"Reducing emails is God's work." — Mitch Ashley, VP & Principal Analyst, The Futurum Group When an analyst invokes the divine, you know you've hit real pain. Most emails exist because someone needs context that lives somewhere else. A status update buried in a project tracker. An answer scattered across Slack, docs, and dashboards. A question only one person on the team can answer. Amazon Quick, the AI assistant for work, connects to your entire team's work context. It finds what you need, makes sense of it, and surfaces the signal over the noise. The result: fewer emails sent, fewer emails received. Not because you're ignoring them, but because the need for them disappears. https://go.aws/4y1isws
Updates
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Consolidating information from a wide array of sources enables scattered work contexts to become agentic workflows." — Larry Carvalho Principal Consultant, RobustCloud New deep dive on how Quick connects local files, email, cloud storage, and web content into agentic workflows. With examples from 3M, Mondelēz, and independent analysts. https://lnkd.in/g2J_jCEN
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Amazon Quick's moat is likely to be context and the knowledge graph." — Larry Dignan, Editor in Chief, Constellation Research Why could Quick be more strategic than recognized? New analysis from Constellation Research on the platform's competitive moat. https://lnkd.in/gU-84wXk
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This is the unlock. 19 agents running autonomously—meeting prep in 30 seconds, relationship decay alerts, knowledge vault maintenance on schedule. The real win isn't time savings; it's decision quality when you're not hunting for context. Four weeks to set up, infinite ROI after.
Last week at AWS Summit New York, Quick launched autonomous agents and a redesigned activity feed. If you're wondering what that looks like in practice, I've been running it for four weeks. It started when I walked into a partner meeting and couldn't remember the last three things we'd agreed to. The information existed. Somewhere. But "somewhere" is the same as nowhere when you're five minutes from a call. So when the "second brain" hype hit my feed for the hundredth time, I gave it a weekend. I wondered if the YouTube videos actually translated into something that would work. That was four weeks ago. Today, 19 AI agents run my work life on a schedule. They have Canadian names (I'm Canadian, naming things is hard, might as well enjoy it): Portage ingests every document I touch. I haven't manually filed one since. Scout maintains 76 people profiles and noticed things I never would have written down, like who goes quiet when they disagree. Terry audits my sent messages every Friday and flags promises I haven't kept. Humbling. Useful. Zamboni resurfaces the knowledge vault every Sunday. And a loop watches my executive partner relationships for decay. If one goes cold and I don't act within 48 hours, it escalates to my EA. Relationships no longer go quiet by accident. The part that surprised me: Quick is the only interface to all of this. I never open the vault directly. I never browse wiki pages. I talk to my orchestrator agent, and it queries the vault, the knowledge graph, live systems, and synthesizes across all of them in one answer. The knowledge graph (which Quick builds automatically from email, Slack, and calendar) and the curated wiki feed each other. One gives me curated intelligence. The other gives me lived context. Together they answer questions I'd forgotten were askable. What changed: meeting prep went from 15 minutes to 30 seconds. A 200-page knowledge wiki compounds while I sleep. I can answer "what did I commit to last week?" in seconds. The honest part: this took four weeks of tuning, not fifteen minutes. But the effort front-loads. The system maintains itself now. Full build in the article below. If you try this, start with one agent, not nineteen. My memory is no longer the single point of failure. That was the whole point. https://lnkd.in/eD8qZ-tf #AmazonQuick #Agents #AWS Rahul Pathak Rima Olinger Swami Sivasubramanian Ruba Borno
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Amazon Quick reposted this
Value scales when frontier complexity becomes dependable machinery. On the ferry ride back from Amazon Web Services (AWS) AI Summit New York, that was my lingering takeaway. Trainium, Project Rainier and Claude on AWS are the silicon-to-scale co-designed layer. Nova and Nova Forge are the model and customization layer. Bedrock and SageMaker offer enterprise control plane for model access, choice, training, MLOps, guardrails and value per token. On Nova, I’ll stay with the publicly available info. OpenAI, Anthropic and Google DeepMind may dominate the frontier conversation today, while Microsoft AI moves into the arena. The Nova team understands what is at stake. Hold your horses. This s-curve is in its early stages. AgentCore, Context, Managed Knowledge Base, web search and S3 annotations add the agent plumbing: runtime, memory, tools, retrieval, policy and governed context. Quick brings agents into everyday work. Kiro and DevOps Agent pull them into engineering and release readiness. Transform keeps modernization moving. Continuum pushes security toward machine-speed remediation. Amazon Connect carries the agent pattern into customer operations. QuEra and Braket point to the longer compute arc. But the part I keep coming back to is formal reasoning. #GenAI model moves grab the headlines. The quieter undercurrent is proof assistants, SMT solvers, model checking, theorem proving, Lean-style verification, policy proofs and automated reasoning. Jane Street is building a formidable team in formal methods. Yes, that Jane Street, one of the most talent-dense money machines in finance. That should tell you something. Agents are getting easier to launch by the week. Making them behave, prove, trace, constrain and recover inside real enterprises is the brutal part. Without that layer, agentic scaling becomes deploy and pray with better dashboards. Every minute with Byron Cook is uniquely rewarding. Byron, Nadia and team reinforced why automated reasoning may become one of AWS’s most underappreciated differentiators in the agentic era. Amazon has one of the best talent benches in formal methods. The frontier matters most when it stops feeling fragile. The patterns across Google Cloud Next, Microsoft Build and the Amazon Web Services (AWS) AI Summit couldn’t be clearer AI-native and agent-native cloud and infrastructures are becoming the foundational architecture enterprises will design around. Gartner was represented by a vanguard of analysts to engage and explore this frontier. It was nice to reconnect with my peers Ed Anderson Lydia Leong Gaurav Gupta Tobi Bet Chris Iervolino Nitish Tyagi Neha Agarwal Nicholas McQuire Var Shankar Tamara Kawashiri Laz Gonzalez As you shape architectures around GenAI and agentic AI, Gartner is here to help. Reach out and we can organize analyst consultative sessions and more. #AI #GenerativeAI #AgenticAI #EnterpriseAI #AIInfrastructure #AWS
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The thought of finalizing month-end close reports shouldn’t make finance teams sweat. Reconciling across 3 ERPs and 5 billing systems used to mean a week of spreadsheet wrangling. Amazon Quick, your AI assistant for work, connects them all and surfaces discrepancies automatically. Because today’s market doesn’t wait for outdated manual systems. With Quick, finance teams spend time acting on numbers rather than hunting them down. Try Quick for free at https://go.aws/4vtbleD
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Learn why Quick and AgentCore are complementary, not competing, in the emerging agentic AI economy.
Amazon Quick vs. Amazon AgentCore: Understanding AWS’s Two Agentic AI Bets One of the more interesting developments coming out of AWS is that the company is pursuing two very different approaches to the agentic AI market simultaneously. At first glance, Amazon Quick and Amazon AgentCore may appear similar. Both are associated with AI agents, automation, and enterprise productivity. In reality, they target entirely different layers of the emerging agent economy. Amazon Quick is designed for the end user. Amazon AgentCore is designed for the builder. That distinction matters because many organizations are still trying to determine where value will ultimately be created in the age of agentic AI. Amazon Quick represents AWS’s entry into the digital coworker market. Similar to Microsoft 365 Copilot, Google Gemini, ChatGPT Enterprise, and Glean, Quick connects to enterprise applications, documents, knowledge repositories, and workflows to help employees research information, create content, summarize data, and increasingly execute business tasks on their behalf. The value proposition is simple: make knowledge workers more productive. AgentCore addresses a different challenge. Rather than helping employees perform work, AgentCore provides the infrastructure required to build, deploy, orchestrate, govern, and manage AI agents at scale. It is focused on developers, architects, and platform teams building production-grade agentic systems that interact with APIs, applications, databases, workflows, and other agents. If Quick is the application layer, AgentCore is the control plane. This distinction highlights a broader shift occurring across the AI industry. The first wave of generative AI focused on models. The second wave focused on copilots. The third wave is becoming focused on execution. As enterprises move from asking AI questions to assigning AI objectives, entirely new infrastructure requirements emerge. Organizations need identity, authorization, governance, observability, orchestration, lifecycle management, and economic controls capable of managing autonomous systems operating across the enterprise. That is where platforms such as AgentCore become strategically important. Quick and AgentCore are not competing products. They are complementary offerings targeting different buyers & different layers of the stack. Quick targets employees looking to accelerate work. AgentCore targets developers building systems that can perform work. The larger opportunity for AWS is not simply selling another AI assistant. It is creating the platform where enterprise agents are built, managed, governed, and executed. Historically, AWS became the infrastructure foundation for cloud applications. The next opportunity is becoming the infrastructure foundation for autonomous work. Quick may be the interface employees see. AgentCore may be the engine powering the agent economy behind it. #AWSSummit #AmazonQuick #AgentCore #TekonyxPOV Melissa Grant
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"Every other vendor will eventually have to copy it or explain why they did not." Techaisle's Anurag Agrawal's published an independent breakdown of where Amazon Quick sits in the agentic AI landscape. His argument: the winning position isn't the model or the interface. "The connective layer wins." Read the full analysis: https://lnkd.in/gkpnevTr
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Amazon Quick reposted this
Had a great few days at the Amazon Web Services (AWS) Summit NYC. Amazon Quick was the most convincing candidate I’ve seen yet for an agentic AI end user work surface, the place knowledge work actually happens rather than another add-on AI assistant trying do work for you. You state the outcome you want and it acts across Slack, email, calendar, files, and structured and unstructed data in AWS, Snowflake or Databricks, with autonomous agents that hold a goal and keep working it on a schedule. The same bet showed up in AWS Context, a new data context layer that builds a knowledge graph from an organization’s existing data, infers the relationships across datasets, and exposes that to an agent at runtime. Read together, both products treat agent effectiveness as a function of how well the system understands your data. The model underneath is increasingly interchangeable. That is where the durable advantage is forming, and it is the layer I am watching most. Kiro handles the first turn with spec-driven generation and now runs from a mobile app. Release Management in AWS DevOps Agent runs production risk assessment before merge, flagging breaking changes and policy violations across dependent services. AWS Transform continuous modernization works dependency and framework debt down in the background and plugs into CodePipeline, Jenkins, GitHub Actions, and GitLab. AgentCore Policies enforces controls outside the agent’s code, on the stated premise that a model cannot reliably separate instructions from data and therefore cannot be trusted to police itself. AWS Continuum, on the security side, graduates from a human-in-the-loop mode to autonomous remediation only as it earns confidence over time. AWS DevOps Agent, AgentCore Harness, Bedrock Managed Knowledge Base, well… you get the idea. Special thanks to the fantastic AWS AR team including Katharine Kemp, Melissa Grant, Ingrid Duffy, Mamta Shah, Amanda Elfving , Monica McCown, and more, for a great event. Full Futurum report in the coming days, where I separate what is generally available from what is still early. The Futurum Group #AWSSummit #AmazonQuick #AgenticAI #KnowledgeGraph #AgentGovernance
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After Quick, everything else feels like crawling. 🤖
He was roaming about for hours, but someone took away his Amazon Web Services (AWS) Quick desktop app, and now he's too overwhelmed to work. #AI #PhysicalAI #AWSSummit
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