Building an AI-powered contract intelligence platform with Amazon Quick and Amazon Bedrock AgentCore

The post outlines an AWS‑based contract intelligence platform that leverages Amazon Bedrock AgentCore to orchestrate AI agents for extracting and verifying contract fields, then answering both aggregate portfolio‑wide and single‑contract queries. By moving beyond traditional RAG chat tools, the architecture incorporates Amazon Quick for data ingestion and Bedrock AgentCore for agent coordination, enabling scalable, end‑to‑end contract processing. The design trades off added orchestration complexity for the ability to handle portfolio‑wide analytics that RAG alone cannot support, and it requires custom agent logic to adapt to varied contract formats.

⚡ Key Takeaways

• Uses Amazon Bedrock AgentCore to orchestrate agents that parse and verify contract fields.
• Integrates Amazon Quick for ingesting contract data into the pipeline.
• Tradeoff: Agent orchestration adds latency and operational overhead compared to a single RAG model.
• Integration step: invoke AgentCore’s API endpoint with contract text to receive structured field output.
• Limitation: The system depends on custom agent logic and may struggle with contracts that deviate significantly from the training data.
• WhyItMatters: Engineers building production AI for enterprise legal workflows can

Engineers building production AI for enterprise legal workflows can

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