Merge branch 'ecommerce' of https://github.com/joshmsmith/temporal-ai-agent into ecommerce

.gitignore

@@ -31,4 +31,6 @@ coverage.xml
 # PyCharm / IntelliJ settings
 .idea/
 
-.env
+.env
+.env*
+

README.md

@@ -47,3 +47,5 @@ See [the guide to adding goals and tools](./adding-goals-and-tools.md) for more
 
 ## For Temporal SAs
 Check out the [slides](https://docs.google.com/presentation/d/1wUFY4v17vrtv8llreKEBDPLRtZte3FixxBUn0uWy5NU/edit#slide=id.g3333e5deaa9_0_0) here and the enablement guide here (TODO).
+
+

adding-goals-and-tools.md

@@ -4,9 +4,10 @@ The agent is set up to allow for multiple goals and to switch back to choosing a
 It may be helpful to review the [architecture](./architecture.md) for a guide and definition of goals, tools, etc.
 
 ## Adding a New Goal Category
-Goal Categories lets you pick which groups of goals to show. Set via an .env setting, GOAL_CATEGORIES.
+Goal Categories lets you pick which groups of goals to show. Set via an .env setting, `GOAL_CATEGORIES`. 
+Even if you don't intend to use the goal in a multi-goal scenario, goal categories are useful for others.
 1. Pick a unique one that has some business meaning
-2. Use it in your .env file
+2. Use it in your [.env](./.env) file
 3. Add to [.env.example](./.env.example)
 4. Use it in your Goal definition, see below.
 
@@ -35,7 +36,7 @@ tools=[
 
 ## Adding Tools
 
-### Optional Tools
+### Note on Optional Tools
 Tools can be optional - you can indicate this in the tool listing of goal description (see above section re: goal registry) by adding something like, "This step is optional and can be skipped by moving to the next tool." Here is an example from an older iteration of the `goal_hr_schedule_pto` goal, when it was going to have an optional step to check for existing calendar conflicts:
 
 ```
@@ -84,4 +85,15 @@ There are three ways to manage confirmation of tool runs:
         ),
 ```
 If you really want to wait for user confirmation, record it on the workflow (as a Signal) and not rely on the LLM to probably get it, use option #3. 
-I recommend exploring all three. For a demo, I would decide if you want the Arguments confirmation in the UI, and if not I'd generally go with option #2 but use #3 for tools that make business sense to confirm, e.g. those tools that take action/write data.
+I recommend exploring all three. For a demo, I would decide if you want the Arguments confirmation in the UI, and if not I'd generally go with option #2 but use #3 for tools that make business sense to confirm, e.g. those tools that take action/write data.
+
+## Add a Goal & Tools Checklist
+[  ] Add goal in [/tools/goal_registry.py](tools/goal_registry.py) <br />
+- [  ] If a new category, add Goal Category to [.env](./.env)  and [.env.example](./.env.example)  <br />
+- [  ] don't forget the goal list at the bottom of the [goal_registry.py](tools/goal_registry.py) <br />
+
+[  ] Add Tools listed in the Goal Registry to the  [tool_registry.py](tools/tool_registry.py)  <br />
+[  ] Define your tools as Activities in `/tools` <br />
+[  ] Add your tools to [tool list](tools/__init__.py) in the tool get_handler() <br />
+
+And that's it! Happy AI Agent building!

enterprise/Activities/TrainActivities.cs

@@ -2,6 +2,7 @@ using System.Net.Http.Json;
 using System.Text.Json;
 using Temporalio.Activities;
 using TrainSearchWorker.Models;
+using Microsoft.Extensions.Logging;
 
 namespace TrainSearchWorker.Activities;
 
@@ -23,6 +24,7 @@ public class TrainActivities
     [Activity]
     public async Task<JourneyResponse> SearchTrains(SearchTrainsRequest request)
     {
+        ActivityExecutionContext.Current.Logger.LogInformation($"SearchTrains from {request.From} to {request.To}");
         var response = await _client.GetAsync(
             $"api/search?from={Uri.EscapeDataString(request.From)}" +
             $"&to={Uri.EscapeDataString(request.To)}" +
@@ -30,17 +32,21 @@ public class TrainActivities
             $"&return_time={Uri.EscapeDataString(request.ReturnTime)}");
 
         response.EnsureSuccessStatusCode();
-
+      
         // Deserialize into JourneyResponse rather than List<Journey>
         var journeyResponse = await response.Content.ReadFromJsonAsync<JourneyResponse>(_jsonOptions)
                               ?? throw new InvalidOperationException("Received null response from API");
 
+        ActivityExecutionContext.Current.Logger.LogInformation("SearchTrains completed");
+
         return journeyResponse;
     }
 
     [Activity]
     public async Task<BookTrainsResponse> BookTrains(BookTrainsRequest request)
     {
+        ActivityExecutionContext.Current.Logger.LogInformation($"Booking trains with IDs: {request.TrainIds}");
+
         // Build the URL using the train IDs from the request
         var url = $"api/book/{Uri.EscapeDataString(request.TrainIds)}";
 
@@ -52,6 +58,8 @@ public class TrainActivities
         var bookingResponse = await response.Content.ReadFromJsonAsync<BookTrainsResponse>(_jsonOptions)
                               ?? throw new InvalidOperationException("Received null response from API");
 
+        ActivityExecutionContext.Current.Logger.LogInformation("BookTrains completed");
+
         return bookingResponse;
     }
 

enterprise/Program.cs

@@ -2,10 +2,19 @@ using Microsoft.Extensions.DependencyInjection;
 using Temporalio.Client;
 using Temporalio.Worker;
 using TrainSearchWorker.Activities;
+using Microsoft.Extensions.Logging;
+using Microsoft.Extensions.Logging.Console;
 
 // Set up dependency injection
 var services = new ServiceCollection();
 
+var loggerFactory = LoggerFactory.Create(builder =>
+{
+    builder
+        .AddSimpleConsole(options => options.TimestampFormat = "[HH:mm:ss] ")
+        .SetMinimumLevel(LogLevel.Information);
+});
+
 // Add HTTP client
 services.AddHttpClient("TrainApi", client =>
 {
@@ -31,7 +40,10 @@ Console.WriteLine($"Connecting to Temporal at address: {address}");
 Console.WriteLine($"Using namespace: {ns}");
 
 // Create worker options
-var options = new TemporalWorkerOptions("agent-task-queue-legacy");
+var options = new TemporalWorkerOptions("agent-task-queue-legacy")
+{
+    LoggerFactory = loggerFactory
+};
 
 // Register activities
 var activities = serviceProvider.GetRequiredService<TrainActivities>();

enterprise/TrainSearchWorker.csproj

@@ -7,6 +7,7 @@
   </PropertyGroup>
 
   <ItemGroup>
+    <PackageReference Include="Microsoft.Extensions.Logging.Console" Version="9.0.4" />
     <PackageReference Include="Temporalio" Version="1.0.0" />
     <PackageReference Include="Microsoft.Extensions.Http" Version="8.0.0" />
   </ItemGroup>

pyproject.toml

@@ -1,5 +1,5 @@
 [tool.poetry]
-name = "temporal-AI-agent"
+name = "temporal_AI_agent"
 version = "0.1.0"
 description = "Temporal AI Agent"
 license = "MIT"
@@ -13,7 +13,13 @@ packages = [
 ]
 
 [tool.poetry.urls]
-"Bug Tracker" = "https://github.com/temporalio/samples-python/issues"
+"Bug Tracker" = "https://github.com/temporal-community/temporal-ai-agent/issues"
+
+[tool.poe.tasks]
+format = [{cmd = "black ."}, {cmd = "isort ."}]
+lint = [{cmd = "black --check ."}, {cmd = "isort --check-only ."}, {ref = "lint-types" }]
+lint-types = "mypy --check-untyped-defs --namespace-packages ."
+test = "pytest"
 
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
@@ -37,9 +43,16 @@ gtfs-kit = "^10.1.1"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.3"
+pytest-asyncio = "^0.18.3"
 black = "^23.7"
 isort = "^5.12"
 
 [build-system]
 requires = ["poetry-core>=1.4.0"]
-build-backend = "poetry.core.masonry.api"
+build-backend = "poetry.core.masonry.api"
+
+[tool.pytest.ini_options]
+asyncio_mode = "auto"
+log_cli = true
+log_cli_level = "INFO"
+log_cli_format = "%(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(lineno)s)"

scripts/find_events_test.py

@@ -1,8 +1,8 @@
-from tools.search_events import find_events
+from tools.search_flights import search_flights
 import json
 
 # Example usage
 if __name__ == "__main__":
     search_args = {"city": "Sydney", "month": "July"}
-    results = find_events(search_args)
+    results = search_flights(search_args)
     print(json.dumps(results, indent=2))

setup.md

@@ -192,7 +192,7 @@ dotnet run
 ```
 If you're running your train API above on a different host/port then change the API URL in `Program.cs`. Otherwise, be sure to run it using `python thirdparty/train_api.py`.
 
-#### Goals: FIN/Money Movement
+#### Goals: FIN - Money Movement and Loan Application
 Make sure you have the mock users you want (such as yourself) in [the account mock data file](./tools/data/customer_account_data.json).
 
 - `AGENT_GOAL=goal_fin_move_money` - This scenario _can_ initiate a secondary workflow to move money. Check out [this repo](https://github.com/temporal-sa/temporal-money-transfer-java) - you'll need to get the worker running and connected to the same account as the agentic worker. 
@@ -200,6 +200,12 @@ By default it will _not_ make a real workflow, it'll just fake it. If you get th
 ```bash
 FIN_START_REAL_WORKFLOW=FALSE #set this to true to start a real workflow
 ```
+- `AGENT_GOAL=goal_fin_loan_application` - This scenario _can_ initiate a secondary workflow to apply for a loan. Check out [this repo](https://github.com/temporal-sa/temporal-latency-optimization-scenarios) - you'll need to get the worker running and connected to the same account as the agentic worker. 
+By default it will _not_ make a real workflow, it'll just fake it. If you get the worker running and want to start a workflow, in your [.env](./.env):
+```bash
+FIN_START_REAL_WORKFLOW=FALSE #set this to true to start a real workflow
+```
+
 
 #### Goals: HR/PTO
 Make sure you have the mock users you want in (such as yourself) in [the PTO mock data file](./tools/data/employee_pto_data.json).

tests/agent_goal_workflow_test.py

@@ -1,53 +0,0 @@
-import asyncio
-
-from temporalio.client import Client, WorkflowExecutionStatus
-from temporalio.worker import Worker
-from temporalio.testing import TestWorkflowEnvironment
-from api.main import get_initial_agent_goal
-from models.data_types import AgentGoalWorkflowParams, CombinedInput
-from workflows.agent_goal_workflow import AgentGoalWorkflow
-from activities.tool_activities import ToolActivities, dynamic_tool_activity
-
-
-async def asyncSetUp(self):
-    # Set up the test environment
-    self.env = await TestWorkflowEnvironment.create_local()
-
-async def asyncTearDown(self):
-    # Clean up after tests
-    await self.env.shutdown()
-
-async def test_flight_booking(client: Client):
-
-    task_queue_name = "agent-ai-workflow"
-    workflow_id = "agent-workflow"
-    
-    initial_agent_goal = get_initial_agent_goal()
-
-    # Create combined input
-    combined_input = CombinedInput(
-        tool_params=AgentGoalWorkflowParams(None, None),
-        agent_goal=initial_agent_goal,
-    )
-
-    workflow_id = "agent-workflow"
-    async with Worker(client, task_queue=task_queue_name, workflows=[AgentGoalWorkflow], activities=[ToolActivities.agent_validatePrompt, ToolActivities.agent_toolPlanner, dynamic_tool_activity]):
-
-        # todo set goal categories for scenarios
-        handle = await client.start_workflow(
-            AgentGoalWorkflow.run, id=workflow_id, task_queue=task_queue_name
-        )
-        # todo send signals based on 
-        await handle.signal(AgentGoalWorkflow.user_prompt, "book flights")
-        await handle.signal(AgentGoalWorkflow.user_prompt, "sydney in september")
-        assert WorkflowExecutionStatus.RUNNING == (await handle.describe()).status
-
-        
-        #assert ["Hello, user1", "Hello, user2"] == await handle.result()
-        await handle.signal(AgentGoalWorkflow.user_prompt, "I'm all set, end conversation")
-        assert WorkflowExecutionStatus.COMPLETED == (await handle.describe()).status
-
-   
-
-
-    

tests/conftest.py

@@ -0,0 +1,55 @@
+import asyncio
+import multiprocessing
+import sys
+from typing import AsyncGenerator
+
+import pytest
+import pytest_asyncio
+from temporalio.client import Client
+from temporalio.testing import WorkflowEnvironment
+
+# Due to https://github.com/python/cpython/issues/77906, multiprocessing on
+# macOS starting with Python 3.8 has changed from "fork" to "spawn". For
+# pre-3.8, we are changing it for them.
+if sys.version_info < (3, 8) and sys.platform.startswith("darwin"):
+    multiprocessing.set_start_method("spawn", True)
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--workflow-environment",
+        default="local",
+        help="Which workflow environment to use ('local', 'time-skipping', or target to existing server)",
+    )
+
+
+@pytest.fixture(scope="session")
+def event_loop():
+    # See https://github.com/pytest-dev/pytest-asyncio/issues/68
+    # See https://github.com/pytest-dev/pytest-asyncio/issues/257
+    # Also need ProactorEventLoop on older versions of Python with Windows so
+    # that asyncio subprocess works properly
+    if sys.version_info < (3, 8) and sys.platform == "win32":
+        loop = asyncio.ProactorEventLoop()
+    else:
+        loop = asyncio.get_event_loop_policy().new_event_loop()
+    yield loop
+    loop.close()
+
+
+@pytest_asyncio.fixture(scope="session")
+async def env(request) -> AsyncGenerator[WorkflowEnvironment, None]:
+    env_type = request.config.getoption("--workflow-environment")
+    if env_type == "local":
+        env = await WorkflowEnvironment.start_local()
+    elif env_type == "time-skipping":
+        env = await WorkflowEnvironment.start_time_skipping()
+    else:
+        env = WorkflowEnvironment.from_client(await Client.connect(env_type))
+    yield env
+    await env.shutdown()
+
+
+@pytest_asyncio.fixture
+async def client(env: WorkflowEnvironment) -> Client:
+    return env.client

tests/workflowtests/agent_goal_workflow_test.py

@@ -0,0 +1,80 @@
+from temporalio.client import Client, WorkflowExecutionStatus
+from temporalio.worker import Worker
+import concurrent.futures
+from temporalio.testing import WorkflowEnvironment
+from api.main import get_initial_agent_goal
+from models.data_types import AgentGoalWorkflowParams, CombinedInput
+from workflows.agent_goal_workflow import AgentGoalWorkflow
+from activities.tool_activities import ToolActivities, dynamic_tool_activity
+from unittest.mock import patch
+from dotenv import load_dotenv
+import os
+from contextlib import contextmanager
+
+
+@contextmanager
+def my_context():
+    print("Setup")
+    yield "some_value"  # Value assigned to 'as' variable
+    print("Cleanup")
+
+
+
+async def test_flight_booking(client: Client):
+
+    #load_dotenv("test_flights_single.env")
+    
+    with my_context() as value:
+        print(f"Working with {value}")
+    
+        
+        # Create the test environment
+        #env = await WorkflowEnvironment.start_local()
+        #client = env.client
+        task_queue_name = "agent-ai-workflow"
+        workflow_id = "agent-workflow"
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor:        
+            worker = Worker(
+                client, 
+                task_queue=task_queue_name,
+                workflows=[AgentGoalWorkflow],
+                activities=[ToolActivities.agent_validatePrompt, ToolActivities.agent_toolPlanner, ToolActivities.get_wf_env_vars, dynamic_tool_activity],
+                activity_executor=activity_executor,
+            )
+
+            async with worker:                 
+                initial_agent_goal = get_initial_agent_goal()
+                # Create combined input
+                combined_input = CombinedInput(
+                    tool_params=AgentGoalWorkflowParams(None, None),
+                    agent_goal=initial_agent_goal,
+                )
+
+                prompt="Hello!"
+
+                #async with Worker(client, task_queue=task_queue_name, workflows=[AgentGoalWorkflow], activities=[ToolActivities.agent_validatePrompt, ToolActivities.agent_toolPlanner, dynamic_tool_activity]):
+
+                # todo set goal categories for scenarios
+                handle = await client.start_workflow(
+                    AgentGoalWorkflow.run,
+                    combined_input,
+                    id=workflow_id, 
+                    task_queue=task_queue_name,
+                    start_signal="user_prompt",
+                    start_signal_args=[prompt],
+                )
+                # todo send signals to simulate user input
+                # await handle.signal(AgentGoalWorkflow.user_prompt, "book flights") # for multi-goal
+                await handle.signal(AgentGoalWorkflow.user_prompt, "sydney in september")
+                assert WorkflowExecutionStatus.RUNNING == (await handle.describe()).status
+
+                
+                #assert ["Hello, user1", "Hello, user2"] == await handle.result()
+                await handle.signal(AgentGoalWorkflow.user_prompt, "I'm all set, end conversation")
+                
+                #assert WorkflowExecutionStatus.COMPLETED == (await handle.describe()).status
+
+                result = await handle.result()
+                #todo dump workflow history for analysis optional
+                #todo assert result is good

todo.md

@@ -1,15 +1,5 @@
 # todo list
-[ ] goal change management tweaks <br />
-  - [x] maybe make the choose_Agent_goal tag not be system/not always included? <br />
-  - [x] try taking out list-agents as a tool because agent_prompt_generators may do it for you <br />
-  - [x] make goal selection not be a system tool but be an option in .env, see how that works, includes taking it out of the goal/toolset for all goals <br />
-  - [x] test single-goal <br />
-  - [x] test claude and grok<br />
-  - [x] document in sample env and docs how to control <br />
-
 [ ] expand [tests](./tests/agent_goal_workflow_test.py)<br />
-[x] try claude-3-7-sonnet-20250219, see [tool_activities.py](./activities/tool_activities.py) <br />
-[x] test Grok with changes
 
 [ ] adding fintech goals <br />
 - Fraud Detection and Prevention - The AI monitors transactions across accounts, flagging suspicious activities (e.g., unusual spending patterns or login attempts) and autonomously freezing accounts or notifying customers and compliance teams.<br />
@@ -17,7 +7,21 @@
 - Portfolio Management and Rebalancing - The AI monitors a customer’s investment portfolio, rebalancing it automatically based on market trends, risk tolerance, and financial goals (e.g., shifting assets between stocks, bonds, or crypto).<br />
 
 [ ] new loan/fraud check/update with start <br />
+[ ] financial advise - args being freeform customer input about their financial situation, goals
+    [ ] tool is maybe a new tool asking the LLM to advise
 
+[ ] for demo simulate failure  - add utilities/simulated failures from pipeline demo <br />
+
+[ ] ecommerce goals <br />
+- [ ] add to docs <br />
+- [ ] decide about api key names with Laine <br />
+
+[ ] LLM failure->autoswitch: <br />
+    - detect failure in the activity using failurecount <br />
+    - activity switches to secondary LLM defined in .env
+    - activity reports switch to workflow
+
+[ ] for demo simulate failure  - add utilities/simulated failures from pipeline demo <br />
 
 [ ] ask the ai agent how it did at the end of the conversation, was it efficient? successful? insert a search attribute to document that before return <br />
 - Insight into the agent’s performance <br />

tools/__init__.py

@@ -16,6 +16,7 @@ from .hr.checkpaybankstatus import checkpaybankstatus
 from .fin.check_account_valid import check_account_valid
 from .fin.get_account_balances import get_account_balance
 from .fin.move_money import move_money
+from .fin.submit_loan_application import submit_loan_application
 
 from .ecommerce.get_order import get_order
 from .ecommerce.track_package import track_package
@@ -57,7 +58,9 @@ def get_handler(tool_name: str):
     if tool_name == "FinCheckAccountBalance":
         return get_account_balance    
     if tool_name == "FinMoveMoneyOrder":
-        return move_money    
+        return move_money
+    if tool_name == "FinCheckAccountSubmitLoanApproval":
+        return submit_loan_application    
     if tool_name == "GetOrder":
         return get_order
     if tool_name == "TrackPackage":

tools/fin/move_money.py

@@ -31,7 +31,6 @@ class MoneyMovementWorkflowParameterObj:
 # this assumes it's a valid account - use check_account_valid() to verify that first
 async def move_money(args: dict) -> dict:
     
-    print("in move_money")
     account_key = args.get("accountkey")
     account_type: str = args.get("accounttype")
     amount = args.get("amount")

tools/fin/submit_loan_application.py

@@ -0,0 +1,103 @@
+from datetime import date, timedelta
+import os
+from pathlib import Path
+import json
+from temporalio.client import (
+    Client,
+    WithStartWorkflowOperation,
+    WorkflowHandle,
+    WorkflowUpdateFailedError,
+)
+from temporalio import common
+from dataclasses import dataclass
+from typing import Optional
+import asyncio
+from temporalio.exceptions import WorkflowAlreadyStartedError
+from shared.config import get_temporal_client
+
+
+# Define data structures to match the Java workflow's expected input/output
+# see https://github.com/temporal-sa/temporal-latency-optimization-scenarios for more details
+@dataclass
+class TransactionRequest:
+    amount: float
+    sourceAccount: str
+    targetAccount: str
+
+@dataclass
+class TxResult:
+    transactionId: str
+    status: str
+
+#demonstrate starting a workflow and early return pattern while the workflow continues
+async def submit_loan_application(args: dict) -> dict:
+    account_key = args.get("accountkey")
+    amount = args.get("amount")
+
+    loan_status: dict = await start_workflow(amount=amount,account_name=account_key)
+
+    if loan_status.get("error") is None:
+        return {'status': loan_status.get("loan_application_status"), 'detailed_status': loan_status.get("application_details"), 'next_step': loan_status.get("advisement"), 'confirmation_id': loan_status.get("transaction_id")}  
+    else:
+        print(loan_status)
+        return loan_status
+    
+
+# Async function to start workflow
+async def start_workflow(amount: str, account_name: str, )-> dict:
+ 
+    # Connect to Temporal 
+    client = await get_temporal_client()
+    start_real_workflow = os.getenv("FIN_START_REAL_WORKFLOW")
+    if start_real_workflow is not None and start_real_workflow.lower() == "false":
+        START_REAL_WORKFLOW = False
+        return {'loan_application_status': "applied", 'application_details': "loan application is submitted and initial validation is complete",'transaction_id': "APPLICATION"+account_name, 'advisement': "You'll receive a confirmation for final approval in three business days", }  
+    else:
+        START_REAL_WORKFLOW = True
+    
+        # Define the workflow ID and task queue
+        workflow_id = "LOAN_APPLICATION-"+account_name+"-"+date.today().strftime('%Y-%m-%d')
+        task_queue = "LatencyOptimizationTEST"
+
+        # Create a TransactionRequest (matching the Java workflow's expected input)
+        tx_request = TransactionRequest(
+            amount=float(amount),
+            targetAccount=account_name,
+            sourceAccount=account_name,
+        )
+
+        start_op = WithStartWorkflowOperation(
+            "TransactionWorkflowLocalBeforeUpdate",
+            tx_request,
+            id=workflow_id,
+            id_conflict_policy=common.WorkflowIDConflictPolicy.USE_EXISTING,
+            task_queue=task_queue,
+        )
+
+        try:
+            print("trying update-with-start")
+            tx_result = TxResult(
+                await client.execute_update_with_start_workflow(
+                    "returnInitResult",
+                    start_workflow_operation=start_op,
+                )
+            )
+        except WorkflowUpdateFailedError:
+            print("aww man got exception WorkflowUpdateFailedError" )
+            tx_result = None
+            return_msg = "Loan could not be processed for " + account_name
+            return {"error": return_msg}
+
+        workflow_handle = await start_op.workflow_handle()
+        print(tx_result)
+
+        print(f"Update result: Transaction ID = {tx_result.transactionId}, Message = {tx_result.status}")
+
+        # Optionally, wait for the workflow to complete and get the final result
+        # final_result = await handle.result()
+        # print(f"Workflow completed with result: {final_result}")
+
+
+        # return {'status': loan_status.get("loan_status"), 'detailed_status': loan_status.get("results"), 'next_step': loan_status.get("advisement"), 'confirmation_id': loan_status.get("workflowID")}  
+        return {'loan_application_status': "applied", 'application_details': "loan application is submitted and initial validation is complete",'transaction_id': tx_result.transactionId, 'advisement': "You'll receive a confirmation for final approval in three business days", }  
+    

tools/goal_registry.py

@@ -305,6 +305,7 @@ goal_fin_check_account_balances = AgentGoal(
 )
 
 # this tool checks account balances, and uses ./data/customer_account_data.json as dummy data
+# it also uses a separate workflow/tool, see ./setup.md for details
 goal_fin_move_money = AgentGoal(
     id = "goal_fin_move_money",
     category_tag="fin",
@@ -322,7 +323,7 @@ goal_fin_move_money = AgentGoal(
     starter_prompt=starter_prompt_generic,
     example_conversation_history="\n ".join(
         [
-            "user: I'd like transfer some money",
+            "user: I'd like to transfer some money",
             "agent: Sure! I can help you out with that. May I have account number and email address?",
             "user: account number is 11235813",
             "user_confirmed_tool_run: <user clicks confirm on FincheckAccountIsValid tool>",
@@ -343,6 +344,34 @@ goal_fin_move_money = AgentGoal(
     ),
 )
 
+# this starts a loan approval process
+# it also uses a separate workflow/tool, see ./setup.md for details #todo
+goal_fin_loan_application = AgentGoal(
+    id = "goal_fin_loan_application",
+    category_tag="fin",
+    agent_name="Easy Loan Apply",
+    agent_friendly_description="Initiate loan application.",   
+    tools=[
+        tool_registry.financial_check_account_is_valid,
+        tool_registry.financial_submit_loan_approval, #todo 
+    ],
+    description="The user wants to apply for a loan at the financial institution. To assist with that goal, help the user gather args for these tools in order: "
+    "1. FinCheckAccountIsValid: validate the user's account is valid"
+    "2. FinCheckAccountSubmitLoanApproval: submit the loan for approval",
+    starter_prompt=starter_prompt_generic,
+    example_conversation_history="\n ".join(
+        [
+            "user: I'd like to apply for a loan",
+            "agent: Sure! I can help you out with that. May I have account number for confirmation?",
+            "user: account number is 11235813",
+            "user_confirmed_tool_run: <user clicks confirm on FincheckAccountIsValid tool>",
+            "tool_result: { 'status': account valid }",
+            "agent: Great! We've validated your account. What will the loan amount be?",
+            "user: I'd like a loan for $500",
+            "user_confirmed_tool_run: <user clicks confirm on FinCheckAccountSubmitLoanApproval tool>",
+            "tool_result: { 'status': submitted, 'detailed_status': loan application is submitted and initial validation is complete, 'confirmation id': 333421, 'next_step': You'll receive a confirmation for final approval in three business days }",
+            "agent: I have submitted your loan application process and the initial validation is successful. Your application ID is 333421. You'll receive a notification for final approval from us in three business days. "
+
 # ----- E-Commerce Goals ---
 #todo: add goal to list all orders for last X amount of time?
 # this tool checks account balances, and uses ./data/customer_account_data.json as dummy data
@@ -430,5 +459,7 @@ goal_list.append(goal_hr_check_pto)
 goal_list.append(goal_hr_check_paycheck_bank_integration_status)
 goal_list.append(goal_fin_check_account_balances)
 goal_list.append(goal_fin_move_money)
+goal_list.append(goal_fin_loan_application)
 goal_list.append(goal_ecomm_list_orders)
-goal_list.append(goal_ecomm_order_status)
+goal_list.append(goal_ecomm_order_status)
+

tools/tool_registry.py

@@ -318,6 +318,22 @@ financial_move_money = ToolDefinition(
     ],
 )
 
+financial_submit_loan_approval = ToolDefinition(
+    name="FinCheckAccountSubmitLoanApproval",
+    description="Submit a loan application. "
+    "Returns the loan status. ",
+    
+    arguments=[
+        ToolArgument(
+            name="accountkey",
+            type="string",
+            description="email address or account ID of user",
+        ),  
+         ToolArgument(
+            name="amount",
+            type="string",
+            description="amount requested for the loan",
+
 # ----- ECommerce Use Case Tools -----
 ecomm_list_orders = ToolDefinition(
     name="ListOrders",

workflows/agent_goal_workflow.py

@@ -108,7 +108,7 @@ class AgentGoalWorkflow:
                         conversation_history=self.conversation_history,
                         agent_goal=self.goal,
                     )
-                    validation_result = await workflow.execute_activity(
+                    validation_result = await workflow.execute_activity_method(
                         ToolActivities.agent_validatePrompt,
                         args=[validation_input],
                         schedule_to_close_timeout=LLM_ACTIVITY_SCHEDULE_TO_CLOSE_TIMEOUT,
@@ -134,7 +134,7 @@ class AgentGoalWorkflow:
                 prompt_input = ToolPromptInput(prompt=prompt, context_instructions=context_instructions)
 
                 # connect to LLM and execute to get next steps
-                tool_data = await workflow.execute_activity(
+                tool_data = await workflow.execute_activity_method(
                     ToolActivities.agent_toolPlanner,
                     prompt_input,
                     schedule_to_close_timeout=LLM_ACTIVITY_SCHEDULE_TO_CLOSE_TIMEOUT,
@@ -211,7 +211,7 @@ class AgentGoalWorkflow:
 
     #Signal that comes from api/main.py via a post to /confirm
     @workflow.signal
-    async def confirmed(self) -> None:
+    async def confirm(self) -> None:
         """Signal handler for user confirmation of tool execution."""
         workflow.logger.info("Received user signal: confirmation")
         self.confirmed = True
@@ -317,8 +317,9 @@ class AgentGoalWorkflow:
     async def lookup_wf_env_settings(self, combined_input: CombinedInput)->None:
         env_lookup_input = EnvLookupInput(
             show_confirm_env_var_name = "SHOW_CONFIRM", 
-            show_confirm_default = True)
+            show_confirm_default = True,
-        env_output:EnvLookupOutput = await workflow.execute_activity(
+        )
+        env_output:EnvLookupOutput = await workflow.execute_activity_method(
             ToolActivities.get_wf_env_vars, 
             env_lookup_input,
             start_to_close_timeout=LLM_ACTIVITY_START_TO_CLOSE_TIMEOUT,
@@ -363,9 +364,11 @@ class AgentGoalWorkflow:
     # also don't forget you can look at the workflow itself and do queries if you want
     def print_useful_workflow_vars(self, status_or_step:str) -> None:
         print(f"***{status_or_step}:***")
-        print(f"force confirm? {self.tool_data['force_confirm']}")
+        if self.tool_data:
-        print(f"next step: {self.tool_data.get('next')}")
+            print(f"force confirm? {self.tool_data['force_confirm']}")
-        print(f"current_tool: {self.tool_data.get('tool')}")
+            print(f"next step: {self.tool_data.get('next')}")
-        print(f"self.confirm: {self.confirmed}")
+            print(f"current_tool: {self.tool_data.get('tool')}")
-        print(f"waiting_for_confirm (about to be set to true): {self.waiting_for_confirm}")
+        else:
+            print("no tool data initialized yet")
+        print(f"self.confirmed: {self.confirmed}")