How to Apply Amazon Bedrock Agents in R&D

GenAI Architecture – Week 9 Project 9: Building Multimodal + Voice Agents at Scale (MCP Unified Stack) If you’ve been following this journey, you know how each week built on the last — from setting up local agents to orchestrating enterprise RAG systems and federated data pipelines. By Week 9, everything finally came together. This was the week we gave our agents the ability to see, listen, reason, and speak — all in one place. 🎯 The Challenge: Most multimodal or voice AI demos you see online are cool but disconnected — a chatbot here, a vision model there, a voice transcriber somewhere else. But in real-world enterprises, you need something unified — a single system that can: 🎙 Listen 🖼 See 🧩 Reason 🗣 Speak … and do it all within one orchestrated environment. 🧩 The Architecture Here’s how this unified setup works: 1️⃣ User Interface Layer The experience starts at the front — voice, camera, or chat inputs through a FastAPI or Streamlit app powered by the MCP SDK. 2️⃣ MCP Agent Orchestrator Built on AWS Bedrock AgentCore, this layer coordinates between vision, audio, and reasoning agents — ensuring context flows seamlessly. 3️⃣ Modular Agent Suite 🎙 Speech Agent – Whisper or Amazon Transcribe (speech-to-text) 🖼 Vision Agent – Claude or Nova (multimodal image reasoning) 🧠 Reasoning Agent – Core logic chain using Claude 3 or Nova 🗣 Response Agent – Amazon Polly or EdgeTTS for natural voice output 4️⃣ Data + Integration Layer Unified APIs (via MindsDB, Vector DB, or RAG engine) provide real-time context, while S3 + DynamoDB store memory and results for continuity. ⚡ Why This Matters This architecture breaks the silos. It lets voice, vision, and reasoning work together — dynamically. Bedrock AgentCore handles context and tool calls. Modular design makes it easy to swap in new capabilities. It’s built for real-time decision-making in complex environments. 💡 Real-World Use Cases - Field engineers using voice + image input for automated diagnostics. - Medical assistants combining patient conversations + scan interpretation. - Voice-enabled dashboards that speak and visualize KPIs in real time. 🛠 Tech Stack Kiro IDE | Cursor IDE | AWS Bedrock AgentCore | Claude | Nova | Whisper | Amazon Polly | MindsDB | DynamoDB | S3 | FastAPI | Streamlit | OpenCV This week felt like the moment it all clicked — when agents stopped acting as standalone tools and started working as a collaborative team. Next week → Week 10: Bringing it all together – Agentic AI in Production. 🚀 #GenAI #AgentCore #AWSBedrock #Claude #Nova #VoiceAI #MultimodalAI #AgenticAI #MCP #10WeeksOfGenAI #KiroIDE #CursorIDE #AIArchitecture

AWS Open-Sources an MCP Server for Bedrock AgentCore to Streamline AI Agent Development AWS has open-sourced an MCP server for Amazon Bedrock AgentCore, enabling IDE-native agent workflows across MCP clients via a simple mcp.json plus uvx install; supported client docs and repo examples cover Kiro and Amazon Q Developer CLI setup, and the server runs directly on AgentCore Runtime with Gateway/Memory integration for end-to-end deploy→test inside the editor; the code and install guidance are live in the awslabs/mcp repository (including the amazon-bedrock-agentcore-mcp-server directory) and AWS developer docs for MCP usage and runtime hosting. Key takeaways: 1️⃣ IDE-native agent loop. MCP clients (Cursor, Claude Code, Kiro, Amazon Q CLI) can drive refactor → deploy → test directly from the editor, reducing bespoke glue code. 2️⃣ Fast setup with consistent config. One-click uvx install plus a standard mcp.json layout across clients lowers onboarding and avoids per-tool integration work. 3️⃣ Production-grade hosting. Agents and MCP servers run on AgentCore Runtime (serverless, managed), with documented build→deploy→invoke flows. 4️⃣ Built-in toolchain integration. AgentCore Gateway auto-converts APIs/Lambda/services into MCP-compatible tools; Memory provides managed short/long-term state for agents. 5️⃣ Security and IAM alignment. Agent identity and access are handled within the AgentCore stack (Identity), aligning agent calls with AWS credentials and policies. 6️⃣ Standards leverage and ecosystem reach. By targeting MCP (open protocol), the server inherits cross-tool interoperability and avoids vendor-specific connectors. full analysis: https://lnkd.in/gRcaBaKK github: https://lnkd.in/gKxVwBk6 technical details: https://lnkd.in/g6PfZjh8 Amazon Web Services (AWS) AWS AI AWS Developers Swami Sivasubramanian Shreyas Subramanian, PhD Primo Mu

🤝🏻️ Bringing it all together: Giving AI Agents the power of ML Models with Amazon SageMaker AI and Amazon Bedrock AgentCore 🚀 Excited to share my latest blog post where I dive deep into combining the power of ML models with AI Agents, and show you how to: ⭐️ Deploy ML models using Amazon SageMaker AI endpoints ⭐️ Leverage Amazon Bedrock AgentCore Gateway with AWS API Smithy Models ⭐️ Create custom MCP servers with Amazon Bedrock AgentCore Runtime ⭐️ Build intelligent AI Agents that can make ML-powered predictions 💡 Whether you're looking to scale your ML operations or enhance your AI Agents with predictive capabilities, this guide shows you two powerful approaches to achieve it. 🛠️ Complete with code examples and step-by-step instructions, you'll learn how to turn your ML models into powerful tools that AI Agents can leverage for real-world applications like demand forecasting. 🔗 Check out the full article and code repository in the comments below 👇🏻️ #AWS #MachineLearning #ArtificialIntelligence #CloudComputing #Innovation #TechNews #AWSCommunity #AIAgents #SageMaker #Bedrock

🤓 Who else was waiting for this? Most days I mess about with gen AI agents and agentic workflows. And while Amazon Bedrock Agents are great at production scale, they can be a little "too big" for experimentation! Know what I mean? When you just want to experiment with an agent, test an idea, etc, setting up a whole agent version and alias is a little heavy. (Yes I was the one who launched a production grade gen AI agent with custom API during a 6 hour hack-a-thon, much to the amusement of the rest of my team hacking away with local code in Cursor! 🤣 ) GREAT NEWS: The Amazon Bedrock Agents team did one of those pre #reInvent announcements and quietly dropped "invoke_inline_agent", a way to configure a quick agent and invoke it all in one API call. Nothing persists in the service, nothing to clean up, and hmmm 🤔 maybe some interesting new architectures are born?! I've made a quick video walking through a simple example to help you get started: https://lnkd.in/gTF9jKKx To get started you will need to update the AWS SDK to the latest version. For me that's Python, so... pip install boto3 -U Then you can use: AgentsforBedrockRuntime.Client.invoke_inline_agent(**kwargs) Pass in all the config you need for the agent, in a very similar way that you would pass in creating your production ready agent alias previously. (The full sample code and documentation are available in the video description above.) As the name suggests, this will invoke the agent there and then, and pass back the results. And that's it. Nothing to clear up. And the agent still works as a normal agent. The session is still maintained by the service until you end it, or it times out. So you can go back with the same sessionId and carry on the agentic conversation. I really like this and will be using it a bunch in some projects coming up. I grabbed this from the doc page, and I think it sums it up nicely: The following are some of the use cases where using inline agents can help by providing you the flexibility to configure your agent at invocation time: - Conducting rapid experimentation by trying out various agent features with different configurations and dynamically updating tools available to your agent without creating separate agents. - Dynamically invoking an agent to perform specific tasks without creating new agent versions or preparing the agent. - Running simple queries or using code interpreter for simple tasks by creating and invoking the agent at runtime. PLEASE let me know what you think. Is it just me excited about this?! 👏 🤓 Connect with me here on LinkedIn and over on YouTube. #reInvent2024 is about to start and I will of course be there! 🚀🤓 #AI #Amazon #BedrockAgents #TechInnovation

Some used to think the hardest part was getting agents to work. Turns out the real challenge is: deploying them reliably at scale while maintaining visibility into their behavior. I spent the few days "productionizing" a deep research agent on AWS. Not a demo. Like A real service (somehow 😂). The real problems I had to solve: 🧪 How do you monitor a non-deterministic LLM agent? → With Langfuse: full traces, latency, failures, sub-agents, prompts, token usage, cost. 🧠 How do you keep user context and long-running tasks? → With Amazon Web Services (AWS) Bedrock AgentCore: isolated sessions, optional memory, long timeouts, support for complex agent frameworks. ⚙️ How do you industrialize deployment? → With Terraform: declarative infrastructure, secrets, buckets, runtimes, versioning. 🌐 How do you test the agent like a real service? → With a simple HTTP runtime, an invoke script, and curl for debugging. My main takeaways: 🧠 You can’t improve what you can’t see. 💬 You can’t iterate on prompts without understanding their effects. 🔁 And you don’t want to redeploy manually ten times a day. If you’re working on agents and thinking about production, I wrote up the full flow + Terraform module. Links in comment. ——— I document my journey with AI here— I share what I learn and how I progress. If you enjoyed: follow me --> @Pierre Ange 🤸🏿

🤖 Multi-Step Agents and Compounding Mistakes - TL;DR, mitigating compounding mistakes in complex agents and multi-step agents via Amazon Bedrock Capabilities. As AI agents tackle increasingly complex tasks, we face a critical challenge: compound mistakes. Imagine an AI system performing a 10-step task with 95% accuracy per step - the cumulative error could reduce overall task success to a mere 60%, turning potentially reliable systems into unpredictable black boxes. With each step, the risk of errors multiplies, potentially tanking overall accuracy. Here are some evolving strategies to keep our AI agents on track using Amazon Bedrock: ⚡ Improving Individual Step Accuracy: Leverage advanced models like Claude 3.5 Sonnet, Amazon Nova Pro, etc. which achieve SOTA accuracy on multi-step reasoning tasks and implement smart data augmentation techniques along with better prompting. Guardrails and Automated Reasoning Checks in Bedrock can validate factual responses for accuracy using mathematical proofs - https://lnkd.in/gdEyUrGE ⚡ Optimize Multi-Step Processes: Utilize frameworks like ReAct for interleaving reasoning and acting along with custom reasoning frameworks. Bedrock Agents now support custom orchestrator* for granular control over task planning, completion, and verification - https://lnkd.in/gQasM7kX ⚡ Monitoring and Metrics : Implementing robust monitoring and establishing clear quality metrics are essential. CloudWatch has an automatic dashboard for Amazon Bedrock added to provide insights into key metrics for Amazon Bedrock models - https://lnkd.in/gee_zdiv ⚡ Hybrid Data Approaches that combine structured and unstructured data can generate more accurate outputs. Bedrock Knowledge Base now has out-of-box support for structured data - https://lnkd.in/gfthHvsi ⚡ Self-reflection and Correction: Amazon Bedrock Agents Code Interpretation support the ability to dynamically generate and execute code in a secure environment enabling complex analytical queries. https://lnkd.in/gQzxdK3P #amazon #bedrock #agenticAI

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗥𝗔𝗚-𝗕𝗮𝘀𝗲𝗱 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗳𝗼𝗿 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 I recently worked on a 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) based Proof of Concept (POC) to streamline financial research and portfolio generation using 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗮𝗻𝗱 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗔𝗴𝗲𝗻𝘁𝘀. Here's a quick breakdown of the implementation: 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗦𝘁𝗲𝗽𝘀: 1️⃣ 𝗨𝗽𝗹𝗼𝗮𝗱 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗗𝗮𝘁𝗮(𝗥𝗲𝗽𝗼𝗿𝘁𝘀) 𝘁𝗼 𝗦𝟯 Set up an S3 bucket and upload company reports for data retrieval. 2️⃣ 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 𝗶𝗻 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 Enable models (𝗖𝗹𝗮𝘂𝗱𝗲 𝟯 𝗛𝗮𝗶𝗸𝘂, 𝗧𝗶𝘁𝗮𝗻 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗩𝟮) and create a knowledge base linked to the S3 bucket. 3️⃣ 𝗗𝗲𝗽𝗹𝗼𝘆 𝗟𝗮𝗺𝗯𝗱𝗮 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗗𝗮𝘁𝗮 The Lambda function serves as a backend API for the AI agent that will be created to access and retrieve company-related data. 4️⃣ 𝗦𝗲𝘁 𝗨𝗽 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗔𝗴𝗲𝗻𝘁 & 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 Create an agent in Amazon Bedrock with defined action groups (e.g., /companyResearch, /createPortfolio). Customize prompts for precise orchestration and output. 5️⃣ 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 𝘄𝗶𝘁𝗵 𝗔𝗴𝗲𝗻𝘁 Link the knowledge base to the agent and configure handling instructions for seamless interaction. 6️⃣ 𝗦𝘆𝗻𝗰 𝘁𝗵𝗲 𝗞𝗕 𝗮𝗻𝗱 𝗽𝗿𝗲𝗽𝗮𝗿𝗲 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 Sync Knowledge base and prepare the agent for real-time enhancements. 7️⃣ 𝗗𝗲𝗽𝗹𝗼𝘆 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝘁 𝗔𝗽𝗽 𝗼𝗻 𝗘𝗖𝟮 Host an interactive AI-driven app by running a Streamlit application on EC2, enabling users to explore insights via an external URL. 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 This AI Agent simplifies analysis by automating research, generating tailored portfolios, and summarizing documents—all while adapting to user feedback for better accuracy. 💬 Have you built an AI agent yet? Let’s connect and share ideas! #AIInnovation #GenerativeAI #RAG #AmazonBedrock #MachineLearning

Every AI agent needs the same scaffolding underneath: tools to interact with, an environment to work in, a system to manage context, memory to help personalize, identity to control access, and observability to understand and course correct. Wrapped in a loop that calls the model, picks a tool, and recovers from failures. This is the agent harness, and until now, every team built it from scratch. Today we launched the 𝗺𝗮𝗻𝗮𝗴𝗲𝗱 𝗮𝗴𝗲𝗻𝘁 𝗵𝗮𝗿𝗻𝗲𝘀𝘀 𝗶𝗻 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗔𝗴𝗲𝗻𝘁𝗖𝗼𝗿𝗲 (public preview). You declare your agent, and run it in three API calls. You point to the model, tools, skills, and instructions as configurations in the API, and AgentCore stitches together everything around it to make the agent production-ready. What you get out of the box: 1️⃣ 𝗔𝗻𝘆 𝗺𝗼𝗱𝗲𝗹, 𝘀𝘄𝗶𝘁𝗰𝗵 𝗺𝗶𝗱-𝘀𝗲𝘀𝘀𝗶𝗼𝗻. Bedrock, Anthropic, OpenAI, Gemini, or any OpenAI-compatible endpoint (coming soon). Switch providers mid-session without losing context. 2️⃣ 𝗧𝗼𝗼𝗹𝘀, 𝗱𝗲𝗰𝗹𝗮𝗿𝗮𝘁𝗶𝘃𝗲𝗹𝘆. MCP servers, AgentCore Gateway, built-in Browser and Code Interpreter, or your own inline functions. One config line per tool - no boilerplate code to write. 3️⃣ 𝗦𝘁𝗮𝘁𝗲𝗳𝘂𝗹 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁. Each session runs in a secure, isolated microVM with its own filesystem and shell. Short-term and long-term memory persist across sessions. 4️⃣ 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆. Run shell commands on the session's dedicated microVM to set up repos, extract artifacts, or debug. 5️⃣ 𝗕𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿. Pre-bake source code, runtimes, and dependencies. The harness wraps your environment and works with it. 6️⃣ 𝗕𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗦𝗸𝗶𝗹𝗹𝘀. Compose your agent with Agent Skills: bundles of markdown and scripts that give it domain knowledge on demand. Use the open ecosystem or write your own. The harness handles loading and execution. 7️⃣ 𝗕𝘂𝗶𝗹𝘁 𝗼𝗻 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲. The harness is powered by Strands Agents, AWS's open-source framework. When config stops being enough, export to code and keep running on the same compute, same microVM, same observability. No re-architecture, no platform tax. Trying a new model or tool is a config change, not a code rewrite. Managing context, remembering across users, enforcing policies, using a new skill: again config, not infrastructure. Weeks of plumbing collapse into minutes! Learn more in our docs: https://lnkd.in/gqv5NmW3 and in our GitHub samples: https://lnkd.in/gKWysZkD #aws #bedrock #agentcore #harness