Building Cloud Messaging Architecture With AWS

Your AI chatbot forgets everything when a user switches between messaging platforms. Here's how to fix that. Most chatbots treat each channel as a separate world. A user shares a photo on WhatsApp, then asks about it on Instagram. The agent has no idea what they're talking about. I built a multichannel AI agent that maintains persistent memory across messaging platforms using Amazon Bedrock AgentCore. One deployment, shared identity, full context. The demo uses WhatsApp and Instagram, but the architecture extends to any messaging channel: Slack, Telegram, Discord, SMS. How it works: → Unified identity: deterministic user IDs per channel (wa-user-{phone}, ig-user-{sender_id}) mapped to a single actor in AgentCore Memory. Adding a new channel means adding one more ID pattern. → Two memory layers: short-term (conversation turns with TTL) and long-term (extracted facts, preferences, summaries that persist indefinitely) → Multimodal processing: text, images (Claude vision), voice (Amazon Transcribe), video (TwelveLabs), and documents → Smart buffering: DynamoDB Streams with 10-second tumbling windows batch rapid messages before invoking the agent The architecture uses three AWS CDK stacks: Stack 00 → AgentCore Runtime + memory layer Stack 01 → WhatsApp (AWS End User Messaging) or Stack 02 → Multi-channel API Gateway (WhatsApp + Instagram + any new channel) Users can even link their accounts across platforms through conversation. The agent merges identities in a unified DynamoDB table. The core memory and identity layers are channel-agnostic. WhatsApp and Instagram are the first two integrations, but the pattern is designed to grow. Full code and deployment guide are open source. Each stack deploys in about 15 minutes #AI #Chatbot #Agents #AWS #LLM

Modern financial operations demand the ability to process millions of invoices daily, with low latency, high availability, and real-time business visibility. Traditional monolithic systems struggle to keep up with the surges and complexity of global invoice processing. By adopting an event-driven approach, organizations can decouple their processing logic, enabling independent scaling, real-time monitoring, and resilient error handling. Amazon Simple Queue Service (#SQS) and Amazon Simple Notification Service (#SNS) enable resilience and scale in this architecture. SNS acts as the event router and broadcaster in this architecture. After events are ingested (via API Gateway and routed through EventBridge), SNS topics are used to fan out invoice events to multiple downstream consumers. Each invoice status—such as ingestion, reconciliation, authorization, and posting—gets its own SNS topic, enabling fine-grained control and filtering at the subscription level. This ensures that only relevant consumers receive specific event types, and the system can easily scale to accommodate new consumers or processing requirements without disrupting existing flows. Each SNS topic fans out messages to one or more SQS queues. SQS provides the critical function of decoupling the event delivery from processing. This means that even if downstream consumers (like AWS Lambda functions or Fargate tasks) are temporarily overwhelmed or offline, no events are lost—SQS queues persist them until they can be processed. Additionally, SQS supports dead-letter queues (DLQs) for handling failed or unprocessable messages, enabling robust error handling and alerting for operational teams. Specific to resilience and scale, look at these numbers.... • Massive Throughput: SNS can publish up to 30,000 messages per second, and SQS queues can handle 120,000 in-flight messages by default (with quotas that can be raised). This supports surges of up to 86 million daily invoice events. • Cellular Architecture: By partitioning the system into independent regional “cells,” each with its own set of SNS topics and SQS queues, organizations can scale horizontally, isolate failures, and ensure high availability. • Real-Time Monitoring: The decoupled, event-driven flow—powered by SNS and SQS—enables near real-time dashboards and alerting, so finance executives and auditors always have up-to-date visibility into invoice processing status. #financialsystems #cloud #data #aws https://lnkd.in/gNnYpeu7

𝗔𝗪𝗦 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: 𝗧𝗵𝗲 𝗧𝗼𝗼𝗹𝘀 𝗧𝗵𝗮𝘁 𝗕𝘂𝗶𝗹𝗱 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗔𝘀𝘆𝗻𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 I've seen teams struggle with event-driven systems because they focus on architectural patterns instead of mastering the tools that actually deliver reliable async communication. Here's what works: 𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗘𝘃𝗲𝗻𝘁 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻  • 𝗘𝘃𝗲𝗻𝘁𝗕𝗿𝗶𝗱𝗴𝗲: Event bus that routes events between services with filtering and transformation rules  • 𝗦𝗡𝗦: Publish events to multiple subscribers with at least once delivery depending on the subscriber protocol. 𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 & 𝗤𝘂𝗲𝘂𝗶𝗻𝗴  • 𝗦𝗤𝗦: Decouple services and handle spikes with durable 'at least once' delivery, with FIFO queues supporting exactly-once semantics  • 𝗟𝗮𝗺𝗯𝗱𝗮: Event-driven compute that processes events at any scale without server management 𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗘𝘃𝗲𝗻𝘁𝘀  • 𝗞𝗶𝗻𝗲𝘀𝗶𝘀 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝗲𝗮𝗺𝘀: Real-time event streaming for high-throughput data pipelines  • 𝗗𝘆𝗻𝗮𝗺𝗼𝗗𝗕 𝗦𝘁𝗿𝗲𝗮𝗺𝘀: Capture database changes as events for real-time processing 𝗣𝗵𝗮𝘀𝗲 𝟰: 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴  • 𝗦𝘁𝗲𝗽 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: Coordinate multi-step workflows with automatic error handling and retries  • 𝗖𝗹𝗼𝘂𝗱𝗪𝗮𝘁𝗰𝗵: Monitor event patterns, failed deliveries, and processing latencies 𝗧𝗵𝗲 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: Reliable async systems are built on simple event patterns, not complex architectural frameworks. These eight tools handle the majority of event-driven requirements without custom infrastructure. Start with EventBridge for event routing and schema management, and SNS for multi-protocol pub/sub. Add SQS when you need buffering and Lambda for processing.  Use Step Functions only when workflows require coordination across multiple services. The biggest event-driven wins come from designing loosely coupled services that communicate through well-defined events. What's your experience with AWS event-driven architecture? Are you building reliable async systems or fighting event complexity? #AWS #awscommunity #kubernetes #CloudNative #DevOps #Containers #TechLeadership