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Scaling AI Agent Infrastructure with the MCP Stateless updates

AINews

Scaling AI Agent Infrastructure with the MCP Stateless updates

The 2026-07-28 Model Context Protocol (MCP) specification replaces legacy stateful constraints with a fully stateless core, enabling cloud-native horizontal scaling, serverless deployments, and standard round-robin load balancing. This architectural shift introduces standardized HTTP headers for efficient routing without deep packet inspection, caching controls, and Multi Round-Trip Requests (MRTR) to handle interactive and long-running tasks without blocking connections. Developers can immediately begin migrating their agentic applications to this highly scalable infrastructure using the newly available beta SDKs for Python, TypeScript, Go, and C#.

Google Developers·August 20, 2026·1 min read

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Model routing with Google Cloud API Gateway

TechnologyOpen Source

Model routing with Google Cloud API Gateway

Google Cloud API Gateway now offers a model routing feature in Public Preview, allowing developers to dynamically route traffic to models like Gemini, Claude, or OpenAI OSS-GPT without hardcoding endpoints or managing open-source proxies. Developers can easily configure these routing rules directly within their OpenAPI 3.x specifications by mapping virtual model names to specific backend targets on a shared host. Once deployed, the Gateway acts as a serverless ingress layer that accepts standard OpenAI-compatible requests, automatically transcodes the payload to the native schema of the target model, and routes the traffic on the fly.

Google Developers·August 20, 2026·1 min read

Scaling real-time AI agents with session-aware load balancing

AINews

Scaling real-time AI agents with session-aware load balancing

Real-time AI agents break traditional request-response load balancing paradigms because they rely on long-lived, stateful bidirectional streams that obscure true server capacity. To solve this, developers must implement application-level session tracking directly within the runtime to accurately measure the committed concurrent workload of active conversations. By feeding these precise session counts alongside standard CPU utilization metrics into a hybrid routing algorithm, infrastructure can effectively distribute stateful AI traffic and prevent individual backend bottlenecks.

Google Developers·August 20, 2026·1 min read

Enable on-demand expertise with Agent Skills in Genkit Go

TechnologyNews

Enable on-demand expertise with Agent Skills in Genkit Go

To prevent context window bloat and reduce token consumption, Genkit Go introduces Agent Skills based on a progressive disclosure architecture. Developers can package specialized instructions, scripts, and references into modular SKILL.md bundles where only the frontmatter metadata is initially exposed to the agent's system prompt. When a task matches the skill's description, Genkit's middleware dynamically loads the full instruction body and associated assets, ensuring the model accesses precise workflows exactly when needed.

Google Developers·August 20, 2026·1 min read

Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA

TechnologyNews

Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA

Agent Platform's evaluation service is now generally available, providing developers with a unified engine to measure agent quality consistently across local development experiments and live production traffic. You can evaluate agents using over 20 pre-built metrics, DeepMind-backed adaptive rubrics, or custom code-based and LLM-as-a-judge metrics stored in a centralized, versioned registry. The service integrates directly into existing workflows via the Agent Platform SDK, agents-cli, and ADK, offering built-in user and environment simulators to automate complex multi-turn testing and streamline CI pipelines.

Google Developers·August 20, 2026·1 min read

How to use Google microbenchmarks for evaluating TPU performance

TechnologyOpen Source

How to use Google microbenchmarks for evaluating TPU performance

Google's open-source TPU microbenchmark suite provides developers with granular performance metrics across Network, Compute, HBM, Host Transfer, and Attention components to validate real-world hardware capabilities. By leveraging these benchmarks to establish a Roofline model, engineers can accurately diagnose whether their machine learning workloads are compute-, memory-, or network-bound. This empirical baseline directly guides targeted software optimizations—such as kernel tuning, mesh sharding, and rematerialization—to maximize hardware utilization for large-scale model deployments.

Google Developers·August 20, 2026·1 min read

Run Ray on TPU, Part 2: Ray AI libraries

AINews

Run Ray on TPU, Part 2: Ray AI libraries

This second installment explores how Ray’s higher-level libraries—Serve, Data, and Train—abstract the complexities of running AI workloads on Google's TPU slices. Ray Serve uses a simple topology configuration to correctly gang-schedule large multi-host models, while Ray Data eliminates data-loading bottlenecks by feeding accelerators directly with native JAX batches. Finally, JaxTrainer streamlines distributed training across TPUs by automatically handling cross-slice coordination, checkpointing, and fault tolerance.

Google Developers·August 20, 2026·1 min read

Build zero-trust AI agents with Google's Agent Development Kit

AINews

Build zero-trust AI agents with Google's Agent Development Kit

Building autonomous AI agents that mutate production state requires moving beyond soft system prompts to a robust zero-trust architecture. To secure Google Agent Development Kit (ADK) workflows against prompt injections and malicious execution, developers must implement hardware-backed cryptographic signatures for database writes, kernel-level sandboxing with gVisor for dynamic code, and deterministic semantic gateways for I/O validation. By enforcing these hard security boundaries at the infrastructure level, you can safely deploy multi-tool AI agents without risking unauthorized data manipulation or server compromise.

Google Developers·August 20, 2026·1 min read

TechnologyNews

Emoji Based Janet Packages and Codebases

1 point 0 comments on Hacker News · hachyderm.io

Hacker News·August 20, 2026·1 min read

When Building Gets Cheap, Knowing What to Build Gets Expensive

TechnologyNews

When Building Gets Cheap, Knowing What to Build Gets Expensive

1 point 0 comments on Hacker News · twitter.com

Hacker News·August 20, 2026·1 min read

Poisoned Postgres Connection Pools

TechnologyNews

Poisoned Postgres Connection Pools

1 point 0 comments on Hacker News · planetscale.com

Hacker News·August 20, 2026·1 min read

The August 17 outage, and the work ahead

ProgrammingNews

The August 17 outage, and the work ahead

An update on the August 17 outage and the steps we're taking to improve reliability. The post The August 17 outage, and the work ahead appeared first on The GitHub Blog .

GitHub Blog·August 20, 2026·3 min read

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