aws·Jun 17, 2026·3 min readCurated summary

Top announcements of the AWS Summit in New York, 2026 | Amazon Web Services

AWS Summit New York 2026 focused on making AI agents more capable, secure, autonomous, and easier to operate in production. Announcements spanned Amazon Bedrock AgentCore, security automation, developer tooling, workplace agents, and S3 data management. The overall direction is toward agents that can access governed knowledge, act independently, and continuously improve while remaining subject to enterprise controls. ## Building More Capable Agents - **Amazon Bedrock Managed Knowledge Base** simplifies enterprise RAG with native data connectors, Smart Parsing for multi-format data, and an Agentic Retriever for complex, multi-step queries. - **Web Search for Bedrock AgentCore** provides managed, current, cited web knowledge while keeping data within the customer’s secured AWS environment. - **Bedrock AgentCore harness** is now generally available, allowing developers to define an agent’s model, tools, skills, and instructions through configuration rather than custom orchestration loops. - **AWS Context**, coming soon, will map relationships across organizational data into a knowledge graph. Agents will be able to use governed relationships, business rules, and domain knowledge at runtime. - **AWS WAF Bot Control** lets publishers and content owners price, meter, and collect payment from AI bots accessing content and APIs, with scoped access enforced at the edge. ## Securing Agents and Applications - **AWS Continuum**, available in gated preview, aggregates vulnerability findings, ranks them by business impact, verifies exploitability, and routes fixes through existing development processes. - **AWS Security Agent**, now part of Continuum, adds: - Threat modeling based on the STRIDE framework - Pull-request code scanning and remediation across major Git platforms - IDE integrations through Kiro, Claude Code, and MCP - These tools are intended to let developers perform security reviews and address vulnerabilities without leaving their normal workflows. ## AI-Assisted Software Development - **Kiro for iOS**, in gated preview, lets developers start, monitor, steer, and approve Kiro sessions from a phone, including reviewing diffs and approving changes without keeping a laptop running. - **AWS DevOps Agent** adds release readiness reviews and autonomous release testing. It evaluates changes against natural-language standards and tests them in production-like environments. - **AWS Transform continuous modernization**, in preview, scans repositories against configurable technical-debt baselines and can autonomously generate remediation pull requests. ## Autonomous Workplace Agents - New **Amazon Quick autonomous agents** can work in the background with defined expertise, tone, permissions, and tools. - Example use cases include: - A finance agent processing incoming orders - A sales agent analyzing CRM, email, and Slack activity - Agents drafting follow-ups, identifying risks, and recommending next steps - A new **activity feed** combines email, messaging, calendars, and tasks into a prioritized view that adapts to the user’s communication and work patterns. ## Richer Metadata for Amazon S3 - **S3 annotations** allow up to 1 GB of mutable, queryable context to be attached directly to an object. - The feature targets AI agents and autonomous workflows that need to discover and interpret data without maintaining separate metadata systems. AWS’s announcements point toward an agent ecosystem that combines managed knowledge retrieval, web access, security automation, autonomous development workflows, and persistent workplace assistance. Organizations adopting these capabilities should pair autonomy with strong governance, scoped permissions, and continuous validation in production.

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Knowledge GraphsAI AgentsAmazon BedrockRAG+4

dropbox·Jan 28, 2026·3 min readCurated summary

Engineering VP Josh Clemm on how we use knowledge graphs, MCP, and DSPy in Dash

Dropbox VP Josh Clemm argues that useful workplace AI requires a unified context engine capable of securely understanding and retrieving information across many SaaS applications. Dropbox Dash combines custom connectors, multimodal content processing, knowledge graphs, hybrid search, and personalized access-control-aware ranking to make that possible. Clemm favors indexed retrieval over purely federated approaches because preprocessing enables richer context, faster search, and company-wide access, though it requires substantial engineering and infrastructure. ## Building Dash’s Context Engine - Custom connectors crawl third-party applications while handling: - Rate limits - API differences - Application-specific permissions and ACLs - Incoming content is normalized, often into Markdown, and enriched with: - Titles and metadata - Extracted links - Embeddings - Other key information - Different media types require different processing: - Documents can be text-extracted and indexed. - Images may require CLIP or multimodal models. - PDFs combine text, figures, and other elements. - Audio is transcribed. - Videos may require scene-by-scene multimodal analysis when dialogue is insufficient. - Dash models relationships between content as a knowledge graph: - Meetings can connect to documents, participants, transcripts, and previous notes. - Cross-application relationships provide richer context for search and agents. - Data is stored in secure systems using: - BM25 lexical search - Dense-vector storage - Hybrid retrieval - Multiple ranking stages personalize results and enforce user-specific permissions. ## Indexed Retrieval Versus Federated Retrieval - Federated retrieval queries external systems at runtime. - Its advantages include: - Fast initial implementation - Minimal storage requirements - Relatively fresh data - Easy addition of MCP servers and connectors - Its drawbacks include: - Inconsistent API speed, quality, and ranking - Limited access to company-wide content - Expensive post-processing and reranking - Large token usage when agents reason over returned results - Indexed retrieval preprocesses content during ingestion. - Its advantages include: - Access to shared company connectors - Enriched datasets created offline - Faster queries - More opportunities for recall and ranking experiments - Its costs include: - Significant custom connector development - Freshness challenges - High hosting costs - Difficult storage and architecture choices involving vector search, BM25, hybrid retrieval, or graph RAG. ## Making MCP Practical at Scale - MCP can simplify tool integration, but tool definitions consume substantial context-window space. - Large tool descriptions and retrieval results contribute to context rot and reduce agent effectiveness. - Dash aims to limit context usage to roughly 100,000 tokens. - MCP-based agents can also be slow: simple queries may take up to 45 seconds, while direct index retrieval returns results within seconds. - Dropbox’s approach is to wrap its index in a consolidated “super tool,” reducing the need to expose many separate tools to the agent. ## Broader AI Engineering Practices - The talk also covers Dropbox’s use of: - LLMs as evaluators or judges - Prompt optimization with DSPy - Tool calling and MCP design - These techniques complement the underlying context engine rather than replacing the indexing, enrichment, graph modeling, and permission systems required for reliable workplace AI. A practical takeaway is that organizations building AI over proprietary data should treat retrieval as a full data-platform problem. Start with robust connectors and permissions, enrich content before retrieval, model relationships across sources, and use MCP selectively where indexed retrieval can provide faster and more controlled results.

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Knowledge GraphsModel Context ProtocolRAGMultimodal AI+4