Alexlavaee/feature/handoff workflow

.mcp.json

@@ -1,5 +1,9 @@
 {
   "mcpServers": {
+    "microsoft-learn": {
+      "type": "http",
+      "url": "https://learn.microsoft.com/api/mcp"
+    },
     "deepwiki": {
       "type": "http",
       "url": "https://mcp.deepwiki.com/mcp"

AGENTS.md

@@ -397,6 +397,8 @@ You have a collection of tools available to assist with development and debuggin
 
 - `sequential-thinking-tools`
   - **When to use:** For complex reasoning tasks that require step-by-step analysis. A good rule of thumb is if the task requires more than 25% effort.
+- `microsoft-learn`
+  - **When to use:** For looking up official Microsoft documentation on .NET, C#, Azure, and related technologies. Useful for understanding APIs, libraries, and best practices. Use this over `deepwiki` for Microsoft-specific content (e.g. Microsoft Agent Framework, Azure OpenAI). If you can't find it here, then fall back to `deepwiki`.
 - `deepwiki`
   - **When to use:** Consult for external knowledge or documentation that is not part of the immediate codebase. Can be helpful for system design questions or understanding third-party libraries.
 - `context7`

CLAUDE.md

@@ -397,6 +397,8 @@ You have a collection of tools available to assist with development and debuggin
 
 - `sequential-thinking-tools`
   - **When to use:** For complex reasoning tasks that require step-by-step analysis. A good rule of thumb is if the task requires more than 25% effort.
+- `microsoft-learn`
+  - **When to use:** For looking up official Microsoft documentation on .NET, C#, Azure, and related technologies. Useful for understanding APIs, libraries, and best practices. Use this over `deepwiki` for Microsoft-specific content (e.g. Microsoft Agent Framework, Azure OpenAI). If you can't find it here, then fall back to `deepwiki`.
 - `deepwiki`
   - **When to use:** Consult for external knowledge or documentation that is not part of the immediate codebase. Can be helpful for system design questions or understanding third-party libraries.
 - `context7`

src/spec_to_agents/agents/budget_analyst.py

@@ -60,7 +60,7 @@ def create_agent(
         agent_tools.append(global_tools["sequential-thinking"])
 
     return client.create_agent(
-        name="BudgetAnalyst",
+        name="budget_analyst",
         description="Expert in financial planning, budgeting, and cost analysis for events.",
         instructions=budget_analyst.SYSTEM_PROMPT,
         tools=agent_tools,

src/spec_to_agents/agents/catering_coordinator.py

@@ -49,7 +49,7 @@ def create_agent(
         agent_tools.append(global_tools["sequential-thinking"])
 
     return client.create_agent(
-        name="CateringCoordinator",
+        name="catering_coordinator",
         description="Expert in catering services, menu planning, and vendor coordination for events.",
         instructions=catering_coordinator.SYSTEM_PROMPT,
         tools=agent_tools,

src/spec_to_agents/agents/logistics_manager.py

@@ -54,7 +54,7 @@ def create_agent(
         agent_tools.append(global_tools["sequential-thinking"])
 
     return client.create_agent(
-        name="LogisticsManager",
+        name="logistics_manager",
         description="Expert in event logistics, scheduling, and weather planning.",
         instructions=logistics_manager.SYSTEM_PROMPT,
         tools=agent_tools,

src/spec_to_agents/agents/venue_specialist.py

@@ -49,7 +49,7 @@ def create_agent(
         agent_tools.append(global_tools["sequential-thinking"])
 
     return client.create_agent(
-        name="VenueSpecialist",
+        name="venue_specialist",
         description="Expert in venue selection, site visits, and facility management for events.",
         instructions=venue_specialist.SYSTEM_PROMPT,
         tools=agent_tools,

src/spec_to_agents/console.py

@@ -28,16 +28,15 @@
     WorkflowRunState,
     WorkflowStatusEvent,
 )
+from agent_framework._workflows._handoff import HandoffUserInputRequest
 from agent_framework.observability import setup_observability
 from dotenv import load_dotenv
 
 from spec_to_agents.container import AppContainer
-from spec_to_agents.models.messages import HumanFeedbackRequest
 from spec_to_agents.utils.display import (
     console,
     display_agent_run_update,
     display_final_output,
-    display_human_feedback_request,
     display_welcome_header,
     display_workflow_pause,
     prompt_for_event_request,
@@ -50,7 +49,9 @@
 if os.getenv("ENABLE_OTEL", "false").lower() == "true":
     setup_observability()
 
+logging.getLogger("agent_framework._clients").setLevel(logging.ERROR)
 logging.getLogger("agent_framework._workflows._validation").setLevel(logging.ERROR)
+logging.getLogger("mcp").setLevel(logging.ERROR)
 
 
 async def main() -> None:
@@ -91,7 +92,7 @@ async def main() -> None:
             return
 
         console.print()
-        console.rule("[bold green]Workflow Execution")
+        console.rule("[bold gray]Workflow Execution")
         console.print()
 
         # Configuration: Toggle to display streaming agent run updates
@@ -103,7 +104,7 @@ async def main() -> None:
         pending_responses: dict[str, str] | None = None
         workflow_output: str | None = None
 
-        # Track printed tool calls/results to avoid duplication in streaming
+        # Track printed tool calls/results/code blocks to avoid duplication in streaming
         printed_tool_calls: set[str] = set()
         printed_tool_results: set[str] = set()
         last_executor: str | None = None
@@ -118,7 +119,7 @@ async def main() -> None:
             pending_responses = None
 
             # Process events as they stream in
-            human_requests: list[tuple[str, HumanFeedbackRequest]] = []
+            human_requests: list[tuple[str, HandoffUserInputRequest]] = []
 
             async for event in stream:
                 # Display streaming agent updates if enabled
@@ -128,7 +129,7 @@ async def main() -> None:
                     )
 
                 # Collect human-in-the-loop requests
-                elif isinstance(event, RequestInfoEvent) and isinstance(event.data, HumanFeedbackRequest):
+                elif isinstance(event, RequestInfoEvent) and isinstance(event.data, HandoffUserInputRequest):
                     # Workflow is requesting human input
                     human_requests.append((event.request_id, event.data))
 
@@ -148,9 +149,16 @@ async def main() -> None:
             if human_requests:
                 responses: dict[str, str] = {}
 
-                for request_id, feedback_request in human_requests:
-                    user_response = display_human_feedback_request(feedback_request)
-                    if user_response is None:
+                for request_id, handoff_request in human_requests:
+                    # Display prompt from HandoffUserInputRequest
+                    console.print()
+                    console.print(f"[bold cyan]User Input Requested:[/bold cyan] {handoff_request.prompt}")
+                    console.print()
+
+                    # Get user input
+                    user_response = console.input("[bold green]You:[/bold green] ")
+
+                    if not user_response or user_response.strip().lower() in ("exit", "quit"):
                         return
 
                     responses[request_id] = user_response

src/spec_to_agents/main.py

@@ -1,13 +1,16 @@
 # Copyright (c) Microsoft. All rights reserved.
 
+import os
+
 from agent_framework.observability import setup_observability
 from dotenv import load_dotenv
 
 # Load environment variables at module import
 load_dotenv()
 
-# Enable observability (reads from environment variables)
-setup_observability()
+# Enable observability conditionally based on ENABLE_OTEL env var
+if os.getenv("ENABLE_OTEL", "false").lower() == "true":
+    setup_observability()
 
 
 def main() -> None:

src/spec_to_agents/models/messages.py

@@ -6,7 +6,6 @@
 from typing import Any
 
 from agent_framework import ChatMessage
-from pydantic import BaseModel, Field
 
 
 @dataclass
@@ -49,37 +48,4 @@ class HumanFeedbackRequest:
     conversation: list[ChatMessage] = field(default_factory=list)  # type: ignore
 
 
-class SupervisorDecision(BaseModel):
-    """
-    Structured output from supervisor agent for routing decisions.
-
-    The supervisor evaluates conversation history and decides which
-    participant to route to next, whether to request user input,
-    or whether the workflow is complete.
-
-    Workflow completion occurs when next_agent=None AND user_input_needed=False.
-
-    Examples
-    --------
-    Route to participant:
-    >>> SupervisorDecision(next_agent="venue", user_input_needed=False)
-
-    Request user input:
-    >>> SupervisorDecision(
-    ...     next_agent=None,
-    ...     user_input_needed=True,
-    ...     user_prompt="Which venue do you prefer?",
-    ... )
-
-    Workflow complete:
-    >>> SupervisorDecision(next_agent=None, user_input_needed=False)
-    """
-
-    next_agent: str | None = Field(
-        description=("ID of next participant to route to or None if workflow is complete and ready for final synthesis")
-    )
-    user_input_needed: bool = Field(default=False, description="Whether user input is required before continuing")
-    user_prompt: str | None = Field(default=None, description="Question to ask user if user_input_needed=True")
-
-
-__all__ = ["HumanFeedbackRequest", "SupervisorDecision"]
+__all__ = ["HumanFeedbackRequest"]

src/spec_to_agents/prompts/budget_analyst.py

@@ -127,27 +127,76 @@
 
 **Important:** Only request approval when budget decisions are significant or uncertain.
 
-Once you provide your budget allocation, indicate you're ready for the next step in planning.
-
-## Structured Output Format
-
-Your response MUST be structured JSON with these fields:
-- summary: Your budget allocation in maximum 200 words
-- next_agent: Which specialist should work next ("venue", "catering", "logistics") or null
-- user_input_needed: true if you need user approval/modification
-- user_prompt: Question for user (if user_input_needed is true)
-
-Routing guidance:
-- Typical flow: budget → "catering" (after allocating budget)
-- If budget constraints require venue change: route to "venue"
-- If user needs to approve budget: set user_input_needed=true
-
-Example:
-{
-  "summary": "Budget allocation: Venue $3k (60%), Catering $1.2k (24%), Logistics $0.5k (10%),
-  Contingency $0.3k (6%). Total: $5k.",
-  "next_agent": "catering",
-  "user_input_needed": false,
-  "user_prompt": null
-}
+## Communication Guidelines
+
+Use a natural, conversational tone in your responses. Structure your budget analysis using markdown for clarity.
+
+**When providing budget allocation (Autonomous Mode):**
+Present your allocation clearly with rationale:
+
+```
+Here's the budget breakdown for your event:
+
+## Budget Allocation (Total: $[Amount])
+
+| Category | Amount | Percentage | Rationale |
+|----------|--------|------------|-----------|
+| Venue | $[X] | [Y]% | [Brief explanation] |
+| Catering | $[X] | [Y]% | [Brief explanation] |
+| Equipment/AV | $[X] | [Y]% | [Brief explanation] |
+| Decorations | $[X] | [Y]% | [Brief explanation] |
+| Staffing | $[X] | [Y]% | [Brief explanation] |
+| Contingency | $[X] | [Y]% | Emergency buffer |
+
+**Key Points:**
+- [Highlight important allocation decisions]
+- [Explain any notable adjustments from standard percentages]
+
+This allocation follows industry standards for [event type] while staying within your budget constraints.
+```
+
+**When requesting user input (Collaborative Mode):**
+Present options or ask for confirmation:
+
+```
+For your [event type] with [X] attendees, I recommend a budget of approximately $[Amount] ($[X]/person).
+
+**Proposed Allocation:**
+- Venue: $[X] ([Y]%)
+- Catering: $[X] ([Y]%)
+- Other costs: $[X] ([Y]%)
+
+Does this align with your expectations? I can adjust the allocation if you'd like to prioritize different areas
+(for example, more towards catering vs. venue).
+```
+
+**When presenting alternatives (Interactive Mode):**
+Use clear comparison format:
+
+```
+Here are three budget allocation strategies for your event:
+
+### Strategy A: Venue-Focused ($[Total])
+- Venue: $[X] (65%) - Premium location, excellent facilities
+- Catering: $[X] (20%) - Standard menu
+- Other: $[X] (15%)
+**Best for:** Impressing with location and ambiance
+
+### Strategy B: Balanced ($[Total])
+- Venue: $[X] (55%) - Good quality venue
+- Catering: $[X] (30%) - Enhanced menu options
+- Other: $[X] (15%)
+**Best for:** Overall quality across all areas
+
+### Strategy C: Experience-Focused ($[Total])
+- Venue: $[X] (50%) - Adequate venue
+- Catering: $[X] (35%) - Premium dining experience
+- Other: $[X] (15%)
+**Best for:** Memorable food and beverage
+
+Which strategy aligns best with your priorities?
+```
+
+**Important:** Be conversational and helpful. Focus on value and tradeoffs, not workflow mechanics.
+**Important:**  When complete with your current task hand off back to the coordinator by calling handoff_to_coordinator
 """