Amazon Bedrock AgentCore is a platform designed to build, connect, and optimize AI agents at scale, with any framework or model. It accelerates the due diligence process by orchestrating AI agents that autonomously handle data gathering, analysis, and compliance checks within defined guardrails. The solution enables M&A teams to conduct thorough reviews of multiple acquisition targets while maintaining speed and analytical rigor. This approach addresses the persistent challenge of manually reviewing targets, which often takes weeks of analyst time, and replaces it with an automated system that significantly reduces the workload. The system is tailored for business intelligence and research workflows, offering a fast path to production for organizations with standard analysis patterns. It also supports customization for specialized requirements such as proprietary valuation models or strategic fit frameworks. The solution combines agent orchestration, knowledge retrieval, and governance controls to support production-ready workflows. The reference architecture demonstrates how the platform integrates with internal systems and manages multi-agent coordination, ensuring compliance and auditability throughout the process.
The platform addresses key challenges in the M&A due diligence process, including slow cycles, fragmented data, duplicated effort, and governance requirements. AI agents autonomously gather data from financial databases, market data APIs, and internal knowledge bases, synthesizing the results into preliminary assessments. In testing, work that previously required weeks of analyst time was completed in hours, as the agent handles the repetitive search-and-summarize loop without human intervention. Agents also evaluate opportunities against strategic criteria, routing high-priority deals to the right specialists. The system surfaces the most promising candidates first and flags specific dimensions such as financial performance, strategic fitness, and compliance risk for deeper analysis. Additionally, the platform builds institutional knowledge over time, enriching a shared memory layer so that future deals benefit from prior research and integration lessons. Governance is built into the agent workflow from the start, ensuring each assertion is grounded in a citation and validated by an evaluator. Compliance teams can trace each claim back to its source document, ensuring transparency and accuracy.
The reference architecture for the custom implementation shows how the platform integrates with internal systems and manages multi-agent coordination. The architecture uses Amazon Bedrock AgentCore to orchestrate a multi-agent due diligence workflow, with a supervisor agent coordinating four specialist agents, each responsible for a distinct phase of the due diligence process. Users interact through a Jupyter notebook or terminal interface, while the AgentCore runtime manages agent execution and collaboration. The system combines Retrieval Augmented Generation (RAG), structured financial analysis, external tool integration, memory, and policy-based governance to support production-ready workflows. The platform also includes a supervisor agent that uses the agents-as-tools pattern to route requests to domain-specific specialists based on task requirements. The Target Screening Agent identifies acquisition candidates by converting natural-language queries into SQL and executing them against Amazon Aurora PostgreSQL. The Financial Analysis Agent performs valuation analysis using structured and unstructured data sources, applying standard methodologies such as discounted cash flow (DCF) analysis and comparable company analysis. The Strategic Fit Agent evaluates integration risks, synergies, and organizational alignment, retrieving context from prior transactions stored in AgentCore memory. The Compliance Validation Agent audits responses against the M&A governance checklist, invoking a custom citation-check evaluator implemented as an AWS Lambda function to examine every factual claim in a response and flag assertions that lack a supporting source citation.
Source: awsml
