Search “AWS”

AWS says Amazon Connect has been named a Leader in the CCaaS Magic Quadrant for the fourth consecutive year

On October 28, 2026, AWS announced that its Amazon Connect has been named a Leader in the 2026 Gartner Magic Quadrant for Contact Center as a Service (CCaaS) for the fourth consecutive year. In an official blog post, AWS introduced that Amazon Connect combines customer context with generative and agentic AI, emphasizing driving complex issues all the way through to resolution rather than merely transferring calls, with every step auditable and human intervention available when necessary. Currently, this news comes only from AWS's official source, with no other independent reports or subsequent developments yet.

AWS says Amazon Connect named a Leader in the CCaaS Magic Quadrant for the fourth consecutive year

On October 28, 2026, AWS announced that its Amazon Connect has been named a Leader in the 2026 Gartner Magic Quadrant for Contact Center as a Service (CCaaS) for the fourth consecutive year. In an official blog post, AWS introduced that Amazon Connect combines customer context with generative and agentic AI, emphasizing driving complex issues all the way through to resolution rather than merely transferring calls, with every step auditable and human intervention available when necessary. Currently, this news comes only from AWS's official source, with no other independent reports or subsequent developments yet.

View story:AWS says Amazon Connect has been named a Leader in the CCaaS Magic Quadrant for the fourth consecutive year

AWS releases an edge vision model lifecycle solution based on SageMaker SDK v3

On October 27, 2026, AWS released a fleet-level edge computer vision model lifecycle solution based on the SageMaker Python SDK v3. The solution replaces the deprecated SageMaker Edge Manager with SageMaker Neo for edge optimization, and uses a dual-branch SageMaker Pipeline to train an anomaly detector and a ResNet18 defect classifier for each camera; after passing quality gates, the models are compiled and registered to the Model Registry, with Step Functions controlling the fleet training cadence. AWS states that a full pipeline run on synthetic data costs approximately $0.07 in compute. There are currently no further developments.

AWS releases edge vision model lifecycle solution based on SageMaker SDK v3

On October 27, 2026, AWS released a fleet-level edge computer vision model lifecycle solution based on the SageMaker Python SDK v3. The solution replaces the deprecated SageMaker Edge Manager with SageMaker Neo for edge optimization, and uses a dual-branch SageMaker Pipeline to train an anomaly detector and a ResNet18 defect classifier for each camera; after passing quality gates, the models are compiled and registered to the Model Registry, with Step Functions controlling the fleet training cadence. AWS states that a full pipeline run on synthetic data costs approximately $0.07 in compute. There are currently no further developments.

View story:AWS releases an edge vision model lifecycle solution based on SageMaker SDK v3

Jassy says most AWS capacity is already booked through 2028

Amazon CEO Andy Jassy said that most of AWS's capacity is already booked through 2028. In an interview on the Andreessen Horowitz podcast, he revealed that AWS's large-scale compute capacity for 2026 has been fully booked, 2027 is essentially sold out, and a considerable portion of 2028 has also been locked in early by customers. Jassy mentioned that Amazon disclosed earlier this year that capital expenditure for 2026 will reach about $220 billion, mainly directed toward data centers, servers, and networking equipment; but he also said that data centers, power, and memory resources are tight, and the shortage may not end in 2027. He also said that 99% of AWS's top 1,000 EC2 customers already use its self-developed Graviton processor, that Trainium chips offer about 30% to 40% better price-performance than comparable processors, and that AI inference costs still need to fall by about tenfold to drive enterprise-scale adoption.

Jassy said most of AWS's capacity is already booked through 2028

Amazon CEO Andy Jassy said that most of AWS's capacity is already booked through 2028. In an interview on the Andreessen Horowitz podcast, he revealed that AWS's large-scale compute capacity for 2026 has been fully booked, 2027 is essentially sold out, and a considerable portion of 2028 has also been locked in early by customers. Jassy mentioned that Amazon disclosed earlier this year that capital expenditure for 2026 will reach about $220 billion, mainly directed toward data centers, servers, and networking equipment; but he also said that data centers, power, and memory resources are tight, and the shortage may not end in 2027. He also said that 99% of AWS's top 1,000 EC2 customers already use its self-developed Graviton processor, that Trainium chips offer about 30% to 40% better price-performance than comparable processors, and that AI inference costs still need to fall by about tenfold to drive enterprise-scale adoption.

View story:Jassy says most AWS capacity is already booked through 2028

AWS makes Continuum code vulnerability detection and remediation generally available

On October 23, 2026, AWS made Continuum's code vulnerability detection and remediation capabilities generally available. The service can find and verify vulnerabilities in code repositories, combine AWS environment information to determine whether an attacker can reach the vulnerability and what resources could be further accessed, rank them by business impact, and provide remediation recommendations; when reproducing vulnerabilities in an isolated sandbox, it provides PoC code and reproduction steps, and fixes are submitted as pull requests to the associated GitHub repository, requiring human review before being applied. Currently, this capability is available only in the US East (N. Virginia) and US West (Oregon) regions. In a benchmark announced on October 5, AWS said that Continuum completed 819 of 920 tasks on CyberGym-E2E, with an end-to-end success rate of 89.0%, compared with the previous publicly reported best of 65.9%; however, the proportion of patches that also fixed the historical vulnerabilities specified by the benchmark was only 37.8%, compared with the previous publicly reported best of 26.2%. The benchmark covers only C/C++ memory safety vulnerabilities in 139 open-source projects, and the tests were conducted by AWS itself.

AWS makes Continuum code vulnerability detection and remediation generally available

On October 23, 2026, AWS made Continuum's code vulnerability detection and remediation capabilities generally available. The service can find and verify vulnerabilities in code repositories, combine AWS environment information to determine whether an attacker can reach the vulnerability and what resources could be further accessed, rank them by business impact, and provide remediation recommendations; when reproducing vulnerabilities in an isolated sandbox, it provides PoC code and reproduction steps, and fixes are submitted as pull requests to the associated GitHub repository, requiring human review before being applied. Currently, this capability is available only in the US East (N. Virginia) and US West (Oregon) regions. In a benchmark announced on October 5, AWS said that Continuum completed 819 of 920 tasks on CyberGym-E2E, with an end-to-end success rate of 89.0%, compared with the previous publicly reported best of 65.9%; however, the proportion of patches that also fixed the historical vulnerabilities specified by the benchmark was only 37.8%, compared with the previous publicly reported best of 26.2%. The benchmark covers only C/C++ memory safety vulnerabilities in 139 open-source projects, and the tests were conducted by AWS itself.

View story:AWS makes Continuum code vulnerability detection and remediation generally available

LMSYS releases SGLang AWS Trainium plugin Beta

On October 22, 2026, LMSYS partnered with AWS to release the SGLang Trainium plugin Beta. Built on the newly released TorchNeuron, the plugin supports LLM inference serving and RL rollout generation on Trainium2 and Trainium3. Initial coverage includes Qwen3 (dense), Qwen3-MoE-30B-A3B, DeepSeek-V2-Lite, GLM-4, and GLM-5.2/5.3, and it supports data parallelism, tensor parallelism, expert parallelism, Radix Cache, BF16/FP8, and asynchronous scheduling. In the first end-to-end heterogeneous RL run, SGLang generated rollouts for Qwen3-MoE-30B-A3B on Trainium3, while Miles trained a GRPO policy with Megatron on a single H200 node. The Beta is currently limited to a small number of teams. The next day, Amazon announced the open-sourcing of TorchNeuron, a backend that converts PyTorch code into operations executable on AWS Trainium, allowing PyTorch code already running on other accelerators to migrate with almost no changes.

AWS publishes tutorial on deriving autoscaling targets for TTS on SageMaker AI

On October 23, 2026, AWS published a tutorial introducing a method for deriving autoscaling targets for TTS on Amazon SageMaker AI. The tutorial deploys the 82M-parameter TTS model Kokoro-82M as a custom container on a SageMaker AI real-time endpoint, and uses measured Q_max (the maximum concurrency a single instance can sustain within the SLO) and T_total (the end-to-end time of one scale-out) to calculate the scale-out threshold, scale-in threshold, and cooldown period, replacing target values set by guesswork. The solution scales on the ConcurrentRequestsPerModel metric natively published by SageMaker AI, avoiding the hidden failure of custom metrics that no one reports. There are currently no further developments.

AWS publishes tutorial on deriving auto-scaling targets for TTS on SageMaker AI

On October 23, 2026, AWS published a tutorial introducing a method for deriving autoscaling targets for TTS on Amazon SageMaker AI. The tutorial deploys the 82M-parameter TTS model Kokoro-82M as a custom container on a SageMaker AI real-time endpoint, and uses measured Q_max (the maximum concurrency a single instance can sustain within the SLO) and T_total (the end-to-end time of one scale-out) to calculate the scale-out threshold, scale-in threshold, and cooldown period, replacing target values set by guesswork. The solution scales on the ConcurrentRequestsPerModel metric natively published by SageMaker AI, avoiding the hidden failure of custom metrics that no one reports. There are currently no further developments.

View story:AWS publishes tutorial on deriving autoscaling targets for TTS on SageMaker AI

LMSYS releases SGLang AWS Trainium plugin Beta

On October 22, 2026, LMSYS partnered with AWS to release the SGLang Trainium plugin Beta. Built on the newly released TorchNeuron, the plugin supports LLM inference serving and RL rollout generation on Trainium2 and Trainium3. Initial coverage includes Qwen3 (dense), Qwen3-MoE-30B-A3B, DeepSeek-V2-Lite, GLM-4, and GLM-5.2/5.3, and it supports data parallelism, tensor parallelism, expert parallelism, Radix Cache, BF16/FP8, and asynchronous scheduling. In the first end-to-end heterogeneous RL run, SGLang generated rollouts for Qwen3-MoE-30B-A3B on Trainium3, while Miles trained a GRPO policy with Megatron on a single H200 node. The Beta is currently limited to a small number of teams. The next day, Amazon announced the open-sourcing of TorchNeuron, a backend that converts PyTorch code into operations executable on AWS Trainium, allowing PyTorch code already running on other accelerators to migrate with almost no changes.

View story:LMSYS releases SGLang AWS Trainium plugin Beta

AWS Solutions Architecture team builds multi-agent system Deep Insight

The AWS Solutions Architecture team built and open-sourced the multi-agent system Deep Insight, which can transform CSV data and natural language prompts into cited .docx analysis reports. The system consists of eight agents—Coordinator, Planner, Supervisor, Coder, Validator, Reporter, Tracker, and others—collaborating in layers, controlling execution flow based on the Graph and Agents-as-Tools patterns of the Strands Agents SDK, and selecting Claude Haiku, Opus, and Sonnet respectively by role. Production deployment relies on the Amazon Bedrock AgentCore runtime; LLM-generated code is executed in isolation in per-session temporary containers on AWS Fargate, accompanied by a Web UI and Ops dashboard. Currently this fact is reported only by a single source, AWS Machine Learning, and there are no subsequent developments.

AWS Solutions Architecture team builds multi-agent system Deep Insight

The AWS Solutions Architecture team built and open-sourced the multi-agent system Deep Insight, which can transform CSV data and natural language prompts into cited .docx analysis reports. The system consists of eight agents—Coordinator, Planner, Supervisor, Coder, Validator, Reporter, Tracker, and others—collaborating in layers, controlling execution flow based on the Graph and Agents-as-Tools patterns of the Strands Agents SDK, and selecting Claude Haiku, Opus, and Sonnet respectively by role. Production deployment relies on the Amazon Bedrock AgentCore runtime; LLM-generated code is executed in isolation in per-session temporary containers on AWS Fargate, accompanied by a Web UI and Ops dashboard. Currently this fact is reported only by a single source, AWS Machine Learning, and there are no subsequent developments.

View story:AWS Solutions Architecture team builds multi-agent system Deep Insight

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i14 migrates its custom architecture to AWS Trainium2

i14 migrated its custom architecture to AWS Trainium2. According to information released by AWS Machine Learning on October 21, 2026, i14 collaborated with AWS to train and deploy the Gated Linear Attention-based Laplace 3B model on AWS Trainium2 using TorchNeuron Native. Training throughput on a single trn2.48xlarge is approximately 120,000 tokens/s; the model's recurrent state is fixed at 29.5 MB per sequence and does not grow with context length; inference reaches 11,544 output tokens/s at 1,280 concurrent sequences. The team also reworked Muon's all_to_all communication, reducing per-card communication volume by 24x and increasing training throughput by 1.63x. There are currently no subsequent developments.

View story:i14 migrates its custom architecture to AWS Trainium2

AWS presents: Securing AI agent identities with Keyfactor EJBCA and AWS services

On October 21, 2026, AWS presented a solution for securing AI agent identities using Keyfactor EJBCA combined with AWS services. According to an article published by AWS Machine Learning, AWS and Keyfactor jointly released a reference architecture that uses Keyfactor EJBCA's X.509 PKI to establish verifiable, revocable cryptographic identities for AI agents, filling the gap left by MCP's lack of a built-in authentication standard. The solution combines Amazon Bedrock, the AWS Strands Agents SDK, AWS Secrets Manager, and Amazon RDS, and covers three trust boundaries—user authentication, agent-to-agent communication, and MCP tool invocation—through four layers of mechanisms: signed request envelopes, user-signed delegation assertions, RFC 8693 audience-restricted tokens, and real-time OCSP revocation checks. Currently, the solution is presented publicly only as a reference architecture, and there are no public facts regarding subsequent implementation progress.

View story:AWS presents using Keyfactor EJBCA and AWS services to secure AI agent identities

i14 migrates its custom architecture to AWS Trainium2

i14 migrated its custom architecture to AWS Trainium2. According to information released by AWS Machine Learning on October 21, 2026, i14 collaborated with AWS to train and deploy the Gated Linear Attention-based Laplace 3B model on AWS Trainium2 using TorchNeuron Native. Training throughput on a single trn2.48xlarge is approximately 120,000 tokens/s; the model's recurrent state is fixed at 29.5 MB per sequence and does not grow with context length; inference reaches 11,544 output tokens/s at 1,280 concurrent sequences. The team also reworked Muon's all_to_all communication, reducing per-card communication volume by 24x and increasing training throughput by 1.63x. There are currently no subsequent developments.

AWS presents using Keyfactor EJBCA and AWS services to secure AI agent identities

On October 21, 2026, AWS presented a solution for securing AI agent identities using Keyfactor EJBCA combined with AWS services. According to an article published by AWS Machine Learning, AWS and Keyfactor jointly released a reference architecture that uses Keyfactor EJBCA's X.509 PKI to establish verifiable, revocable cryptographic identities for AI agents, filling the gap left by MCP's lack of a built-in authentication standard. The solution combines Amazon Bedrock, the AWS Strands Agents SDK, AWS Secrets Manager, and Amazon RDS, and covers three trust boundaries—user authentication, agent-to-agent communication, and MCP tool invocation—through four layers of mechanisms: signed request envelopes, user-signed delegation assertions, RFC 8693 audience-restricted tokens, and real-time OCSP revocation checks. Currently, the solution is presented publicly only as a reference architecture, and there are no public facts regarding subsequent implementation progress.