Accelerating M&A due diligence with Amazon Bedrock AgentCore

Mergers and acquisitions teams encounter difficulties when conducting exhaustive due diligence on potential acquisition targets while ensuring efficiency and rigorous analysis. Traditionally, they allocate weeks to manually assess targets before determining viable prospects. Amazon Bedrock AgentCore, a platform designed to construct, link, and refine agents on a large scale, can expedite this procedure by managing AI agents that autonomously gather data, evaluate analyses, and perform compliance checks within predefined boundaries.

This article demonstrates how to construct a multi-agent due diligence system using Amazon Bedrock AgentCore and provides a reference architecture combining agent orchestration, information retrieval, and governance controls, followed by a guide for deploying and executing the solution via a comprehensive sample repository.

The challenge faced by M&A analysts is significant, particularly in the transportation and logistics domain. They gather information from financial databases, market research tools, regulatory filings, and internal data repositories, subsequently reconciling the information manually. This process is not only time-consuming and resource-heavy but also prone to duplicated effort.

For instance, industry analyses, valuation models, and competitive assessments are often recreated for each new deal rather than building upon prior knowledge gained from previous transactions. Additionally, governance concerns often hinder AI integration due to the necessity for legal and compliance teams to verify the accuracy, traceability, and source-cited nature of AI-generated insights.

These four challenges (prolonged cycles, fragmented data, redundant work, and governance requisites) provide a clear pathway through which AI agents can revolutionize the due diligence process.

AI agents tackle these challenges by combining autonomous data acquisition, intelligent distribution, persistent memory, and inherent traceability. On the information gathering side, agents independently query various sources, including financial databases, knowledge bases, and market data APIs, synthesizing the retrieved data into preliminary evaluations.

In our experiments, tasks that typically consumed weeks of analyst time were completed within hours, as agents manage the repetitive loop of searching and summarizing without human intervention. For prioritizing targets, agents assess opportunities based on strategic criteria and direct high-priority deals to the appropriate experts.

Instead of reviewing each target sequentially, the system highlights the most promising candidates and identifies specific areas (financial performance, strategic relevance, compliance risks) that require further scrutiny. Agents also contribute to institutional memory; each completed analysis updates a shared knowledge base, enabling future deals to benefit from previous research, valuation assumptions, and integration insights, thereby avoiding the need to recreate context from scratch.

Governance is inherently integrated into the agent workflow from the outset rather than being applied retrospectively. The system is configured so that every assertion made by the agent is supported by a citation, and validated through a citation-checker. Additionally, each agent invocation generates an audit trail, and runtime guardrails enforce organizational policies. Governance teams can track and verify each cited claim back to its original source document.

The article presents two implementation strategies, each tailored to distinct organizational requirements. The first option, Amazon Quick, offers ready-to-use AI capabilities specifically designed for business intelligence and research tasks. It provides a swift route to production if your requirements align with standard analytical patterns.

Amazon Quick Research generates comprehensive, exportable reports, Amazon Quick Flows automate repetitive tasks through pre-built workflows, and Amazon Quick Index enables unified searching across connected data sources. Adopt the Amazon Quick platform if you seek rapid deployment, your workflows fit standard business intelligence models, and prefer fully managed services over custom development.

For more information and a reference architecture, refer to the announcement on Amazon Quick. Customization of Quick is also possible using Amazon Bedrock AgentCore agents for specific needs, such as proprietary valuation models or strategic fit assessments. An illustrative example of this approach can be found in the article "Building Intelligent Contract Management with Quick and AgentCore," which illustrates how Amazon Quick manages document management and user interfaces while Amazon Bedrock AgentCore handles specialized agent collaboration.

The second option involves a custom architecture built around Amazon Bedrock AgentCore. This configuration provides precise control over agent behavior, memory, and coordination, making it suitable for M&A teams with proprietary methodologies, intricate multi-agent coordination demands, integration with specialized internal systems, or a requirement for full control over agent behavior and model selection.

The remainder of this article delves into this custom architecture through the lens of a transportation and logistics due diligence case study. We deploy a functional multi-agent system, test it against simulated acquisition targets, and validate the outputs using an automated citation-checker. The reference architecture for M&A teams opting for the custom route illustrates the end-to-end integration of various components.

Figure 1 presents the multi-agent M&A due diligence architecture utilizing Amazon Bedrock AgentCore. The system employs AgentCore to orchestrate a multi-agent due diligence workflow, with a supervisor agent managing four specialized agents, each tasked with a distinct phase of the due diligence process. User interaction occurs through a Jupyter notebook or terminal interface, while AgentCore manages agent execution and collaboration.

The architecture combines Retrieval Augmented Generation (RAG), structured financial analysis, and integration with external tools.