diff --git a/agent-langgraph-long-term-memory/agent_server/agent.py b/agent-langgraph-long-term-memory/agent_server/agent.py
index 6ccf7e85..faed7923 100644
--- a/agent-langgraph-long-term-memory/agent_server/agent.py
+++ b/agent-langgraph-long-term-memory/agent_server/agent.py
@@ -29,6 +29,7 @@
     get_user_workspace_client,
     process_agent_astream_events,
 )
+from agent_server.utils_agent_memory import agent_memory_tools, read_agent_instructions
 from agent_server.utils_memory import (
     get_lakebase_access_error_message,
     get_user_id,
@@ -56,6 +57,7 @@ def get_current_time() -> str:
 _LAKEBASE_INSTANCE_NAME_RAW = os.getenv("LAKEBASE_INSTANCE_NAME") or None
 EMBEDDING_ENDPOINT = "databricks-gte-large-en"
 EMBEDDING_DIMS = 1024
+UC_VOLUME = os.getenv("UC_VOLUME", "")
 LAKEBASE_AUTOSCALING_PROJECT = os.getenv("LAKEBASE_AUTOSCALING_PROJECT") or None
 LAKEBASE_AUTOSCALING_BRANCH = os.getenv("LAKEBASE_AUTOSCALING_BRANCH") or None
 
@@ -100,7 +102,11 @@ def get_current_time() -> str:
 - Trivial or one-off details (e.g., what they ate for lunch, a single troubleshooting step)
 - Highly sensitive personal information (health conditions, political affiliation, sexual orientation, \
 religion, criminal history) — unless the user explicitly asks you to store it
-- Information that could feel intrusive or overly personal to store"""
+- Information that could feel intrusive or overly personal to store
+
+## Agent Memory (shared across all users)
+- Use save_agent_instruction to save learnings that apply to ALL users: team preferences, process rules, best practices
+- Use get_agent_instructions to read the current shared instructions"""
 
 
 def init_mcp_client(workspace_client: WorkspaceClient) -> DatabricksMultiServerMCPClient:
@@ -116,8 +122,10 @@ def init_mcp_client(workspace_client: WorkspaceClient) -> DatabricksMultiServerM
     )
 
 
-async def init_agent(store: BaseStore, workspace_client: Optional[WorkspaceClient] = None):
+async def init_agent(store: BaseStore, workspace_client: Optional[WorkspaceClient] = None, system_prompt: str = SYSTEM_PROMPT):
     tools = [get_current_time] + memory_tools()
+    if UC_VOLUME:
+        tools += agent_memory_tools(workspace_client or sp_workspace_client, UC_VOLUME)
     # To use MCP server tools instead, replace the line above with:
     # mcp_client = init_mcp_client(workspace_client or sp_workspace_client)
     # try:
@@ -128,7 +136,7 @@ async def init_agent(store: BaseStore, workspace_client: Optional[WorkspaceClien
     return create_agent(
         model=ChatDatabricks(endpoint=LLM_ENDPOINT_NAME),
         tools=tools,
-        system_prompt=SYSTEM_PROMPT,
+        system_prompt=system_prompt,
         store=store,
     )
 
@@ -175,9 +183,16 @@ async def stream_handler(
             if user_id:
                 config["configurable"]["user_id"] = user_id
 
+            # Inject agent-scoped instructions from UC Volume into system prompt
+            full_prompt = SYSTEM_PROMPT
+            if UC_VOLUME:
+                instructions = read_agent_instructions(sp_workspace_client, UC_VOLUME)
+                if instructions.strip():
+                    full_prompt += f"\n\n## Current Agent Instructions\n{instructions}"
+
             # By default, uses service principal credentials (sp_workspace_client).
             # For on-behalf-of user authentication, use get_user_workspace_client() instead.
-            agent = await init_agent(workspace_client=sp_workspace_client, store=store)
+            agent = await init_agent(workspace_client=sp_workspace_client, store=store, system_prompt=full_prompt)
             async for event in process_agent_astream_events(
                 agent.astream(messages, config, stream_mode=["updates", "messages"])
             ):
diff --git a/agent-langgraph-long-term-memory/agent_server/utils_agent_memory.py b/agent-langgraph-long-term-memory/agent_server/utils_agent_memory.py
new file mode 100644
index 00000000..0c607a70
--- /dev/null
+++ b/agent-langgraph-long-term-memory/agent_server/utils_agent_memory.py
@@ -0,0 +1,47 @@
+import logging
+from io import BytesIO
+
+from databricks.sdk import WorkspaceClient
+from langchain_core.runnables import RunnableConfig
+from langchain_core.tools import tool
+
+logger = logging.getLogger(__name__)
+
+MAX_INSTRUCTION_LINES = 50
+
+
+def _volume_base_path(volume: str) -> str:
+    return f"/Volumes/{volume.replace('.', '/')}"
+
+
+def read_agent_instructions(w: WorkspaceClient, volume: str) -> str:
+    """Read instructions.md from a UC Volume. Returns empty string if not found."""
+    path = f"{_volume_base_path(volume)}/instructions.md"
+    try:
+        resp = w.files.download(path)
+        return resp.contents.read().decode("utf-8")
+    except Exception:
+        return ""
+
+
+def agent_memory_tools(workspace_client: WorkspaceClient, volume: str):
+    @tool
+    def save_agent_instruction(instruction: str, config: RunnableConfig) -> str:
+        """Save a new instruction to the shared agent memory. Use for learnings that
+        apply to ALL users: team preferences, process rules, best practices."""
+        current = read_agent_instructions(workspace_client, volume)
+        lines = [l for l in current.strip().split("\n") if l.strip()] if current.strip() else []
+        if sum(1 for l in lines if l.startswith("- ")) >= MAX_INSTRUCTION_LINES:
+            return f"Cannot save — already at {MAX_INSTRUCTION_LINES} instructions."
+        lines.append(f"- {instruction}")
+        path = f"{_volume_base_path(volume)}/instructions.md"
+        workspace_client.files.upload(path, BytesIO(("\n".join(lines) + "\n").encode("utf-8")), overwrite=True)
+        return f"Saved agent instruction: {instruction}"
+
+    @tool
+    def get_agent_instructions(config: RunnableConfig) -> str:
+        """Read the current shared agent instructions."""
+        content = read_agent_instructions(workspace_client, volume)
+        return content if content.strip() else "No agent instructions saved yet."
+
+    return [save_agent_instruction, get_agent_instructions]
diff --git a/agent-openai-agents-sdk/agent_server/agent.py b/agent-openai-agents-sdk/agent_server/agent.py
index b336ffdc..2116617e 100644
--- a/agent-openai-agents-sdk/agent_server/agent.py
+++ b/agent-openai-agents-sdk/agent_server/agent.py
@@ -4,7 +4,7 @@
 
 import litellm
 import mlflow
-from agents import Agent, Runner, function_tool, set_default_openai_api, set_default_openai_client
+from agents import Agent, ModelSettings, Runner, function_tool, set_default_openai_api, set_default_openai_client
 from agents.tracing import set_trace_processors
 from databricks.sdk import WorkspaceClient
 from databricks_openai import AsyncDatabricksOpenAI
@@ -40,21 +40,49 @@ def get_current_time() -> str:
     return datetime.now().isoformat()
 
 
-async def init_mcp_server(workspace_client: WorkspaceClient):
-    return McpServer(
-        url=build_mcp_url("/api/2.0/mcp/functions/system/ai", workspace_client=workspace_client),
-        name="system.ai UC function MCP server",
-        workspace_client=workspace_client,
+MEMORY_MCP_HOST = "https://eng-ml-agent-platform.staging.cloud.databricks.com"
+memory_ws_client = WorkspaceClient(host=MEMORY_MCP_HOST, profile="agent-platform")
+MEMORY_STORE = "test-embed"
+
+MEMORY_SYSTEM_PROMPT = f"""You are a helpful assistant with long-term memory. You proactively remember things about users.
+
+Always use memory_store="{MEMORY_STORE}" for all memory operations.
+
+## Before every response
+1. Call search_memory scope="agent", query="response preferences and procedures" to load shared instructions.
+2. Call search_memory scope="user" to check for personal context about the current user.
+
+## Saving memories
+Proactively save anything the user shares about themselves (location, role, preferences, interests, etc.) using write_memory. Use scope="user" for personal facts, scope="agent" for shared rules that apply to all users.
+
+## Conversation history
+Refer to the current chat history for questions about this session. Only search memory for info from previous sessions."""
+
+
+async def init_mcp_servers():
+    memory = McpServer(
+        url=f"{MEMORY_MCP_HOST}/api/2.0/mcp/sql",
+        name="memory-mcp",
+        workspace_client=memory_ws_client,
+        params={
+            "headers": {"x-databricks-traffic-id": "testenv://liteswap/jennymemorysa"},
+        },
     )
+    github = McpServer(
+        url=f"{MEMORY_MCP_HOST}/api/2.0/mcp/external/github_demo",
+        name="github-mcp",
+        workspace_client=memory_ws_client,
+    )
+    return memory, github
 
 
 def create_agent(mcp_servers: list[McpServer] | None = None) -> Agent:
     return Agent(
-        name="Agent",
-        instructions="You are a helpful assistant.",
+        name="Code review agent",
+        instructions=MEMORY_SYSTEM_PROMPT,
         model="databricks-gpt-5-2",
-        tools=[get_current_time],
         mcp_servers=mcp_servers or [],
+        model_settings=ModelSettings(parallel_tool_calls=False),
     )
 
 
@@ -62,19 +90,12 @@ def create_agent(mcp_servers: list[McpServer] | None = None) -> Agent:
 async def invoke_handler(request: ResponsesAgentRequest) -> ResponsesAgentResponse:
     if session_id := get_session_id(request):
         mlflow.update_current_trace(metadata={"mlflow.trace.session": session_id})
-    # To use MCP server tools, wrap the code below with this async context manager.
-    # By default, uses service principal credentials via WorkspaceClient().
-    # For on-behalf-of user authentication, use get_user_workspace_client() instead.
-    # try:
-    #     async with await init_mcp_server(WorkspaceClient()) as mcp_server:
-    #         agent = create_agent(mcp_servers=[mcp_server])
-    # except Exception:
-    #     logger.warning("MCP server unavailable. Continuing without MCP tools.", exc_info=True)
-    #     agent = create_agent()
-    agent = create_agent()
-    messages = [i.model_dump() for i in request.input]
-    result = await Runner.run(agent, messages)
-    return ResponsesAgentResponse(output=[item.to_input_item() for item in result.new_items])
+    memory_srv, github_srv = await init_mcp_servers()
+    async with memory_srv as mem, github_srv as gh:
+        agent = create_agent(mcp_servers=[mem, gh])
+        messages = [i.model_dump() for i in request.input]
+        result = await Runner.run(agent, messages)
+        return ResponsesAgentResponse(output=[item.to_input_item() for item in result.new_items])
 
 
 @stream()
@@ -83,18 +104,11 @@ async def stream_handler(
 ) -> AsyncGenerator[ResponsesAgentStreamEvent, None]:
     if session_id := get_session_id(request):
         mlflow.update_current_trace(metadata={"mlflow.trace.session": session_id})
-    # To use MCP server tools, wrap the code below with this async context manager.
-    # By default, uses service principal credentials via WorkspaceClient().
-    # For on-behalf-of user authentication, use get_user_workspace_client() instead.
-    # try:
-    #     async with await init_mcp_server(WorkspaceClient()) as mcp_server:
-    #         agent = create_agent(mcp_servers=[mcp_server])
-    # except Exception:
-    #     logger.warning("MCP server unavailable. Continuing without MCP tools.", exc_info=True)
-    #     agent = create_agent()
-    agent = create_agent()
-    messages = [i.model_dump() for i in request.input]
-    result = Runner.run_streamed(agent, input=messages)
-
-    async for event in process_agent_stream_events(result.stream_events()):
-        yield event
+    memory_srv, github_srv = await init_mcp_servers()
+    async with memory_srv as mem, github_srv as gh:
+        agent = create_agent(mcp_servers=[mem, gh])
+        messages = [i.model_dump() for i in request.input]
+        result = Runner.run_streamed(agent, input=messages)
+
+        async for event in process_agent_stream_events(result.stream_events()):
+            yield event