AWS Builder ID now supports Sign in with GitHub and Amazon

AWS Builder ID, your profile for accessing AWS applications including AWS Builder Center, AWS Training and Certification and Kiro, now supports two new social logins: GitHub and Amazon. This expansion of sign-in options builds on the existing Google Apple social sign-in capabilities, providing GitHub and Amazon users with a streamlined way to access AWS resources without managing separate credentials on AWS.
With Sign in with Github and Amazon integration, developers and builders can now enjoy access to their AWS Builder ID profile using their GitHub or Amazon Account credentials. This enhancement eliminates password management complexity, reduces forgotten password issues, and provides a frictionless experience for both new user registration and returning user sign-ins. Whether you’re accessing development resources in AWS Builder Center, enrolling in certification programs or using Kiro to code your next app, your GitHub and Amazon Accounts can now serve as a secure gateway to your builder AWS journey.
To learn more about AWS Builder ID and get started with Sign in with GitHub and Amazon, visit the AWS Builder ID documentation.
Quelle: aws.amazon.com

Amazon Bedrock now supports observability of First Token Latency and Quota Consumption

Amazon Bedrock is a fully managed service for building generative AI applications using high-performing foundation models from leading AI providers. It now supports two new CloudWatch metrics, TimeToFirstToken and EstimatedTPMQuotaUsage, giving you deeper visibility into inference performance and quota consumption.
TimeToFirstToken measures the latency from when a request is sent to when the first token is received, for streaming APIs (ConverseStream and InvokeModelWithResponseStream). You can use this metric to set CloudWatch alarms which monitor latency degradation and establish SLA baselines, without any client-side instrumentation. EstimatedTPMQuotaUsage tracks your estimated Tokens Per Minute (TPM) quota consumption, including cache write tokens and output burndown multipliers, across all inference APIs (Converse, InvokeModel, ConverseStream, and InvokeModelWithResponseStream). You can use this metric to set proactive alarms before reaching your quota limit, track your quota consumption across your models, and request further quota increases before usage is rate limited.
Both metrics are supported in all commercial Bedrock regions for models available via cross-region inference profiles and in-region inference, updated every minute for successfully completed requests. These are available in your CloudWatch out of the box; you pay only for the underlying model inference you consume, with no API changes or opt-in required.
To learn more about TimeToFirstToken and EstimatedTPMQuotaUsage, see our documentation page on Monitoring Amazon Bedrock.
Quelle: aws.amazon.com

Amazon Bedrock AgentCore Runtime now supports stateful MCP server features

Amazon Bedrock AgentCore Runtime now supports stateful Model Context Protocol (MCP) server features, enabling developers to build MCP servers that leverage elicitation, sampling, and progress notifications alongside existing support for resources, prompts, and tools. These capabilities allow MCP servers deployed to AgentCore Runtime to collect user input interactively during tool execution, request LLM-generated content from clients, and provide real-time progress updates for long-running operations. With stateful MCP sessions, each user session runs in a dedicated microVM with isolated resources, and the server maintains session context across multiple interactions using an Mcp-Session-Id header. Elicitation enables server-initiated, multi-turn conversations to gather information such as user preferences. Sampling allows servers to request AI-powered text generation from the client for tasks like personalized recommendations. Progress notifications keep clients informed during operations such as searching for flights or processing bookings. These features work together to support complex, interactive agent workflows that go beyond simple request-response patterns.
Stateful MCP server features are supported in AgentCore Runtime across fourteen AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Canada (Central), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), and Europe (Stockholm).
To learn more, see Stateful MCP server features in the Amazon Bedrock AgentCore documentation.
Quelle: aws.amazon.com

Introducing GPT-5.4 in Microsoft Foundry

Built for Reliable AI Production: Stronger reasoning, dependable execution, and agentic workflows at scaleToday, we’re announcing OpenAI’s GPT‑5.4 to be generally available soon in Microsoft Foundry: a model designed to help organizations move from planning work to reliably completing it in production environments. As AI agents are applied to longer, more complex workflows; consistency and follow‑through become as important as raw intelligence. GPT‑5.4 combines stronger reasoning with built in computer use capabilities to support automation scenarios, and dependable execution across tools, files, and multi‑step workflows at scale.

GPT-5.4: Enhanced Reliability in Production AIGPT-5.4 is designed for organizations operating AI in real production environments, where consistency, instruction adherence, and sustained context are critical to success. The model brings together advances in reasoning, coding, and agentic workflows to help AI systems not only plan tasks but complete them with fewer interruptions and reduced manual oversight.

Compared with earlier generations, GPT-5.4 emphasizes stability across longer interactions, enabling teams to deploy agentic AI with greater confidence in day-to-day production use.

GPT-5.4 introduces advancements that aim for production grade AI:

More consistent reasoning over time, helping maintain intent across multi‑turn and multi‑step interactionsEnhanced instruction alignment to reduce prompt tuning and oversightLatency improved performance for responsive, real-time workflowsIntegrated computer use capabilities for structured orchestration of tools, file access, data extraction, guarded code execution, and agent handoffsMore dependable tool invocation reducing prompt tuning and human oversightHigher‑quality generated artifacts, including documents, spreadsheets, and presentations with more consistent structureTogether, these improvements support AI systems that behave more predictably as tasks grow in length and complexity.

From capability to real-world outcomesGPT‑5.4 delivers practical value across a wide range of production scenarios where follow‑through and reliability are essential:

Agent‑driven workflows, such as customer support, research assistance, and business process automationEnterprise knowledge work, including document drafting, data analysis, and presentation‑ready outputsDeveloper workflows, spanning code generation, refactoring, debugging support, and UI scaffoldingExtended reasoning tasks, where logical consistency must be preserved across longer interactionsTeams benefit from reduced task drift, fewer mid‑workflow failures, and more predictable outcomes when deploying GPT‑5.4 in production.

GPT-5.4 Pro: Deeper analysis for complex decision workflowsGPT‑5.4 Pro, a premium variant designed for scenarios where analytical depth and completeness are prioritized over latency.

Additional capabilities include:

Multi‑path reasoning evaluation, allowing alternative approaches to be explored before selecting a final responseGreater analytical depth, supporting problems with trade‑offs or multiple valid solutionsImproved stability across long reasoning chains, especially in sustained analytical tasksEnhanced decision support, where rigor and thoroughness outweigh speed considerationsOrganizations typically select GPT‑5.4 Pro when deeper analysis is required such as scientific research and complex problems, while GPT‑5.4 remains the right choice for workloads that prioritize reliable execution and agentic follow‑through.

Microsoft Foundry: Enterprise‑Grade Control from Day OneGPT‑5.4 and GPT‑5.4 Pro are available through Microsoft Foundry, which provides the operational controls organizations need to deploy AI responsibly in production environments. Foundry supports policy enforcement, monitoring, version management, and auditability, helping teams manage AI systems throughout their lifecycle.

By deploying GPT‑5.4 through Microsoft Foundry, organizations can integrate advanced agentic capabilities into existing environments while aligning with security, compliance, and operational requirements from day one.

Customer Spotlight

Get Started with GPT-5.4 in Microsoft FoundryGPT‑5.4 sets a new bar for production‑ready AI by combining stronger reasoning with dependable execution. Through enterprise‑grade deployment in Microsoft Foundry, organizations can move beyond experimentation and confidently build AI systems that complete complex work at scale. Computer use capabilities will be introduced shortly after launch.

GPT‑5.4 in Microsoft Foundry is priced at $2.50 per million input tokens, $0.25 per million cached input tokens, and $15.00 per million output tokens. It is available at launch in Standard Global and Standard Data Zone (US), with additional deployment options coming soon. GPT‑5.4 Pro is priced at $30.00 per million input tokens, and $180.00 per million output tokens, and is available at launch in Standard Global.

Build agents for real-world workloads. Start building with GPT‑5.4 in Microsoft Foundry today.
The post Introducing GPT-5.4 in Microsoft Foundry appeared first on Microsoft Azure Blog.
Quelle: Azure

The economics of enterprise AI: What the Forrester TEI study reveals about Microsoft Foundry

Leaders are chasing the AI frontier, reimagining business systems as human-led and agent-operated. To do this, customers are on the hunt for smarter models, more capable agents, and market-ready solutions to operationalize AI workflows.

When Forrester modeled the economics of enterprise AI with Microsoft Foundry, the biggest driver behind the 327% ROI over three years1 was surprising: developer productivity, worth $15.7 million over the same period.

The study showed that the bottleneck to ROI can be removed by enabling developers to focus on what matters.

Read the full Forrester study

The hidden tax on your AI investment

In most organizations, senior engineers spend a third of their time on undifferentiated work: stitching together fragmented tools, recreating context pipelines, and navigating bespoke governance processes. None of that is competitive advantage for firms—it’s a tax on every AI initiative.

According to Forrester, organizations using Foundry avoided much of this work, improving technical team productivity up to 35%. Teams using Foundry to develop AI apps and agents saw payback in as few as six months and with benefits accelerating year over year1.

Learn more about what you can do with Microsoft Foundry

The details: What the Forrester study found

Forrester interviewed 10 decision-makers at five organizations and surveyed 154 other decision-makers and AI leaders across the U.S. and Europe with experience using Microsoft Foundry. They modeled a composite enterprise with $10 billion revenue, 25,000 employees, and 100 technical staff using Foundry. To model conservative estimates, benefits were adjusted downward and costs upward; the results reflect the composite enterprise.

Read the full Forrester study

Figure 1: Survey results and reported benefits

When asked “What benefits has your organization experienced with Microsoft Foundry?”, respondents cited operational outcomes:

Note: These reflect reported experiences, not the financial model. Composite ROI is calculated separately using Forrester’s risk-adjusted methodology. Source: Survey of 154 AI decision-makers, Forrester TEI study, February 2026

Forrester found that platform investments compound in value. For a team that invests $11.6M in resources, the three-year present value of quantified benefits for the composite organization totaled $49.5M: Year one delivered $10.0M, year two $21.1M, year three $30.5M.

Figure 2: Benefits breakdown

Source: The Total Economic Impact™ Of Microsoft Foundry, a commissioned study conducted by Forrester Consulting, February 2026

When every project starts from scratch

AI initiatives will require models, enterprise knowledge, tools, and governance. Without a shared platform, teams will encounter toil. With enterprise knowledge as the example, for every AI project, teams need to create vector databases, RAG pipelines, integrations, and access-control rules, creating internal infrastructure that does not directly influence business outcomes.

75% of teams reported easier model grounding or knowledge source integration

Read the study

With Foundry, teams develop AI applications and agents on a unified, interoperable AI platform designed to enable agents to be intelligent and trustworthy: with reusable knowledge bases on data anywhere in the enterprise, protected by built-in evaluations, and agent controls. In Forrester’s TEI study, 75% of teams cited easier model grounding or knowledge source integration with Foundry IQ.

Over three years, the productivity gain alone was worth up to $15.7 million1. One Foundry customer said,

Our developers can go super fast because they can get what they need in Microsoft Foundry … We estimate that we reduce overall development time by 30%–40%.
—Global head of technology platforms, professional services

Organizations saw compounding returns when they built once and reuse everywhere with shared templates, knowledge bases, standardized evaluations, and consistent governance. This helps to explain a counterintuitive finding: organizations that focused energy consolidating on a unified platform outperformed those which did not. Their execution is simpler and therefore stronger.

The need for platform thinking

Point solutions develop in enterprises over time. Each solves a narrow problem, but each also introduces its own governance layer, context pipeline, and integration surface. The hidden cost here builds up in the stitching between these solutions.

32% were able to decrease costs by decommissioning legacy AI tools

Read the study

In the Forrester study, 32% of surveyed organizations that adopted Foundry were able to decrease costs by decommissioning legacy AI tools, and the composite organization avoided up to $4.3M in infrastructure costs over three years by eliminating duplicative workflows, integrations, and operational overhead. For example, one customer shared they were able to decommission their container-based infrastructure and eliminate spending on previous AI model development tools since the functionality was included in the Foundry platform:

One of the benefits of using Foundry versus taking those models and running them in containers in the cloud is that then you don’t have to manage the container infrastructure.
—Managing director and global head of co-innovation, professional services

Department-level budgets favor point solutions, but enterprise-level outcomes require platform thinking. That mismatch is why AI spend often fails to translate into sustained value as organizations shift from isolated pilots to scaled deployments.

Microsoft Agent Factory
Scale AI and move from ideas to outcomes with one pre-paid plan, expert-led AI skilling, and engineering expertise.

Learn more

Trust unlocks higher-impact work

Most enterprises start with internal-facing AI use cases before they shift to customer-facing solutions. Two-thirds of AI agents today focus on process automation, while one-third support direct human assistance1. The ratio matters. Most enterprises need to trust AI with bounded, auditable tasks before they can trust it to enhance human judgment.

Foundry Control Plane enables organizations to govern the AI lifecycle with organization-wide observability and controls. This includes centrally managed policies for model deployment, configurable guardrails, and continuous evaluations to see what’s running, fix what’s failing, and prove compliance across any environment.

Model scanning done by Microsoft on the models … is a key requirement for us. … we want to make sure we understand what the model contains and whether it contains anything that is not in line with policy.
—Principal product manager, professional services

67% adopted Foundry to reduce concerns with AI security, privacy, and governance

Read the study

It’s no surprise that 67% of surveyed organizations cited concerns with AI security, privacy, or governance as a top reason for adopting Microsoft Foundry, ranking it higher than model access, capabilities, and cost inefficiencies. In essence, trust is a permission slip that enables organizations to expand from isolated process automation projects into higher-impact work at scale.

What leaders should do about AI now

The Forrester TEI study makes one thing unmistakable: enterprise AI ROI compounds when AI is treated as a platform, not a series of one-off projects.

The biggest gains come from giving technical teams a reusable foundation, including models, agents, and tools that scale across use cases and eliminate repetitive work. When AI development becomes repeatable, value accelerates and confidence follows.

Three questions for your next leadership meeting
– How much of your engineering capacity goes toward rebuilding the same foundations vs. building differentiated AI capabilities? If it’s over 20%, you’re paying a hidden tax.– Do your AI initiatives share a common platform for data, evaluation, and governance, or are you scaling fragmentation?– What would it take for your organization to move from isolated automation projects to higher‑impact use cases?

Learn more about the benefits of AI workflows

Read the full Forrester TEI Study.

Build with Microsoft Foundry.

Shift from ideas to outcomes faster with Microsoft Agent Factory.

Read the full Forrester study

The Forrester Total Economic Impact™ study on Microsoft Foundry was commissioned by Microsoft and conducted by Forrester Consulting.

1The Total Economic Impact™ Of Microsoft Foundry, a commissioned study conducted by Forrester Consulting, February 2026

2Represents results for the composite organization
The post The economics of enterprise AI: What the Forrester TEI study reveals about Microsoft Foundry appeared first on Microsoft Azure Blog.
Quelle: Azure