Multi-agent architecture

Enterprise Multi-Agent Financial Intelligence

A production-oriented AI system implementing Router Agents, Task Agents, RAG pipelines, and Agent-to-Agent coordination.

Backend
FastAPI async
Deploy
Cloud Run
RAG
Chroma
Ops
Structured logs
System Overview
Router Agent
Finance Analyst
RAG Policy
Tool Agent
FastAPI
Vector Store
LLM
Observability
This UI is a preview. The full agent orchestration runs on the backend.
Architecture

System Components

A modular, production-oriented multi-agent architecture designed for enterprise workflows.

Agent

Router Agent

Classifies intent and orchestrates A2A coordination across specialized agents.

  • Intent routing
  • Task decomposition
  • Response aggregation
Agent

Finance Analyst Agent

Runs analysis over transactional datasets and highlights anomalies and patterns.

  • Rule-based anomaly checks
  • Metrics summaries
  • Report generation
RAG

RAG Policy Agent

Answers with grounding using company policies and finance playbooks indexed in a vector store.

  • Chroma vector store
  • Citations-ready context
  • Policy Q&A
Agent

Tool Agent

Executes tools safely (ERP mock APIs, SQL, validators) and returns structured outputs.

  • Tool calling
  • Input validation
  • Safe execution boundaries
Backend

FastAPI Backend

Async API surface for chat + agent workflows with clean separation of layers.

  • /v1/chat endpoint
  • Async services
  • Clean Architecture
RAG

Vector Store

Local-first retrieval for MVP. Designed to swap to managed vector DB later.

  • Document ingestion
  • Embedding pipeline
  • Pluggable interface
Backend

LLM Provider

LLM inference via provider API. Prompt versioning and guardrails supported.

  • Prompt templates
  • Token budgeting
  • Fallback strategy
Ops

Observability

Structured logs and tracing-friendly metadata for production diagnosis.

  • request_id correlation
  • JSON logs
  • Error taxonomy
Design note
Each component is replaceable behind interfaces. The MVP uses Chroma locally, but the same contracts support managed vector databases or Cloud SQL + pgvector when scaling.
Coordination

Agent-to-Agent Orchestration

A deterministic, production-friendly flow where the Router Agent owns the plan and delegates to specialized agents and tools.

Execution Flow
User → Router → Agents → Tools → Output
User
1
User Input
Natural language request enters the system through the /v1/chat API surface.
Router
2
Router Agent
Classifies intent, selects agent plan, and coordinates A2A handoffs.
Agents
3
Task Agents
Finance, RAG Policy, and Tool agents execute their specialized tasks in parallel/sequence.
Tools
4
Tool Execution
Structured tool calls (ERP mock API, validators, queries) run with strict boundaries.
Output
5
Response Aggregation
Router composes a final answer with structured results and (optionally) citations.
Tech Stack

Production-Oriented Stack

Chosen to demonstrate deployability, modularity, and enterprise-safe agent orchestration in an MVP timeline.

Core
FastAPI (async)
Async API + orchestration surface
Python 3.11+
Typed services + tooling
Infra
Docker
Reproducible builds and local dev
Google Cloud Run
Serverless container runtime
AI
Chroma (Vector Store)
Local-first semantic retrieval
RAG Pipeline
Policy grounding and citations-ready context
LLM Provider API
Inference + tool calling
Quality
Prompt Versioning
Iterate safely across releases
Structured Logging
JSON logs with request_id
Basic Tracing Hooks
Correlation across agent steps
Demo

Try the Assistant

Front-end chat wired to /v1/chat. Supports mock mode, trace IDs, tool events, and citations.

Demo Chat
Online
Assistant
Hi. This is a demo UI. Ask about expenses, anomalies, or policies — responses are mocked for now.
1771518693537