temporal-ai-agent/adding-goals-and-tools.md

Customizing the Agent

The agent is set up to allow for multiple goals and to switch back to choosing a new goal at the end of every successful goal. A goal is made up of a list of tools that the agent will guide the user through.

Adding a Goal

1. Open /tools/goal_registry.py - this file contains descriptions of goals and the tools used to achieve them
2. Pick a name for your goal!
3. Fill out the required elements:

• id: needs to be the same as the name
• agent_name: user-facing name for the agent/chatbot
• agent_friendly_description: user-facing description of what the agent/chatbot does
• tools: the list of tools the goal will walk the user through.
• Important! The last tool listed must be list_agents_tool. This allows the AI to let the user go back to choosing from the list of available goals.
• description:
• starter-prompt:
• example_conversation_history:

4. Add your new goal to the goal_list at the bottom using goal_list.append(your_super_sweet_new_goal)

Adding Tools

Notes

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 the CalendarConflict tool/step of the goal_hr_schedule_pto goal:

description="Help the user gather args for these tools in order: "
    "1. CurrentPTO: Tell the user how much PTO they currently have "
    "2. FuturePTO: Tell the user how much PTO they will have as of the prospective date "
    "3. CalendarConflict: Tell the user what conflicts if any exist around the prospective date on a list of calendars. This step is optional and can be skipped by moving to the next tool. "
    "4. BookPTO: Book PTO "

Add to Tool Registry

• tool_registry.py contains the mapping of tool names to tool definitions (so the AI understands how to use them)
• name:
• description:
• arguments: These are the input arguments to the tool.

Create Each Tool

• The tools themselves are defined in their own files in /tools - you can add a subfolder to organize them
• The file name and function name will be the same as each other and should also be the same as the name of the tool, without "tool" - so future_pto_tool would be future_pto.py with a function named future_pto within it.
• The function should have args: dict as the input and also return a dict
• The return dict should match the output format you specified in the goal's example_conversation_history

Add to tools/__init__.py

• In tools/__init__.py, add an import statement for each new tool as well as an applicable return statement in get_handler. The tool name here should match the tool name as described in the goal's description field.

Configuring the Starting Goal

The agent can be configured to pursue different goals using the AGENT_GOAL environment variable in your .env file.

Goal: Find an event in Australia / New Zealand, book flights to it and invoice the user for the cost

• AGENT_GOAL=goal_event_flight_invoice (default) - Helps users find events, book flights, and arrange train travel with invoice generation
  • This is the scenario in the video above

Goal: Find a Premier League match, book train tickets to it and invoice the user for the cost

• AGENT_GOAL=goal_match_train_invoice - Focuses on Premier League match attendance with train booking and invoice generation
  • This is a new goal that is part of an upcoming conference talk

If not specified, the agent defaults to goal_event_flight_invoice. Each goal comes with its own set of tools and conversation flows designed for specific use cases. You can examine tools/goal_registry.py to see the detailed configuration of each goal.

See the next section for tool configuration for each goal.

Configuring Existing Tools

Agent Goal: goal_event_flight_invoice (default)

• The agent uses a mock function to search for events. This has zero configuration.
• By default the agent uses a mock function to search for flights.
  • If you want to use the real flights API, go to tools/search_flights.py and replace the search_flights function with search_flights_real_api that exists in the same file.
  • It's free to sign up at RapidAPI
  • This api might be slow to respond, so you may want to increase the start to close timeout, TOOL_ACTIVITY_START_TO_CLOSE_TIMEOUT in workflows/workflow_helpers.py
• Requires a Stripe key for the create_invoice tool. Set this in the STRIPE_API_KEY environment variable in .env
  • It's free to sign up and get a key at Stripe
  • If you're lazy go to tools/create_invoice.py and replace the create_invoice function with the mock create_invoice_example that exists in the same file.

Agent Goal: goal_match_train_invoice

• Finding a match requires a key from Football Data. Sign up for a free account, then see the 'My Account' page to get your API token. Set FOOTBALL_DATA_API_KEY to this value.
  • If you're lazy go to tools/search_fixtures.py and replace the search_fixtures function with the mock search_fixtures_example that exists in the same file.
• We use a mock function to search for trains. Start the train API server to use the real API: python thirdparty/train_api.py
• • The train activity is 'enterprise' so it's written in C# and requires a .NET runtime. See the .NET backend section for details on running it.
• Requires a Stripe key for the create_invoice tool. Set this in the STRIPE_API_KEY environment variable in .env
  • It's free to sign up and get a key at Stripe
  • If you're lazy go to tools/create_invoice.py and replace the create_invoice function with the mock create_invoice_example that exists in the same file.