
+
+        return ""
+
     # ---------------------------
     # Orchestration construction
     # ---------------------------
@@ -58,7 +109,7 @@ async def init_orchestration(
         Initialize a Magentic workflow with:
           - Provided agents (participants)
           - HumanApprovalMagenticManager as orchestrator manager
-          - AzureAIAgentClient as the underlying chat client
+          - AzureAIClient as the underlying chat client
           - Event-based callbacks for streaming and final responses
         - Uses same deployment, endpoint, and credentials
         - Applies same execution settings (temperature, max_tokens)
@@ -72,33 +123,45 @@ async def init_orchestration(
 
         # Create Azure AI Agent client for orchestration using config
         # This replaces AzureChatCompletion from SK
-        agent_name = team_config.name if team_config.name else "OrchestratorAgent"
+        # Sanitize agent name: must start/end with alphanumeric, only hyphens allowed, max 63 chars
+        raw_name = team_config.name if team_config.name else "OrchestratorAgent"
+        # Replace spaces and invalid chars with hyphens, strip leading/trailing hyphens
+        sanitized_name = re.sub(r'[^a-zA-Z0-9-]', '-', raw_name)
+        sanitized_name = re.sub(r'-+', '-', sanitized_name)  # Collapse multiple hyphens
+        sanitized_name = sanitized_name.strip('-')[:63]  # Trim and limit length
+        agent_name = sanitized_name if sanitized_name else "OrchestratorAgent"
 
         try:
-            chat_client = AzureAIAgentClient(
+            # Create the chat client (AzureAIClient)
+            chat_client = AzureAIClient(
                 project_endpoint=config.AZURE_AI_PROJECT_ENDPOINT,
                 model_deployment_name=team_config.deployment_name,
                 agent_name=agent_name,
-                async_credential=credential,
+                credential=credential,
+            )
+
+            # New API: Create a ChatAgent to wrap the chat client for the manager
+            manager_agent = ChatAgent(
+                chat_client=chat_client,
+                name="MagenticManager",
+                default_options={"store": False},  # Client-managed conversation to avoid stale tool call IDs across rounds
             )
 
             cls.logger.info(
-                "Created AzureAIAgentClient for orchestration with model '%s' at endpoint '%s'",
+                "Created AzureAIClient and manager ChatAgent for orchestration with model '%s' at endpoint '%s'",
                 team_config.deployment_name,
                 config.AZURE_AI_PROJECT_ENDPOINT,
             )
         except Exception as e:
-            cls.logger.error("Failed to create AzureAIAgentClient: %s", e)
+            cls.logger.error("Failed to create AzureAIClient: %s", e)
             raise
 
-        # Create HumanApprovalMagenticManager with the chat client
-        # Execution settings (temperature=0.1, max_tokens=4000) are configured via
-        # orchestration_config.create_execution_settings() which matches old SK version
+        # Create HumanApprovalMagenticManager with the manager agent
+        # New API: StandardMagenticManager takes agent as first positional argument
         try:
             manager = HumanApprovalMagenticManager(
                 user_id=user_id,
-                chat_client=chat_client,
-                instructions=None,  # Orchestrator system instructions (optional)
+                agent=manager_agent,  # New API: pass agent instead of chat_client
                 max_round_count=orchestration_config.max_rounds,
             )
             cls.logger.info(
@@ -132,13 +195,19 @@ async def init_orchestration(
 
         # Assemble workflow with callback
         storage = InMemoryCheckpointStorage()
+
+        # New SDK: participants() accepts a Sequence (list) of agents
+        # The orchestrator uses agent.name to identify them
+        participant_list = list(participants.values())
+        cls.logger.info("Participants for workflow: %s", list(participants.keys()))
+
         builder = (
             MagenticBuilder()
-            .participants(**participants)
-            .with_standard_manager(
-                manager=manager,
+            .participants(participant_list)  # New SDK: pass as list
+            .with_manager(
+                manager=manager,  # Pass manager instance (extends StandardMagenticManager)
                 max_round_count=orchestration_config.max_rounds,
-                max_stall_count=0,
+                max_stall_count=0,  # CRITICAL: Prevent re-calling agents when stalled (default is 3!)
             )
             .with_checkpointing(storage)
         )
@@ -162,17 +231,22 @@ async def get_current_or_new_orchestration(
         team_config: TeamConfiguration,
         team_switched: bool,
         team_service: TeamService = None,
+        force_rebuild: bool = False,
     ):
         """
         Return an existing workflow for the user or create a new one if:
           - None exists
           - Team switched flag is True
+          - force_rebuild is True (for new tasks after workflow completion)
         """
         current = orchestration_config.get_current_orchestration(user_id)
-        if current is None or team_switched:
-            if current is not None and team_switched:
+        needs_rebuild = current is None or team_switched or force_rebuild
+
+        if needs_rebuild:
+            if current is not None and (team_switched or force_rebuild):
+                reason = "team switched" if team_switched else "force rebuild for new task"
                 cls.logger.info(
-                    "Team switched, closing previous agents for user '%s'", user_id
+                    "Rebuilding orchestration for user '%s' (reason: %s)", user_id, reason
                 )
                 # Close prior agents (same logic as old version)
                 for agent in getattr(current, "_participants", {}).values():
@@ -200,20 +274,17 @@ async def get_current_or_new_orchestration(
                 cls.logger.error(
                     "Failed to create agents for user '%s': %s", user_id, e
                 )
-                print(f"Failed to create agents for user '{user_id}': {e}")
                 raise
             try:
                 cls.logger.info("Initializing new orchestration for user '%s'", user_id)
-                orchestration_config.orchestrations[user_id] = (
-                    await cls.init_orchestration(
-                        agents, team_config, team_service.memory_context, user_id
-                    )
+                workflow = await cls.init_orchestration(
+                    agents, team_config, team_service.memory_context, user_id
                 )
+                orchestration_config.orchestrations[user_id] = workflow
             except Exception as e:
                 cls.logger.error(
                     "Failed to initialize orchestration for user '%s': %s", user_id, e
                 )
-                print(f"Failed to initialize orchestration for user '{user_id}': {e}")
                 raise
         return orchestration_config.get_current_orchestration(user_id)
 
@@ -299,61 +370,122 @@ async def run_orchestration(self, user_id: str, input_task) -> None:
         task_text = getattr(input_task, "description", str(input_task))
         self.logger.debug("Task: %s", task_text)
 
+        # Track how many times each agent is called (for debugging duplicate calls)
+        agent_call_counts: dict = {}
+
         try:
             # Execute workflow using run_stream with task as positional parameter
             # The execution settings are configured in the manager/client
             final_output: str | None = None
 
             self.logger.info("Starting workflow execution...")
+
+            last_message_id: str | None = None
             async for event in workflow.run_stream(task_text):
                 try:
-                    # Handle orchestrator messages (task assignments, coordination)
-                    if isinstance(event, MagenticOrchestratorMessageEvent):
-                        message_text = getattr(event.message, "text", "")
-                        self.logger.info(f"[ORCHESTRATOR:{event.kind}] {message_text}")
+                    # Only log non-streaming events (reduce noise)
+                    event_type_name = type(event).__name__
+                    if event_type_name != "AgentRunUpdateEvent":
+                        self.logger.info("[EVENT] %s", event_type_name)
+
+                    # Handle orchestrator events (plan, progress ledger)
+                    if isinstance(event, MagenticOrchestratorEvent):
+                        self.logger.info(
+                            "[Magentic Orchestrator Event] Type: %s",
+                            event.event_type.name
+                        )
+                        if isinstance(event.data, ChatMessage):
+                            self.logger.info("Plan message: %s", event.data.text[:200] if event.data.text else "")
+                        elif isinstance(event.data, MagenticProgressLedger):
+                            self.logger.info("Progress ledger received")
 
-                    # Handle streaming updates from agents
-                    elif isinstance(event, MagenticAgentDeltaEvent):
+                    # Handle agent streaming/updates (replaces MagenticAgentDeltaEvent and MagenticAgentMessageEvent)
+                    elif isinstance(event, AgentRunUpdateEvent):
+                        message_id = event.data.message_id if hasattr(event.data, 'message_id') else None
+                        executor_id = event.executor_id
+
+                        # Stream the update
                         try:
                             await streaming_agent_response_callback(
-                                event.agent_id,
-                                event,  # Pass the event itself as the update object
-                                False,  # Not final yet (streaming in progress)
+                                executor_id,
+                                event.data,  # Pass the data object
+                                False,  # Not final yet
                                 user_id,
                             )
                         except Exception as e:
                             self.logger.error(
-                                f"Error in streaming callback for agent {event.agent_id}: {e}"
+                                "Error in streaming callback for agent %s: %s",
+                                executor_id, e
                             )
 
-                    # Handle final agent messages (complete response)
-                    elif isinstance(event, MagenticAgentMessageEvent):
-                        if event.message:
-                            try:
+                        # Track message for formatting
+                        if message_id != last_message_id:
+                            last_message_id = message_id
+
+                    # Handle group chat request sent
+                    elif isinstance(event, GroupChatRequestSentEvent):
+                        agent_name = event.participant_name
+                        agent_call_counts[agent_name] = agent_call_counts.get(agent_name, 0) + 1
+                        call_num = agent_call_counts[agent_name]
+
+                        self.logger.info(
+                            "[REQUEST SENT (round %d)] to agent: %s (call #%d)",
+                            event.round_index,
+                            agent_name,
+                            call_num
+                        )
+
+                        if call_num > 1:
+                            self.logger.warning("Agent '%s' called %d times", agent_name, call_num)
+
+                    # Handle group chat response received - THIS IS WHERE AGENT RESPONSES COME
+                    elif isinstance(event, GroupChatResponseReceivedEvent):
+                        self.logger.info(
+                            "[RESPONSE RECEIVED (round %d)] from agent: %s",
+                            event.round_index,
+                            event.participant_name
+                        )
+                        # Send the agent response to the UI
+                        if event.data:
+                            response_text = self._extract_response_text(event.data)
+
+                            if response_text:
+                                self.logger.info("Sending agent response to UI from %s", event.participant_name)
                                 agent_response_callback(
-                                    event.agent_id, event.message, user_id
-                                )
-                            except Exception as e:
-                                self.logger.error(
-                                    f"Error in agent callback for agent {event.agent_id}: {e}"
+                                    event.participant_name,
+                                    ChatMessage(role="assistant", text=response_text),
+                                    user_id,
                                 )
 
-                    # Handle final result from the entire workflow
-                    elif isinstance(event, MagenticFinalResultEvent):
-                        final_text = getattr(event.message, "text", "")
-                        self.logger.info(
-                            f"[FINAL RESULT] Length: {len(final_text)} chars"
+                    # Handle executor completed - just log, don't send to UI (GroupChatResponseReceivedEvent handles that)
+                    elif isinstance(event, ExecutorCompletedEvent):
+                        self.logger.debug(
+                            "[EXECUTOR COMPLETED] agent: %s",
+                            event.executor_id
                         )
+                        # Don't send to UI here - GroupChatResponseReceivedEvent already handles agent messages
+                        # This avoids duplicate messages
 
                     # Handle workflow output event (captures final result)
                     elif isinstance(event, WorkflowOutputEvent):
                         output_data = event.data
+                        # Handle different output formats
                         if isinstance(output_data, ChatMessage):
-                            final_output = getattr(output_data, "text", None) or str(
-                                output_data
-                            )
+                            final_output = output_data.text or ""
+                        elif isinstance(output_data, list):
+                            # Handle list of ChatMessage objects
+                            texts = []
+                            for item in output_data:
+                                if isinstance(item, ChatMessage):
+                                    if item.text:
+                                        texts.append(item.text)
+                                else:
+                                    texts.append(str(item))
+                            final_output = "\n".join(texts)
+                        elif hasattr(output_data, "text"):
+                            final_output = output_data.text or ""
                         else:
-                            final_output = str(output_data)
+                            final_output = str(output_data) if output_data else ""
                         self.logger.debug("Received workflow output event")
 
                 except Exception as e:
@@ -365,6 +497,9 @@ async def run_orchestration(self, user_id: str, input_task) -> None:
             # Extract final result
             final_text = final_output if final_output else ""
 
+            # Log agent call summary
+            self.logger.info("Agent call counts: %s", agent_call_counts)
+
             # Log results
             self.logger.info("\nAgent responses:")
             self.logger.info(