Your finance team is slow. They're stuck in spreadsheets, drowning in manual reports. You can't afford to wait days for answers you need in minutes.
AI this. AI that. We see it and hear it all over the place. But most companies are still running on manual processes that handcuff their ambitions. True financial AI workflow automation isn't about asking a chatbot a question. It's about building systems that do the work for you.
We've built AI workflows that cut processing time from days to minutes. We know what works. In this guide, I'll show you how to build a multi-agent financial workflow with Google's Agent Development Kit. You'll learn how to build agents that connect to data, run accurate simulations, and generate visual reports on command.
What is a multi-agent financial workflow?
A multi-agent financial workflow is a system of specialized AI agents that collaborate to complete complex financial tasks. It's not a single, all-knowing chatbot. It's a team of digital specialists. One agent retrieves data, another performs calculations, and a third creates a report.
The noise is deafening. The opportunity? Real. This isn't just about faster answers. It's about creating entirely new capabilities for your business.
From reactive operations that respond. To proactive systems that anticipate. After building dozens of these systems, I can say this with certainty: this approach is more robust, scalable, and easier to manage than any monolithic AI model.
Key concepts you need to understand
Here's what you need to know before you start building. These aren't abstract theories. They are the essential building blocks for a system that actually works.
Multi-agent system
A multi-agent system is a team of interacting AI agents. Each agent has a specialized skill. They collaborate to solve problems that are too complex for a single agent.
Why it matters: Without it, you build a monolithic agent that's a nightmare to debug and scale. With it, you create a modular, resilient workflow where each component does one thing well. Think of it as an assembly line, not a single artisan trying to do everything.
Agent-to-Agent (A2A) protocol
The A2A protocol is a secure communication standard. It allows your agents to discover and interact with other external agents. Your agent can essentially 'hire' another agent with a specific skill it doesn't have.
Why it matters: This is a massive shortcut. Instead of building a connection to a proprietary database from scratch, you can connect to a remote agent that already has access. It lets you augment your agent's skills instantly.
Code execution agent
A code execution agent is an AI agent that can write and run code - usually Python - to perform precise calculations. It doesn't guess the math. It calculates it.
Why it matters: LLMs are terrible at math. Relying on them for financial simulations is a recipe for disaster. A code execution agent ensures every calculation is 100% accurate, which is non-negotiable in finance.
Server-sent events (SSE) streaming
Server-Sent Events (SSE) is a technology that pushes real-time updates from the server to the user. It shows the agent's step-by-step thought process and actions as they happen.
Why it matters: This isn't just a fancy UI trick. It builds trust. By making the AI's reasoning transparent, you reduce perceived wait time and show users exactly how the system arrived at its conclusion.
How to build a financial AI workflow step by step
Let's be honest: most companies are still running on spreadsheets. Building custom AI workflows takes time and commitment. But the alternative - staying manual - costs you more every day.
Here's the five-step process we use to build these systems.
1. Design the multi-agent architecture
First, you need to map out your workflow. Don't just start coding. A bad process design is the number one reason AI projects fail. Not the technology.
- Define a 'root agent' as the main orchestrator that interacts with the user
- Design a 'handler agent' that delegates tasks to specialized sub-agents
- Create agents for specific functions: data retrieval, calculations, and visualization
- Draw the path of a request from user through all agents and back
Key action: Diagram this flow first. The visual map prevents countless headaches later.
2. Connect to external data with the A2A protocol
Your agent is useless without data. The A2A protocol lets you connect to remote agents that have access to specific data sources, like a database of financial transactions.
- The remote agent publishes a 'business card' that lists its capabilities
- Your handler agent calls this remote agent just like any other tool
- It's a plug-and-play approach to expanding your agent's knowledge
Key action: Implement the A2A connection to an external data agent. Wrap it as a tool so your handler agent can call it when needed.
3. Implement specialized agents for core tasks
Now you build your team of specialists. One agent, one job. This is where you get ground-level and build the core functions.
- Create a 'calculator agent' using the built-in Code Executor in the ADK
- This agent's only job is to run Python code for accurate math
- Build a 'graph agent' with tools like Matplotlib for visualization
- The graph agent takes structured data and turns it into visual output
Key action: Configure these two distinct agents. One for pure calculation, one for pure visualization. Don't mix their responsibilities.
4. Configure agent callbacks to save outputs
Your agent needs a memory. When the graph agent generates a chart, you need to save it. The ADK allows you to use 'after agent callbacks' to automatically save these outputs.
- Saved files are called 'artifacts' - images, CSV files, or JSON data
- By saving them, you create a stateful system
- The user can refer back to previously generated graphs or datasets
Key action: Set up a callback function that automatically saves any generated files to an artifacts directory.
5. Build a streaming front-end interface
The final step is connecting your agent system to a user interface. Don't just show a loading spinner and then the final answer. It feels slow and builds zero trust.
- Use the streaming API endpoint with Server-Sent Events (SSE)
- Show the user what the agent is doing in real-time
- Display messages like 'Calling remote agent for data...' and 'Running simulation...'
Key action: Set up an API route that connects to the agent's streaming endpoint and renders step-by-step updates in your UI.
Best practices and pro tips
Here's what we've learned building these systems for service companies. This isn't theory. This is what actually happens on the ground.
Use multi-agent systems to break down complexity
A single agent trying to manage data, calculations, and user interaction will fail. Assigning specialized roles makes the workflow robust and easier to debug.
Connect to remote agents with the A2A protocol
Don't reinvent the wheel. If an agent already exists that can securely access CRM data or a financial database, connect to it. Saves hundreds of hours.
Use a dedicated code execution agent for all calculations
This is not optional. LLMs hallucinate numbers. A code executor guarantees 100% accuracy for financial calculations.
Stream agent responses using SSE
A user waiting 15 seconds for a response thinks the system is broken. A user watching the agent work for 15 seconds thinks it's brilliant.
Save agent outputs as artifacts for reuse
This makes your agent stateful. It can refer to its own work, allowing for complex multi-turn conversations about generated charts.
Common mistakes that will kill your project
Most AI implementations fail. It's not because the technology is bad. It's because the design and execution are flawed.
This isn't easy. But it's possible. The biggest reason AI implementations fail is poor process design. Get the architecture right, and the technology will work.
Real-world applications for service companies
So what does this look like in practice? Here are three examples of how service companies are using these workflows right now.
Automated financial advisory & reporting
A financial advisor types 'Simulate client X's net worth over 10 years with a 7% annual return.' The root agent connects to client data. A calculator agent runs the simulation. A graph agent creates a line chart.
Result: A client-ready report generated in 60 seconds. Used to take an analyst two hours.
Dynamic project budgeting and forecasting
A project manager asks 'Forecast the budget for project Y if we add two more engineers in Q3.' The agent connects to HR and project data, simulates costs, and generates a revised budget chart.
Result: Leaders make go/no-go decisions on staffing in minutes, backed by accurate financial models.
Client sales performance analysis
A sales director says 'Show me the conversion rate for our enterprise team this quarter and forecast revenue.' The agent connects to CRM and financial systems, calculates the rate, and generates performance charts.
Result: Sales leadership gets a real-time view of performance without waiting for manual reports.
Key takeaways
- Multi-agent systems simplify complex workflows - break problems down into specialized tasks
- Connect remote agents with the A2A protocol - don't rebuild what you can connect to
- Use code execution for 100% accurate calculations - never trust an LLM with math
- Stream agent thoughts to build user trust - show the work, don't just deliver the answer
- Generate graphs for multi-modal outputs - an image is often better than a thousand words
- Save agent outputs as artifacts for reuse - create a stateful system that remembers
- You can build powerful agents in hours, not months - the tools are here
Manual processes handcuff your ambitions. AI workflows set you free. The technology exists. The proof is real. The choice is yours.
Frequently asked questions
A multi-agent AI workflow uses several specialized AI agents that collaborate to complete a complex task. For example, one agent retrieves data, another performs calculations, and a third creates a report. This modular approach is more robust and scalable than using a single, monolithic agent.
AI agents achieve mathematical accuracy by using a 'code executor' tool. Instead of relying on the language model, the agent writes and runs a small piece of code (e.g., Python) to get a precise answer. This is essential for reliable financial simulations and analysis.
The A2A protocol is a communication standard that allows different AI agents to interact and share capabilities securely. It lets developers connect their agent to external agents with pre-existing skills, like accessing a specific database, without having to build that functionality from scratch.
Streaming shows the user the AI's step-by-step reasoning and actions in real-time. This reduces perceived waiting time and builds trust by making the agent's process transparent, allowing users to see how the AI arrives at its final answer.
Yes, an AI agent can generate visual outputs like pie charts, bar graphs, and line charts. This is done by giving the agent access to data visualization tools (like Matplotlib) and instructions on how to use them. The generated charts can then be saved and displayed in a user interface.
Want help building this?
We've built dozens of AI workflows for service companies. If you're looking to automate your operations - and you want it done right - let's talk.