AWS released the AgentCore memory lifecycle management and AI-driven development process reference solution, aimed at addressing the accumulation of agent memory over time and code issues…
To address the decline in response quality and compliance risks caused by cumulative memory in Amazon Bedrock AgentCore, industry has introduced memory lifecycle management practices. This approach utilizes AgentCore memory, AWS StepFunctions, and Amazon Bedrock to create nightly workflows, aimed at handling support, sales, and IT agents with high amounts of interactive data. For low-traffic agents, only TTL expiration and GDPR compliance strategies are required. The core architecture includes three types of memory: Episodic memory for recording past conversations, which should have a shorter retention period; Semantic memory for extracting facts and preferences from interactions, suitable for long-term retention and integration; and Procedural memory for encoding workflow patterns, with the longest retention period and highest pruning threshold. Additionally, engineering teams use AgentCore to build A…
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AWS released the AgentCore memory lifecycle management and AI-driven development process reference solution, aimed at addressing the accumulation of agent memory over time and code issues…
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2026-09-03
AI-driven development lifecycle reference solution released
Engineering teams facing issues with conceptual frameworks and code disconnect when using Amazon Bedrock AgentCore to build AI-driven development lifecycle. This article provides two reference implementations to bridge this gap: one uses AgentCore runtime to generate Mermaid entities from SQL schemas…
2026-09-04
AgentCore memory lifecycle management practices released
To address the decline in response quality and compliance risks caused by memory accumulation, the industry has introduced memory lifecycle management practices, which involve systematically scoring, integrating, and trimming agent memory. This solution utilizes AgentCore memory, AWS StepFunctions, and Amazon Bedrock to build nightly work…
The engineering team faces the issue of disconnect between conceptual frameworks and code when using Amazon Bedrock AgentCore to build an AI-driven development lifecycle (AI-DLC). This article provides two reference implementations to bridge this gap: one involves using AgentCore’s runtime to generate Mermaid entity-relationship diagrams from SQL schemas; the other involves automated code security analysis through a multi-agent architecture that integrates AgentCore Gateway, memory, and external tools. Both solutions reflect the AI-DLC construction phase model, where AI is responsible for proposing architectures, generating plans, writing code, and creating deployment artifacts, while human oversight over key decisions remains intact. Both implementations are built on AgentCore and linked to a GitHub repository containing complete deployment instructions.
To address the decline in response quality and compliance risks caused by accumulated memory in Amazon Bedrock AgentCore, the industry has introduced memory lifecycle management practices, which systematically score, integrate, and trim agent memory. This approach utilizes AgentCore memory, AWS StepFunctions, and Amazon Bedrock to build nightly workflows, aiming to handle support, sales, and IT agents with high amounts of interactive data. For low-traffic agents, only TTL expiration and GDPR compliance strategies are required. The core architecture includes three types of memory: Episodic memory, which stores past conversations and requires shorter retention periods; Semantic memory, which captures facts and preferences from interactions and is suitable for long-term retention and integration; and Procedural memory, which encodes workflow patterns and has the longest retention period and highest pruning thresholds. Based on this…