How Schroders built its multi-agent financial analysis research assistant

Financial analysts spend hours grappling with ever-increasing volumes of market and company data to extract key signals, combine diverse data sources, and produce company research. Schroders is a leading global active investment manager. Being an active manager means understanding investment opportunities — combining rigorous research, innovative thinking and deep market perspective — to help build resilience and capture returns for clients. 

To maximise its edge as an active manager, Schroders wants to enable its analysts to shift from data collection to the higher-value strategic thinking that is critical for business scalability and client investment performance. 

To achieve this, Schroders and Google Cloud collaborated to build a multi-agent research assistant prototype using Vertex AI Agent Builder.

Why multi-agent systems?

At Schroders, analysts are typically responsible for conducting in-depth research on 20 to 30 companies, with another 20 under close watch. An initial report on a new company can take days to complete, most of which are primarily spent gathering quality data. Reducing this research down to minutes would allow analysts to screen more companies, directly increasing their potential to discover promising investment opportunities for their clients. An AI assistant offers a significant productivity boost in driving early-stage company research.

An AI agent is a software system that can perceive its environment, take actions, and employ tools to achieve specific goals. It shows reasoning, planning, and memory, and has a level of autonomy to make decisions, learn, and adapt. Tools are crucial functions or external resources that an agent can utilize to interact with its environment and enhance its capabilities, enabling them to take actions on a user’s behalf. 

Standalone generative AI models often struggle with complex, multi-step financial research workflows, which require ordered data retrieval and reasoning (i.e., fetching fundamentals, filings, and news, and then synthesizing analysis). Given the complexity of its use case, Schroders opted to build a multi-agent system due to the following characteristics:

• Specialization: Designing agents which are hyper-focused on specific tasks (e.g., R&D Agent, Working Capital Agent, etc.) with only the necessary tools and knowledge for their respective domains.

• Modularity and scalability: Each agent is a distinct component developed, tested, and updated independently thereby simplifying development and debugging.

• Complex workflow orchestration: Multi-agent systems model their workflows as graphs of interacting agents. For example, a Porter’s 5 Forces Agent designed to identify and analyze industry competition, could trigger child agents like a Threat of New Entrants Agent, in parallel or sequence, to better manage dependencies between deterministic (e.g., calculations) and non-deterministic (e.g., summarization) tasks.

• Simplified tool integration: Specialized agents can handle specific toolsets (i.e., an R&D Agent using SQL database query tools) rather than having a single agent manage numerous APIs.

Leveraging Vertex AI Agent Builder

Schroders selected Vertex AI Agent Builder as the core platform for developing and deploying its multi-agent system. This choice provided several key benefits that helped accelerate development, including access to state-of-the-art Google foundation models like Gemini and pre-built connectors for various tools and data sources.

For example, Vertex AI Agent Builder provided easy tool integration for leveraging:

Internal knowledge: Grounding with Vertex AI Search tool was leveraged to ground Gemini to private document corpus, such as internal research notes, using, enabling agents to answer questions based on Schroder’s proprietary data.

• Example tool call: search_internal_docs(query="analyst notes for $COMPANY", company_id="XYZ").

Structured data: To simplify financial data querying for analysts in BigQuery, agents employed a custom tool to translate natural language into SQL queries.

• Example flow: User: "What were $COMPANY's revenues for the last 3 quarters?" -> Agent -> SQL Query on BigQuery.

Public web data: The team integrated Grounding with Google Search tool for handling real-time public information like news and market sentiment.

• Example tool call: google_search(query="latest news $COMPANY stock sentiment").

Vertex AI’s flexible orchestration supports both native function calling and frameworks like LangGraph, CrewAI, and LangChain, allowing the team to prototype its multi-agent system with function calling before transitioning to a specific framework. In addition, Vertex AI offers seamless integration with other Google Cloud services and tools that help facilitate rapid agent governance and management, including Cloud Logging, Cloud Monitoring, IAM Access Control, Vertex AI evaluation, BigQuery and more.

The evolution of the Vertex AI to support building multi-agent systems, including the latest Agent Development Kit (ADK) and Agent-to-Agent (A2A) protocol, offers future opportunities to further streamline agent development, productization, and integration with existing agent deployments.

Framework choices and implementation tradeoffs

One of the most critical decisions was framework selection for agent orchestration. Initially, native function calling helped Schroders get familiar with Vertex AI Agent Builder and develop agent-building best practices. This approach kept things simple to start with and allowed finer-grained control and reliability over agent interactions and tool invocation, providing easier debugging and faster iterative development for simple, linear agent design and workflows. However, it also required significant custom code to manage state and errors, track dependencies, and handle retry and control logic — all of which created significant complexity.

With a solid foundation in individual agents, Schroders decided to explore integrating multiple agents for achieving complex tasks and quickly recognized the need for a framework for better workflow state and inter-agent dependency management. Subsequently, the team transitioned to LangGraph, an open-source, multi-agent framework, primarily for its state management capabilities, native support for cyclical complex workflows and human in the loop checkpoints, which allow an agent to complete a task, update the state, and pass it to the configured sub agent.  The adopted parent-child graph structure requires managing both parent and child agent states; child agents complete tasks with the parent graph leading the orchestration. This structured hierarchy often ends with a “summary” node aggregating child results. Each child stores its tool calls and AI messages before writing its final output to the parent.

Key features and system architecture deep dive

Schroder’s multi-agent system is designed for intuitive, flexible end-user interaction. An analyst creates an agent by providing a name, a description, prompt template sections (e.g., objective, instructions, constraints), and selecting tools. For example, an agent that receives the user query, “Summarize recent earnings and news sentiment for Company X, highlighting any changes in management guidance,” would need access to company documents and market news tools. Agent configurations are versioned in Firestore, ensuring robust management for Create, Read, Update, and Delete (CRUD) operations.

A “quick chat” function allows users to smoke-test agents and tweak prompts. Tested agents join a pool of available agents, which users can then combine agents into “workflows” — directed graphs for multi-step processes. For instance, a Porter’s 5 Forces analysis agent will use pre-built agents and tools like Vertex AI AutoSxS Model Evaluation alongside child agents that integrate current information or internal document insights.

The following diagram illustrates the Google Cloud architecture for orchestring agents:

Here is an example query flow:

1. Router agent: Receives the user query, uses Gemini to classify intent and identify the target specialized agent or workflow (e.g., “Analyze Company XYZ” routes to the Porter’s 5 Forces Agent).

2. Task delegation: The router requests parameters and routes to the appropriate agent and workflow.

3. Agent execution and tools: Specialized agents execute tasks, interacting with configured tools, such as APIs and databases via secure gateways.

4. Response: Combined results from workflows or individual agent responses are returned.

5. Follow-ups: Conversation history is stored in Firestore, maintaining full context.

Here is an example workflow for a user wanting to analyse a company:

This distributed approach ensures each component focuses on its strength, providing vital flexibility that encourages user adoption.

Personalization and user adaptation

Personalization was key as a core goal of Schroder’s use case was supporting analysts’ unique workflows, not forcing rigid processes. To achieve this, the system uses customizable system instructions — underlying prompts that can be tuned by analysts and developers. A templating system gives developers control of generalized prompt parts and analysts over the business logic, helping to foster cross-functional collaboration. In addition, the system allows for personalized agent configuration. Analysts can prioritize or toggle on different tools and data sources depending on the research context. These tools are developer-built, restricting direct access to any underlying files like PDF documents. The team also decided to expose model parameters like temperature, allowing users to make small adjustments and modifications during development.

Measuring success: Agent evaluation and iteration

An agentic system is only valuable if it’s accurate, reliable, and truly helpful. These attributes are also important in generating quality investment research, which is vital for client trust and Schroders’ active capability. To address this, Schroders implemented a multi-faceted evaluation strategy, using Vertex AI Generative AI Evaluation. This approach includes:

• Automated metrics: Tracking task success, tool accuracy, latency, and resource use.

• Human-in-the-loop (HITL): Analysts review outputs for accuracy, relevance, completeness, and conciseness.

• A ground truth dataset: This dataset is built based on structured analyst feedback, including corrections and data source indications.

• Iterative refinement: Data is fed back into development to refine prompts, tool descriptions, orchestration logic, and identify needs for new agents, rapidly improving performance and trust.

Building a new financial future

Working together, Schroders and Google Cloud developed a successful prototype with Vertex AI Agent Builder, demonstrating that multi-agent systems are capable of tackling complex financial workflows. By combining specialized agents, good architecture and robust evaluation, the collaboration proved the feasibility of developing an equity research AI research assistant that can enhance analyst productivity significantly — reducing the time required to complete a detailed company analysis from days to minutes.

Along the way, the team also discovered several key learnings for building effective agents:

• Meticulously decompose tasks. Thoroughly map analyst workflows, breaking them into the smallest logical, atomic units for clear multi-agent roles. Single-task agents are more effective at accomplishing their defined objective.

• Prompt engineering is key. Generative AI foundation models rely heavily on tool descriptions, and ambiguity can impact reliability. Effective prompts, especially precise tool descriptions, are critical.

• Tool reliability is non-negotiable. Agents are limited by their tools. Instability and bugs in tools can degrade performance and lead to incorrect outputs, which can then impact investment decision making. Implement robust error handling (retries, circuit breakers) and ensure good tool debugging.

• Limit tool scope per agent. Agents perform better with fewer (e.g. less than five) highly relevant tools to avoid misuse.

• Managing state is complex. Orchestrating multiple agents demands careful management of history and careful tracking of intermediate results. Frameworks like LangGraph or ADK can help significantly.

• Leverage Agent-of-Agents. Power comes from collaboration, not overly complex individual agents. For complex tasks, it’s better to build single-responsibility, reusable atomic agents that can work together, carefully orchestrating their interactions.

• User trust is earned. Always be transparent and consistent. High-quality, user feedback is essential for driving results that gain user trust and engagement.

In order to scale the prototype in the future, Schroders plans to explore more agents with sophisticated reasoning, support for new multimodal data types like images and charts, enhanced discoverability (Agent-to-Agent protocols), and more autonomy for routine tasks. 

To learn more…

• Build your multi agent system with Vertex AI Agent Builder today!

• ADK/ Agent Engine/ A2A: Unlock collaborative agents