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Serverless
102 posts about Serverless. Every summary links to the original.
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MCP went stateless: Is your AWS MCP server deployment well-architected?
MCP 2026-07-28 removes the initialize handshake and Mcp-Session-Id, making the protocol core stateless and changing how remote servers scale on AWS. Requests carry protocol version and client context, while server/discover can expose versions, capabilities, and identity; continuity remains possible through server-minted identifiers passed in tool arguments and stored application state. It covers round-robin routing, header-based gateway signals, cache freshness fields, W3C Trace Context, idempotent tools, and Multi Round-Trip Requests for input-dependent calls. It recommends retaining sticky routing and session stores for 2025-era clients, then migrating after legacy traffic reaches zero and validating against the official conformance suite. The conclusion is that the revision aligns remote MCP deployments with AWS Well-Architected practices and permits removal of infrastructure required only by the old protocol.
Gallup scales real-time coaching for thousands with Amazon Bedrock
Gallup needed to centralize decades of proprietary workplace performance research to provide proactive, real-time coaching for organizational leaders directly within their daily workflows. To solve this challenge, Gallup developed Gallup AI inside its Gallup Access platform using Amazon Bedrock and Anthropic Claude models. The multi-tenant serverless architecture leverages Amazon Bedrock Knowledge Bases and Amazon Kendra for retrieval-augmented generation, while AWS Lambda with FastAPI powers real-time streaming responses. Amazon ElastiCache Serverless caches recent conversation history for sub-millisecond session retrieval, Amazon RDS for MySQL provides durable transactional storage, and Amazon Bedrock Guardrails enforce content safety. Since its June 2024 launch, the system has achieved sub-second time-to-first-byte streaming latency while prompt volume grew approximately sevenfold across thousands of organizations.
Build a Serverless Video Chat Application with JavaScript and Twilio Programmable Video
This tutorial builds a basic browser-based video conferencing application with JavaScript, HTML, and CSS using Twilio Programmable Video. It targets desktop and mobile web browsers, requiring no installed client, and uses the Twilio Serverless platform to provide hosting and a public URL. The setup creates a Twilio Serverless project, configures ACCOUNT_SID, API_KEY_SID, and API_KEY_SECRET in .env, runs npm start locally, and deploys with twilio serverless:deploy; the client manages joining, participant video streams, disconnection, and participant counts. After deployment, users can open the generated index.html URL on separate devices or share it with others to see participants appear dynamically, while the conclusion notes additional Twilio Video capabilities such as recording, custom data tracks, screen sharing, and virtual backgrounds.
The Cloudflare Blog – Brought to you by EmDash
Cloudflare migrated its official blog to EmDash, an Astro-focused content management system, acting as internal Customer Zero to evaluate scaling and usability. To prepare for traffic spikes reaching thousands of requests per second, engineers used k6 to execute ramp, breakpoint, and burst load tests. The resulting production architecture runs EmDash on Cloudflare Workers, pairing it with Workers Cache, an object cache backed by Workers KV, and Hyperdrive connected to PlanetScale. This layered caching design serves 99.5 percent of static files and 70 percent of requests from cache, flattening p95 latency under real-world loads reaching 850 requests per second. The migration also introduced a frontend redesign using the Kumo design system and added Model Context Protocol servers for automated agent workflows.
Run, debug, and scale Databricks workloads from your local IDE
Databricks introduces updated IDE and CLI integrations that connect VS Code, Cursor, or a terminal directly to Databricks compute, workspace, and Unity Catalog. Existing Databricks Connect supported local Spark development, but remote non-Spark workloads and dependency synchronization remained pain points. The new SSH tunnel supports interactive execution and debugging of Python and SQL workloads on Serverless, AI Runtime, and dedicated clusters, while keeping files and dependencies aligned with Databricks Runtime and the workspace. CLI flags can select serverless, GPU-backed AI Runtime, or a dedicated cluster, and can start the tunnel in VS Code or Cursor; additional options configure base environments and usage policies, while the IDE can browse Unity Catalog assets.
Build a unified AI agent architecture with DynamoDB and Bedrock
AI agent architectures on AWS frequently divide operational records and vector embeddings across separate databases, creating synchronization lag and infrastructure overhead. To resolve this fragmentation, a single DynamoDB table can store operational document attributes alongside embeddings by leveraging native vector search. An Amazon Bedrock agent directs incoming queries to AWS Lambda action groups, which run standard CRUD operations or semantic searches against the DynamoDB SearchVectors API. When items change, a DynamoDB Streams pipeline uses Amazon Titan Text Embeddings V2 to generate new embeddings and write them back to the table. This unified approach eliminates standalone vector databases while keeping semantic indexes synchronized with operational updates.
How AgentFlo built AI sales agents with Amazon Bedrock AgentCore – Part 2
AgentFlo designed an architecture using Amazon Bedrock AgentCore and AWS serverless infrastructure to address traffic spikes and build trust in autonomous commerce agents. Security operates across three stages: AWS Fargate filters prompt injections and opt-outs, AgentCore Gateway enforces deterministic Cedar authorization policies during tool execution, and post-turn privacy filters screen outputs. Session isolation is maintained across merchants through AgentCore runtime microVMs, while AgentCore Observability and Amazon CloudWatch provide end-to-end tracing. Data reliability and long conversation journeys rely on Amazon DynamoDB context storage, Amazon Bedrock Knowledge Bases, and Amazon S3 Vector embeddings. Early deployment data showed a 12 percent net revenue uplift.
How AgentFlo built AI sales agents with Amazon Bedrock AgentCore – Part 1
AgentFlo built autonomous AI sales agents to address cart abandonment, unassisted messaging channels, and severe traffic spikes across eCommerce platforms. The system uses the Strands Agents SDK alongside Amazon Bedrock AgentCore to manage intent, recommend products, and execute transactions. Incoming customer communications pass through AWS Fargate before entering AgentCore runtime, which executes model-driven agents inside isolated microVMs with stateful sessions lasting up to eight hours. Tool execution is brokered separately through AgentCore Gateway to ensure operations like cart modifications scale independently from model reasoning. This deployment model enables merchants to launch specialized conversational agents in minutes without pre-provisioning capacity or maintaining custom container configurations.
A revisit of remote Spectre attacks on Cloudflare Workers
Cloudflare reassessed remote Spectre attack feasibility within Cloudflare Workers production workloads to evaluate defenses against modern stabilization techniques. The experimental proof of concept bypassed Dynamic Process Isolation (DyPrIs) by keeping invocations alive using WebSocket traffic, which also diluted hardware performance counter ratios below detection thresholds. By combining branch mistraining, cache line eviction, and speculative type confusion, researchers transiently accessed out-of-bounds memory via raw 64-bit TypedArray backing store pointers. This attack demonstrated cross-tenant data leakage reaching up to 12 bit/s with over 99% accuracy in production. In response, Cloudflare mitigated the vulnerability by integrating the V8 memory sandbox, deploying Memory Protection Keys for hardware-assisted in-process isolation, and redesigning DyPrIs to monitor active long-lived executions.
Secure all your internal vibe-coded applications — in one click
AI-assisted development enables employees to build applications rapidly, but unmanaged deployments can accidentally expose internal company data to the public Internet. Cloudflare introduced direct Cloudflare Access integration for Cloudflare Workers, allowing organizations to enforce authentication policies directly at the account or individual Worker level rather than per hostname. When enabled, incoming requests are authenticated before reaching application code across custom domains, routes, workers.dev subdomains, and preview environments. Developers can access authenticated identity data such as emails and groups directly via the Worker context object without manually validating JSON Web Tokens. This capability was implemented on Cloudflare's Rust-based FL2 proxy, which separated Worker routing from Worker execution so routing runs safely prior to Access policy enforcement.
Serverless vehicle tracking at scale: Bosch L.OS on AWS
Bosch Mobility Platform Solutions developed the Logistics Operating System on AWS to unify vehicle tracking across fragmented spot logistics markets. The system standardizes multiple telematics providers, disparate data formats, and diverse communication protocols into a centralized visibility layer. Orchestration is handled by a Tracking Connector running on Amazon ECS with AWS Fargate, while AWS Lambda functions act as provider-specific adapters for protocol translation. Event-driven message ingestion and streaming rely on Amazon MSK, complemented by Amazon ElastiCache for low-latency caching and Amazon DynamoDB for compliance policy enforcement. Currently operational in India with ten integrated ISVs, the platform reportedly reduces integration costs by 15–20% and lowers total tracking costs for small transporters by 25–30%.
Everything we launched during Agents Week
Agents Week introduced a series of products and primitives across Cloudflare to support an Agentic Internet where autonomous software and humans interact. Developing capable agents requires addressing technical challenges spanning identity, communication, orchestration, memory, observability, and security. Cloudflare introduced the Agent Development Lifecycle alongside tools like Cloudflare Agents for live tracing, replay, and human-in-the-loop approvals. The releases also included programmable CI/CD pipelines capable of repairing failures, Cloudflare Wallets for agent transactions, and Kitesurf, an agent-first browser running in V8 isolates. New protocols and tools like WebMCP, MCPv2, and Cloudflare AI Search aim to make web data and applications readable, discoverable, and searchable by autonomous software.
Introducing Radar Researcher: An AI tool for exploring Internet data in plain language
Cloudflare launched the beta of Radar Researcher, an AI tool designed to let users explore global Internet traffic data using plain-language queries. Built entirely on Cloudflare's developer platform, the system uses the Cloudflare Agents SDK within a Cloudflare Worker, maintaining conversation state and SQLite databases inside Durable Objects. The inference layer relies on Workers AI running models like Kimi K2.7 through an ordered fallback chain across three model families to prevent outages, routed via AI Gateway for logging, caching, and guardrails. Radar's frontend renders retrieved data using its existing native interactive charts, while smaller dedicated models generate conversation titles and follow-up suggestions asynchronously. Additionally, Radar introduced support for the WebMCP standard, exposing imperative and declarative tools to allow external browser agents to navigate and interact with the platform directly.
Unifying Workers AI and AI Gateway into a single AI control plane
Workers AI and AI Gateway are converging into a unified AI control plane to simplify model access, billing, and observability. Developers can now access both services through a single entrypoint using unified Workers bindings and the /ai/ REST endpoint. Specifying a default gateway automatically instruments requests with payload logging, token tracking, and cost attribution without manual dashboard setup. Cloudflare has also rolled out unified prepaid billing, allowing teams to spend AI Gateway credits on Workers AI models while unlocking higher rate limits. Planned future features include model-first routing for automated provider failover and smart routing powered by task classifiers on Workers AI.
Cloudflare AI Search: give your agents a search engine for your data
Cloudflare introduced developer experience improvements and a preview pricing model for Cloudflare AI Search. Previously, users had to manually connect separate Cloudflare primitives like Workers AI, AI Gateway, Vectorize, R2, and Browser Run to build a search solution. The platform now automates crawling, ingestion, embedding, and retrieval across structured and unstructured data sources. Developers can index sites without sitemaps using link discovery, expose public /search and /mcp endpoints across namespaces, bind search instances directly to Workers, or secure endpoints with Cloudflare Access. Cloudflare uses this system for developer documentation, its corporate blog, and the Cloudflare Dev Stack MCP server to supply coding agents with cited documentation.
The next generation of MCP
Model Context Protocol previously required stateful connections between clients and remote servers, creating operational complexity around sticky sessions, message replay, and open streams. The newly released MCP 2026-07-28 specification transitions MCP to a fully stateless protocol alongside updated SDKs across TypeScript, Python, Go, and C#. The protocol eliminates mandatory handshakes and session identifiers from core request paths while enabling servers to run on request-scoped infrastructure like Cloudflare Workers. Interactive elicitation is restructured through Multi Round-Trip Requests, which allow servers to return input requirements without sustaining persistent connections. Standardized headers for method names and improved OAuth authorization mechanisms further enable standard web infrastructure to inspect and manage MCP traffic efficiently.
Building an open Agentic Internet: readable, discoverable, callable, and payable
The modern web produces billions of bot requests that repeatedly fetch unchanged pages, highlighting an architecture fundamentally misaligned with automated visitors acting on behalf of paying users. Cloudflare outlines an open Agentic Internet structured around four foundational capabilities: readability, discoverability, callability, and payability. Identity and access rely on open specifications such as Web Bot Auth and Private Access Control Tokens, while Markdown for Agents and the Kitesurf browser reduce token overhead and context window bloat. For execution and monetization, WebMCP exposes structured tools directly through the browser, and protocols like x402 paired with Monetization Gateway enable per-fetch transactions. These open standards allow domain owners to define tool contracts, verify bot identities, and monetize agent traffic without relying on legacy advertising models.
Baseten on Hugging Face Inference Providers 🔥
Baseten is now a supported Inference Provider on the Hugging Face Hub, extending serverless inference on model pages and through the Hugging Face Python and JavaScript SDKs. As part of the initial integration, it supports conversational and text-generation tasks, including access to open-weight models such as Kimi K3, latest DeepSeek V4 Flash, and GLM-5.2, with additional tasks planned to roll out soon. Developers can use a custom provider key for direct calls or authenticate with a Hugging Face token so requests are routed to Baseten; model pages and account settings support provider compatibility and preference ordering. Python and JavaScript OpenAI-compatible examples use the Hugging Face router, while integrated agent harnesses can access Baseten-hosted models without extra glue code.
Scaling AI Agent Infrastructure with the MCP Stateless updates
The 2026-07-28 Model Context Protocol specification release candidate addresses production bottlenecks caused by the earlier session-oriented HTTP model, including session pinning, sticky routing, lost state, and Redis-backed infrastructure. It removes the initialize/initialized handshake and Mcp-Session-Id, carrying protocol version, client information, and capabilities in _meta on each request while exposing method and name through HTTP headers. The update also introduces ttlMs/cacheScope caching, Multi Round-Trip Requests for elicitations and task progress, issuer and resource-indicator verification, and full JSON Schema 2020-12 support. The post says the stateless core enables ordinary load balancing, serverless deployment, and transparent failover, while SDK beta releases for TypeScript, Python, Go, and C# support migration.
Bringing serverless functions closer to the speed of wire
Modal’s legacy I/O plane routed all function inputs and outputs through us-east servers, adding up to ~200ms of network latency for calls from other regions. The new geographically distributed I/O plane, available in four or more regions, reduces end-to-end function call latencies by roughly 80ms at p50. The redesign involved moving non-critical work to background async processing, minimizing shared storage interactions on the hot path, and rewriting the server layer in Go for better concurrency. The team evaluated Valkey but chose Redis 7.1 because engine CPU usage spiked aggressively on Redis 7.2 onwards during input queue load testing. Users can further optimize by selecting the nearest routing_region, keeping input and output payloads under 2MiB to avoid blob storage uploads, and batching small inputs to reduce network hops.
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