The experimental phase of generative AI is officially over. We are moving beyond simple chatbots and into an era of autonomous, agentic workflows that act as genuine force multipliers for your business. 

Our latest insights from AWS Summit NYC highlight a clear shift in strategy. Innovation is no longer just about generating text faster, it is about building intelligent engines that deliver compounding value. Capitalizing on this trend means finding natural ways to embed AI agents into your infrastructure. The focus is shifting from basic automation toward creating self-optimizing systems that improve continuously. 

AWS is laying the foundation for a future where your digital assets can actively secure, scale, and innovate on your behalf. This blog explores the key takeaways from the summit and shows you how to turn these architectural shifts into your company’s next competitive advantage.

Amazon Bedrock AgentCore’s New Capabilities: Beyond Chatbots 

Models often struggle with simple tasks like checking refund policies because they lack access to your specific data. Without the ability to retrieve documents, pull real-time market data, or learn from experience, an AI system is merely a static text generator. To transform these models into true force multipliers, AWS announced three major shifts for Amazon Bedrock AgentCore at AWS Summit NYC. 

Amazon Bedrock AgentCore’s Core Enhancements & Capabilities: 

• Amazon Bedrock AgentCore Harness: This allows you to define models, tools, and instructions in a single configuration, letting you build and run production-grade AI agents in minutes without writing custom orchestration loops.  
• Web Search on Amazon Bedrock AgentCore: Ground your AI agents in current, accurate web knowledge with built-in Model Context Protocol (MCP) tool support, keeping data secure within your AWS environment with zero data egress. 
• Integrated Guardrails: You can now evaluate every agent action for harmful content, sensitive data exposure, and prompt injection attempts directly through Amazon Bedrock Guardrails.  

To see how these components come together in practice, read our post, How We Built Production-Ready AI Agents in Minutes with Amazon Bedrock AgentCore, and start building your own intelligent agents today. 

Amazon Bedrock Managed Knowledge Base: Instant Data to Intelligence 

Amazon Bedrock Managed Knowledge Base is a fully managed Retrieval-Augmented Generation (RAG) service that automatically builds, scales, and operates end-to-end data pipelines. It serves as a native data-retrieval connector within the broader Amazon Bedrock AgentCore ecosystem. The service securely connects enterprise data to Foundation Models (FMs) and AI agents without the need to manually configure vector databases or retrieval infrastructure.

Figure 1: Introducing Core Capabilities of Amazon Bedrock Managed Knowledge Base

Core Features & Capabilities: 

• Native Data Connectors: Sync data directly from Amazon S3, SharePoint, Confluence, Google Drive, OneDrive, and public websites. 
• Smart Parsing: Automatically applies the optimal parsing strategy for diverse document types and multimodal content (text, images, video) while preserving metadata and thread context. 
• Managed Vector Storage: Automatically handles embeddings generation, vector storage, document re-ranking, and hybrid search optimization. 
• Agentic Retrieval: Automatically orchestrates complex, multi-hop queries that require reasoning and recursive retrieval without writing custom orchestration code.  

AWS Context (Coming Soon): Governing the Intelligence Layer 

AWS Context promised to bridge the gap between raw data and actionable AI by mapping your enterprise information into a governed knowledge graph. This provides agents with a shared understanding of business rules and complex relationships at runtime. Introduced at AWS Summit NYC, AWS Context will help transform scattered metadata into a structured foundation. It ensures your AI agents have the high-quality, relevant context they need to perform accurately.

Figure 2: AWS Context unveiled at AWS Summit NYC, 2026.

Core Capabilities of AWS Context:  

• Continuous Learning: The graph dynamically learns from agent queries, propagating successful join paths and curated rules across your organization without human intervention. 
• Open & Portable Architecture: Built on open standards, metadata is published in Apache Iceberg format on Amazon S3. This allows for seamless querying via Amazon Athena, Redshift, or Spark. 
• Identity-Aware Governance: Every query inherits the calling user’s IAM and Lake Formation permissions, ensuring that agents only access authorized data while maintaining a full audit trail for compliance teams. 
• Seamless Integration: Connects to your existing ecosystem via agentic search APIs and MCP tools, supporting both AWS-native services and third-party catalogs. 

AWS Continuum: Introducing Machine-Speed Security 

Traditional security models struggle to keep pace with AI-driven threats, as frontier models like Claude Mythos accelerate vulnerability discovery beyond human capacity. To address this, AWS announced AWS Continuum at AWS Summit NYC 2026.  

AWS Continuum is an AI-native security platform that manages the entire lifecycle of code vulnerabilities at machine speed. By utilizing business context instead of generic rules, it continuously discovers, validates, and prioritizes risks. It proves exploitability by simulating attacks in a sandbox. This helps teams focus on genuine threats and apply automated, human-approved remediations. 

Figure 3: AWS Continuum’s Core Capabilities Discussed at AWS Summit NYC, 2026.

Core Capabilities of AWS Continuum:  

• Intelligent Four-Phase Workflow: Executes continuous discovery of attack paths, prioritization based on business risk, and validation in sandboxed environments. This helps eliminate false positives and automated mitigation or remediation of the vulnerability. 
• Adaptive Trust Models: Features a graduated control system. It begins in a human-in-the-loop learning mode and transitions to enforce mode as confidence grows. 
• Expanded Security Suite: Includes on-demand pen testing, deep code scanning against compliance requirements, and the automatic generation of STRIDE-format threat models from source code or design documents. 

AWS DevOps Agent: End-to-End Release Management 

AWS DevOps Agent is evolving from a post-deployment operational tool into an autonomous teammate that bridges the gap between development and production. By deeply understanding your environment, including services, dependencies, and real-time behavior, the agent now supports the entire software development lifecycle. This shift allows the agent to handle tasks ranging from initial code creation to ongoing maintenance, functioning as a proactive partner rather than a reactive utility. 

Figure 4: Addition of Release Management Ability to AWS DevOps Agent.

New Capabilities added to AWS DevOps Agent: 

• Release Readiness Review: Evaluates code changes against your natural language standards and the AWS Well-Architected Framework. It catches access control and dependency risks before code is committed. 
• Autonomous Release Testing: Generates change-specific test plans for web and API-based applications. These tests run in isolated, production-like environments to catch functional regressions that static test suites miss. 
• Evidence-Based Approval: Every test run produces structured artifacts providing developers and reviewers with a consistent record of validation results. 

By integrating these features, the AWS DevOps Agent ensures that your delivery process remains smooth, secure, and increasingly autonomous. This applies whether you are operating across AWS, multi-cloud, or on-premises environments. 

AWS Transform: Proactive Technical Debt Remediation 

By automating the undifferentiated heavy lifting of software maintenance, this new capability frees your engineering teams to spend less time on manual patches and more time delivering high-value innovation. The new continuous modernization capability, showcased at AWS Summit NYC, moves technical debt management from periodic manual sprints to a continuous, autonomous pipeline. 

Figure 5: Continuous Modernization Ability Added to AWS Transform.

New Capabilities added to AWS Transform: 

• Continuous Analysis: Automatically scans thousands of repositories for end-of-life dependencies, deprecated frameworks, and custom coding standard violations. It provides an always-current view of your tech debt. 
• Autonomous Remediation: Proactively generates pull requests for common migration scenarios, such as Java version upgrades and SDK updates, while allowing for custom organization-specific transformation patterns. 
• Unified Visibility: Integrates with the AWS Security Agent to treat security vulnerabilities and technical debt in a single, prioritized workflow, providing ground truth verification without manual confirmation. 

Modernizing your infrastructure is essential for the agility required to scale AI initiatives. Read our blog, AWS Transform: Why Your Infrastructure Is the #1 Barrier to AI Readiness, to see how your team can better balance legacy maintenance with the push for future innovation. 

Kiro for iOS: Engineering on the Move 

The launch of Kiro for iOS (announced June 2026) marks a significant step in making agentic engineering truly mobile. Rather than simply being a companion app, it serves as a native mobile surface for managing Kiro’s cloud-based agent sessions. It allows developers to interact with their AI engineering partner without needing to keep a laptop running or stay connected to a VPN.

Figure 6: Kiro for iOS Introduced at AWS Summit NYC, 2026.

Core Capabilities of Kiro for iOS: 

• Persistent Cloud Sessions: Kiro agents run independently in secure cloud sandboxes. You can kick off a task, close your phone, and let the agent continue its work without needing your machine to stay awake.  

• Three Modes of Operation: Just like the web and IDE versions, the mobile app allows you to select your workflow mode: 
  • Chat: For quick back-and-forth questions and brainstorming.  
  • Spec: To continue structured, spec-driven development workflows while on the go.  
  • Autonomy: To delegate complete tasks (like implementing a feature or triaging a bug) and let the agent own the outcome from planning through to the pull request.  

• Mobile-Optimized Diff Review: Recognizing that standard responsive web layouts are often difficult to read on small screens, Kiro for iOS features native diff rendering. Code edits appear as clean red/green cards with file headers, making it easy to scan and review changes while commuting or away from a desk.  

• Unified Context & Sync: The app is an extension of your existing Kiro environment. Sessions started on the web or in your IDE sync automatically, which means you maintain the same agent identity, repository connections, and project context across all surfaces.  

• Real-Time Monitoring & Approval: You can track agent progress in real-time, view status updates for pull requests across multiple repositories, and provide approvals or steer the agent’s direction directly from your phone.  

If you’re ready to see how these capabilities can transform your workflow, [check out our deep dive into Kiro here] to learn how to start engineering from anywhere. 

Note: Kiro for iOS requires an active Kiro Pro, Pro+, Pro Max, or Power account and iOS 17+ (with some materials noting iOS 26+ requirements for the latest release).

Turning Potential into Production with Cloudelligent: What’s Next for Your Business? 

AWS Summit NYC has set the stage for a truly autonomous future. With solutions like AWS Context for smart data navigation and Continuum for automated security, the infrastructure is officially in place. Yet, possessing the right technology is only the first step. The real business value comes from how you choose to execute your strategy. 

Instead of just watching this industry shift, why not lead it? Consider which parts of your workflow are most ready for automation and let us help you bridge the gap between experimentation and reality.  

Book your FREE Agentic AI Assessment with Cloudelligent to explore how these new additions can integrate into your current environment, eliminate technical debt, and accelerate your time to market. 

FAQs (Frequently Asked Questions)

1. What is continuous modernization in AWS Transform? 

AWS Transform runs seamlessly in the background to give your engineering team their time back. It constantly scans your repositories for outdated dependencies and automatically generates the pull requests needed to keep your codebase completely current.

2. Why would I use Kiro for iOS to manage my AI agents?  

Kiro for iOS lets you handle agent sessions, review code diffs, and approve changes natively from your phone. You can easily kick off a complex task on the go and review the progress whenever it fits your schedule. 

3. What is the difference between standard AI assistants and “agentic engineering”?  

While standard AI tools act as an advanced autocomplete for code snippets, agentic engineering manages entire workflows. Solutions like Amazon Bedrock AgentCore and Kiro allow autonomous agents to execute complex, multi-step tasks, run unit tests, and handle routine maintenance without requiring constant human supervision.