Using AI to streamline financial processes is increasingly becoming vital in today’s fast-paced market. One such avenue is through the use of Google’s Agent-to-Agent (A2A) protocol with the python-a2a library. This allows financial agents to communicate seamlessly and provides a standardized format to eliminate custom integration headaches. Let’s explore how you can create AI agents for financial tasks, specifically focusing on an EMI calculator and an inflation-adjusted amount calculator.
Understanding the Target Audience
The primary audience for this guide includes:
- Data Scientists and AI Developers: Interested in integrating AI solutions into financial services.
- Business Analysts: Looking to automate financial calculations and enhance decision-making.
- Financial Institutions: Seeking to improve services through AI-driven tools.
Common struggles include integrating AI systems, managing complex data flows, and ensuring precise calculations. This tutorial aims to assist these professionals in streamlining financial processes using AI.
Getting Started with python-A2A
Our journey begins with the installation of the python-a2a library. This library simplifies the building and running of agents aligned with the A2A protocol. To install it, open your terminal and run:
pip install python-a2aCreating the Financial Agents
EMI Agent
The EMI Agent will be responsible for calculating Equated Monthly Installments (EMI) based on principal amounts, interest rates, and loan durations. Below is the implementation:
from python_a2a import A2AServer, skill, agent, run_server, TaskStatus, TaskState
import re
@agent(
name="EMI Calculator Agent",
description="Calculates EMI for a given principal, interest rate, and loan duration",
version="1.0.0"
)
class EMIAgent(A2AServer):
...
This code snippet demonstrates the structure using decorators for defining the agent and the EMI calculation skill. The skill processes input, performs calculations, and translates the results into user-friendly text.
Inflation Agent
Next, we will create the Inflation Agent to help users understand how an amount changes with inflation over time. Here’s how this agent is crafted:
from python_a2a import A2AServer, skill, agent, run_server, TaskStatus, TaskState
import re
@agent(
name="Inflation Adjusted Amount Agent",
description="Calculates the future value adjusted for inflation",
version="1.0.0"
)
class InflationAgent(A2AServer):
...
This agent also follows the same structure, with its unique skill for managing inflation-related queries.
Creating the Agent Network
After developing both agents, they need to be executed simultaneously to form a network:
python emi_agent.py
python inflation_agent.py
This sets up two separate REST API endpoints for each agent, which can be accessed at http://localhost:4737 for EMI and http://localhost:4747 for inflation calculations.
Routing Queries to Agents
To ensure that queries are directed to the appropriate agent, we’ll create an agent network and a router:
from python_a2a import AgentNetwork, A2AClient, AIAgentRouter
network = AgentNetwork(name="Economics Calculator")
network.add("EMI", "http://localhost:4737")
network.add("Inflation", "http://localhost:4747")
router = AIAgentRouter(
llm_client=A2AClient("http://localhost:5000/openai"),
agent_network=network
)
By routing queries intelligently, you can leverage the strengths of each agent based on the inquiry’s content.
Practical Example Queries
With your agents and network set up, you can now test their functionality:
query = "Calculate EMI for ₹200000 at 5% interest over 18 months."
agent_name, confidence = router.route_query(query)
print(f"Routing to {agent_name} with {confidence:.2f} confidence")
response = agent.ask(query)
print(f"Response: {response}")
query = "What is ₹1500000 worth if inflation is 9% for 10 years?"
agent_name, confidence = router.route_query(query)
response = agent.ask(query)
print(f"Response: {response}")
Each query will be routed to the correct agent, and the response returned can be promptly processed.
Conclusion
Using the python-a2a library alongside Google’s A2A protocol greatly simplifies the integration of AI agents in financial services. This guide has taken you from initial setup to creating, connecting, and deploying agents capable of handling complex financial calculations. Embracing these technologies can significantly empower financial institutions to deliver improved services and insights, ultimately paving the way for a more efficient future. Dive into this AI-driven world and see your financial processes transform!
