AWS Summit New York 2026 brought a flood of AI agent announcements
AWS Summit New York 2026 took place on June 17 and delivered a wave of announcements that, honestly, will be hard to ignore if you follow the artificial intelligence space.
This was not another event full of promises.
It was AWS showing up with real products and concrete use cases, proving that AI agents have moved beyond the drawing board and are being put to actual work inside enterprises.
Over the past six months, something has shifted significantly in the market. The conversation moved from what if we used AI for this straight to we already have agents running here, and the results are coming in. And what AWS brought to the Summit reinforces exactly that turning point:
- Tools that reduce friction for those already in the game
- Infrastructure that scales without requiring a massive engineering team
- Solutions that connect security, context, and autonomy into a package that finally makes sense outside the lab
There is one detail that Swami Sivasubramanian, Vice President of Agentic AI at AWS, highlighted on stage that sums up the moment perfectly: the more you use agents, the better they get. More interactions generate more context. More context generates better results. Better results increase trust. More trust means more work delegated to agents. And then the cycle feeds itself.
This is creating a gap that is growing fast between companies already on this journey and those still waiting for the right moment to start. 👀
In the sections ahead, we will break down each of the announcements, from AWS Continuum to AWS Context, through Amazon Quick, Kiro on iOS, AWS DevOps Agent, AWS Transform, and much more.
Let us dig in.
AWS Continuum for code vulnerabilities: agentic security at machine speed
One of the most talked-about announcements at the AWS Summit was the launch of AWS Continuum for code vulnerabilities, a new AI-native security service that addresses the full vulnerability management lifecycle at machine speed. AWS has been thinking deeply about where the world of security is heading and the role agents play in responding to the threats enterprises face.
The picture is clear: in an agentic world, telemetry, context, reasoning, and actions are the new pillars to build on. Specialized security models like Claude Mythos have already emerged, validating this perspective. The use of these models by both attackers and defenders has dramatically accelerated the ability to find and exploit vulnerabilities. That means organizations need to stay ahead of threats at machine speed, using agents to accelerate responses by continuously processing information and taking action.
AWS Continuum solves this in a pretty straightforward way. It continuously discovers vulnerabilities, validates which ones are genuinely exploitable, prioritizes them by business context, and helps remediate issues across the entire stack — all within guardrails you define yourself. It is a service designed to be model agnostic, leveraging the strengths of different models where they perform best and integrating new models as they emerge.
At every step, you have full visibility into what Continuum is doing, why it suggested a specific action, and what would happen if that action were reversed. Every decision is explainable, every action is auditable, and every outcome feeds back into the system to improve the next cycle. This level of transparency is critical for security teams to trust the agent enough to give it more autonomy.
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On top of that, AWS also launched Continuum threat modeling, which automatically generates comprehensive threat models from design documents or source code, delivering results in industry-standard format. For anyone working with security at scale, this eliminates hours of manual work and reduces the chance of something slipping through. 🔐
AWS Context: the agent that understands what you did not say
If Continuum solves the problem of continuous security, AWS Context solves something even more subtle and, perhaps, more powerful: the ability for an agent to understand the environment it operates in without needing everything spelled out explicitly.
AWS Context is a new service that automatically builds a knowledge graph from your existing data, which your agents can then use. It infers the relationships between your data assets, business rules, and domain knowledge, making all of it available to every agent in your organization.
Context is what makes your agent’s tenth decision better than the first. With the right context, the agent can see the latest interactions you had with a customer in your CRM and recommend the best follow-up. Without context, it tends to deliver recommendations with confidence — but they are wrong. Companies have access to all kinds of data, from information in databases to Slack messages, documents, and emails. For an agent to make that information useful, it needs to understand how to navigate it: what tables exist, what is stored in different columns, which sources are more reliable, and how they relate to each other.
Built on the same knowledge graph technology that powers Amazon Quick, AWS Context works as an agentic search layer for organizational data, connectable to all your agents. It has built-in governance to ensure agents only access the information they should. With all metadata from your data sources stored in Iceberg format on S3 Tables, you can build on top of AWS Context with the tools you already use. No infrastructure to provision. No retrieval pipeline to build.
As agents interact with AWS Context, it learns which sources produce correct results, which paths are most used, and which business rules matter — improving over time. Each agent can then get smarter based on the findings from a single query. For example, a customer support agent triaging an issue with an order might need to access purchase history, shipping status, and return eligibility across multiple different sources. The next time a support agent faces a similar issue, it already knows exactly where to go, reducing search time and reaching a resolution faster.
Amazon Quick: autonomous agents working while you get your time back
Amazon Quick entered the AWS Summit conversation with a proposition unlike anything seen before in enterprise AI assistants. It is the AI assistant built for the way you actually work, with enterprise security your company will actually approve.
AWS made a point of drawing a clear line here. Other vendors have AI assistants that are stuck inside a single application and cannot work across all the systems you need — what AWS called walled gardens. Or they have assistants with little concern for security and data sharing, called wild gardens. With Quick, you do not have to make those tradeoffs.
With the launch of the new Quick autonomous agents, you can create agents that work in the background with specific expertise, a defined tone, and access to tools. You can create a financial agent that processes orders as they come in, or a sales agent that monitors CRM interactions, emails, and Slacks to proactively draft follow-ups, flag risks, or recommend next steps. And the best part: no code required — Quick agents are accessible to anyone.
AWS also launched a new personalized activity feed that consolidates email, messages, calendar, and tasks into a single prioritized view. It learns which messages you always respond to quickly, which threads you skip, and which topics drive your week. Plus, while Quick already connected to the most popular apps, they have now added 16 new native integrations with companies like Adobe, Moody’s, and Snowflake. 🚀
Kiro on iOS: orchestrate agents from anywhere
One announcement that caught attention for a different reason was the arrival of Kiro on iOS. Kiro is the AI-assisted software development service from AWS, and bringing it to the iPhone is not just a convenience play. It is a clear signal that AWS understands that working with AI agents does not only happen in front of a computer.
The momentum companies are seeing around software development is impressive. Dhan, a unicorn fintech startup from India, used Kiro to build a new charting platform with a single engineer in just 8 weeks — when the original estimate was a dozen people working for 12 to 24 months. Having a large team of developers is no longer the bottleneck for the next big idea, but having access to the right tools regardless of where you are can be.
With Kiro on mobile, agents no longer live only on your laptop. They run on an always-on cloud session that works whether you are at your desk or not. You can kick off development on a new feature on the subway, review code between meetings, or approve changes while grabbing lunch. When you get back to your laptop, you are exactly where the agent left off. Same session, same context, and you can jump right back into work immediately.
And despite all that mobility, everything continues running securely in your cloud environment. Full mobility. Zero compromises. 📱
AWS DevOps Agent: your agents do not just write code, they help you ship it safely
While you can now create code faster than ever with agents like Kiro, you only start benefiting from that code when it hits production. Your coding agents might make you ten times more productive, but your pipeline may not have adapted to the new pace. Pull requests waiting. Tests running sequentially. Failures showing up in production.
Last year, AWS launched the AWS DevOps Agent, an always-available operations companion. Now, they have expanded what it can do to help with the release management process, so you can ship code faster and more safely.
AWS DevOps Agent now supports everything from release readiness review to testing, making sure your pipeline can keep up with your development speed. Whether you are building with Kiro or Claude Code, the moment you generate code, you can trigger release readiness reviews directly from where you code.
Here is a practical example: a developer working on a project that is part of a larger application renames a parameter to make it clearer. The update looks small in isolation, and local tests show everything is fine. But the change could cause a breaking change when interacting with the rest of the application. Because the developer is using DevOps Agent, they discover the impact of the code before it goes live, receive a summary of the issue, and get a recommended fix.
Once the code is ready, you create a pull request to enter the pipeline or deploy to sandbox, and then run release testing. AWS DevOps Agent generates and executes test plans specific to the changes in environments you have provisioned, catching regressions, UX issues, and integration failures before they reach production.
AWS Transform: continuous modernization that keeps you ahead of tech debt
When you start moving code to production faster than ever, a new challenge emerges: how do you keep all that software up to date? The truth is, the moment you deploy code, it starts aging. Dependencies fall behind. Frameworks get deprecated. Before you know it, tech debt has piled up.
AWS Transform is the agentic AI service from AWS designed to support modernizations at scale. It has already eliminated more than 1.6 million hours of manual effort for customers like BMW Group, Experian, and others.
Until now, Transform was something you ran as a one-off: you pointed it at a problem, it helped solve it, and that was that. But with the speed that agents write new code, you need an agent that continuously keeps code updated and well-documented.
That is why AWS launched AWS Transform — continuous modernization, an autonomous, always-on software portfolio management and modernization capability. While your agents write new code, AWS Transform works behind the scenes, finding tech debt, fixing it, validating the fix, and learning from each transformation to make the next one better. It integrates with the pipeline tools you already use — like CodePipeline, Jenkins, GitHub Actions, and GitLab — fitting into your workflows without friction.
Amazon Bedrock AgentCore: build, connect, and optimize agents in production with security and scale
AWS is building for a future where billions of agents are in operation. And as customers move in that direction, one thing became clear: building an agent is simple, but taking it to production can be complex. That is why Bedrock AgentCore exists — the platform for when you want to move agents from proof of concept to production.
The approach is clearly resonating. Over the past 6 months, the number of tasks executed by agents on AgentCore has grown 15x. The PGA Tour is writing tournament coverage 10x faster. And Nasdaq, Visa, and Experian are scaling agents across their entire enterprises.
The new capabilities announced include:
- Amazon Bedrock Managed Knowledge Base: a fully managed knowledge base that handles ingestion, parsing, and retrieval for your RAG/knowledge bases, with native connectors for S3, Sharepoint, Confluence, and Google Drive, plus an agentic retriever for complex queries
- Web Search on AgentCore: grounds agents with current, accurate web information using the same search that powers Quick, Kiro, and Alexa+, operating natively within the AWS environment
- New optimization capabilities: turn production traces into continuous improvements with failure insights, intent, and trajectory analysis across hundreds of agent sessions, plus recommendations and A/B testing now in general availability
- New policy integrations: Amazon Bedrock Guardrails integration with AgentCore to evaluate every agent action against prompt injection attempts, harmful content, and sensitive data exposure, with future support for signals from providers like Check Point, Zscaler, Rubrik, Netskope, and SentinelOne
- AgentCore harness in general availability: go from idea to working agent in minutes by declaring what your agent does and letting AgentCore handle orchestration, tool execution, memory management, context handling, and error recovery automatically
Southwest Airlines accelerates AI adoption and cloud modernization with AWS
Wrapping up the event announcements, AWS revealed that Southwest Airlines is partnering with AWS as its preferred cloud provider to modernize its technology foundation and apply AI to transform how the airline operates and serves its customers.
As part of the partnership, Southwest will migrate from a mostly on-premises environment to an AI- and agent-enabled, cloud-based architecture on AWS by 2028, building a foundation for greater speed, flexibility, and reliability across the entire operation.
More than 2,700 Southwest developers are already using Kiro to build features, automate testing, and generate cloud infrastructure to modernize Southwest.com. The airline is also adopting an AI-Driven Development Lifecycle (AI-DLC) approach, where AI agents help move development forward while engineering teams guide and validate the results. As Southwest continues modernizing its presence on AWS, they are expanding AI- and agent-based capabilities across the entire company, adopting new tools like Amazon Quick.
What AWS Summit 2026 tells us about the future of AI agents
What AWS Summit 2026 made clear is that the era of AI agents is no longer a future promise. It is the present for those already building, and the gap between those who act now and those who wait is growing every quarter.
Between AWS Continuum ensuring security at machine speed, AWS Context providing the contextual intelligence agents need, Amazon Quick democratizing the creation of autonomous agents, Kiro on iOS taking development anywhere, AWS DevOps Agent ensuring safe delivery, AWS Transform keeping tech debt under control, and Amazon Bedrock AgentCore scaling agents in production, AWS has assembled a toolset that covers the full lifecycle of AI agents.
It is not perfect, and there will certainly be adjustments along the way, but the direction is set with a clarity rarely seen at tech events. The partnership with Southwest Airlines shows this is not just a lab story — these are massive companies betting on this infrastructure to transform real-world operations.
For anyone closely following the artificial intelligence market, AWS Summit New York 2026 will be remembered as the event where agents stopped being next year’s topic and became today’s reality. And that changes a lot. ⚡
