## Atmos Energy's Major Winter Assistance Initiative

**Atmos Energy** has announced a significant **$2 million donation** to help families struggling with winter heating costs. This substantial contribution comes on top of a previous $1.5 million donation made in September, demonstrating the company's ongoing commitment to community support during the challenging winter months.

### Where the Funds Are Going

The funding will be distributed through **local nonprofit agencies** across Kentucky to provide three key types of assistance:

- **Bill assistance** for past-due natural gas bills
- **Home weatherization** to improve energy efficiency
- **Repair or replacement** of customer-owned natural gas appliances and piping

### Supporting Vulnerable Communities

Public Affairs Manager **Kay Coomes** emphasized that this initiative is part of Atmos Energy's **Fueling Safe and Thriving Communities** program, which specifically targets:

- The elderly
- Individuals with disabilities
- Veterans
- Families experiencing financial hardship
- Other eligible individuals needing urgent access to essential resources

"Fueling hope and growth for neighbors in need is a deeply rooted part of our **AtmoSpirit culture**," Coomes stated. "We are proud to make this new contribution on top of the funds we donated in September."

### Kentucky Agencies Receiving Funding

Several Kentucky organizations will distribute the assistance funds, including:

- Audubon Area Community Services
- The Help Office of Owensboro
- Bluegrass Community Action Agency
- Community Action of Southern Kentucky
- Lake Cumberland Community Action Agency
- Pennyrile Allied Community Services
- The Salvation Army/Kentucky
- West Kentucky Allied Services

### How to Access Assistance

Customers in need of help have several options:

1. **Call 211** to connect with local assistance agencies
2. Visit **atmosenergy.com/assistance** for direct connections to help
3. Contact Atmos customer service at **888-286-6700** for help with monthly payments or service reconnection
4. Use the online **Account Center** for additional support options

### Winter Preparation Recommendations

Atmos Energy encourages all customers to review their **Winter Warm-Up Checklist** as a quick and easy way to prepare households for cold weather. The company remains committed to delivering **safe, reliable, and efficient natural gas service** throughout the winter heating season.

### Additional Support Opportunities

For those who want to help others, Atmos suggests considering a **Sharing the Warmth donation** online. Customers should also explore additional financial assistance through:

- Federal programs like **LIHEAP**
- State and local assistance programs
- Resources supported by the **National Energy & Utility Affordability Coalition**

![Atmos Energy Winter Assistance](https://cdn.owensborotimes.com/wp-content/uploads/2020/10/OT-Atmos-1-750x375.jpg)

*Graphic by Owensboro Times*

Atmos Energy is donating an additional $2 million to help families cover past-due natural gas bills and prepare for the winter heating season.

Atmos Energy's $2M Winter Relief: How This Utility Giant Is Helping Families Stay Warm This Season

I’m wandering through the halls of the Venetian at AWS re:Invent, and I’ve got this nagging thought about contact centers. We’ve always called them **cost centers**, haven’t we? Places where we send money to disappear, or at least to be spent apologizing to people for late deliveries.

But sitting in session BIZ219 at this year’s AWS re:Invent with Ayesha Borker, Principal Solution Architect and Jack Hutton, Principal Product Manager within the Amazon Connect at AWS, I began to wonder if we’ve got it all wrong.

They’re pitching **Amazon Connect** not just as a way to fix problems but as a **growth engine**. It’s a bit of a pivot. We are moving from the era of “How quickly can I get you off the phone?” to “What else can I help you buy?” And it’s powered by **AI**.

## Importing 25 Years of Retail Wisdom

The most compelling part of the pitch wasn’t actually the new features. It was the history lesson.

Ayesha Borker reminded the room that **Amazon has been rehearsing for this moment since 1998**. That was when they launched their first recommendation engine—the “customers who bought this also bought that” feature that we now take for granted.

For 25 years, the Amazon Marketplace has been a massive laboratory for understanding human intent. They have learned how to predict what you want before you know you want it. They have learned how to reduce churn by showing you the right alternative when your size is out of stock.

Now, they are simply lifting that entire infrastructure—the algorithms, the predictive attributes, the “item-to-item” filtering—and dropping it directly into **Amazon Connect**.

It solves the classic problem Ayesha highlighted:

> “I’ve got the data, but I don’t know what to do with it.”

By baking models like “Frequently Paired,” “Trending Now,” and “Recommended for You” into the contact center, they are giving agents the same superpowers that power the Amazon homepage.

## The “Stella” Effect

In the session, they showed a demo involving an AI stylist named “Stella.” A customer, Maya, was looking for a dress for a wedding in Napa. Stella didn’t just search for “dress.” She checked the context. She realized it was an outdoor wedding. She suggested a lighter fabric.

When Maya picked the dress, the “Frequently Paired” algorithm immediately popped up with a matching necklace.

It was smooth.

For the agent on the other end, this changes the game. Instead of scrambling to find a solution, they have a “whisper” in their ear offering a **revenue opportunity**. It turns a service recovery call into a potential sale.

## The “Help Me Decide” Factor

One of the more human touches Jack mentioned was the “Help Me Decide” feature. We all suffer from decision fatigue. You look for a flight. There are fifty options. You end up closing the laptop in frustration.

Amazon Connect is trying to replicate that old-school shop assistant who knew your taste. By embedding **generative AI agents** into the app or web chat, they are moving from passive search to active curation. It removes the mental load for the customer. It’s less about “Here is a list of ten things” and more about “Here is the one thing you actually need.”

## Cleaning the Plumbing Before Painting the House

Of course, none of this works if your data is a mess. And let’s be honest, it usually is.

Jack dropped a rather startling stat from United Airlines. During a review, they found duplicate entries for 50% of their loyal customers. That is a bit of a muddle. You can’t offer hyper-personalized service if you don’t know that the person calling is the same person who emailed yesterday.

This is where the new “Entity Resolution” feature comes in. It’s the unsexy plumbing that makes the magic possible. By using AI to scan and resolve these duplicates, United reduced those errors by 35% and cut infrastructure costs by 30%. It’s a quiet win, but a necessary one.

The numbers Ayesha and Jack shared were rather compelling. They noted that organizations implementing this level of personalization are seeing a **10-15% lift in revenue**. GoStudent, another customer cited, saw a 20% increase in engagement activity by better matching students to tutors using these profiles.

It leaves you with the sense that the contact center is finally growing up. It’s no longer just the department that cleans up the mess. It’s becoming the department that drives the business forward. And for the agents on the frontline, that might just make the job a little more interesting.

For all our event coverage, announcements and exclusive interviews visit our [AWS re:Invent news hub here](https://cxtoday.com/tag/aws-reinvent/)

AWS re:Invent 2025: Ayesha Borker and Jack Hutton show how Amazon Connect uses 25 years of retail AI to transform contact centers from cost centers into revenue drivers.

From Cost Center to Revenue Engine: How Amazon Connect is Revolutionizing Contact Centers with 25 Years of AI

We're witnessing the AI revolution in customer support as it happens.

From decades of customer support defined by reaction to calls, tickets, or queues, to the evolution of proactive support with pre-AI digital platforms, to the current **AI-powered ecosystem** that is redefining how support teams strategize, operate, and deliver resolutions. **AI-enabled support anticipates needs, predicts failures, and delivers instant, seamless resolutions at scale**.

And most importantly, this shift is as transformational as it is technological.

## Keeping pace with transformation

As customers navigate complex and ambitious transformation projects, whether it's moving to the cloud, scaling **AI**, or modernizing complex operations, there is always a quiet mandate: systems supporting critical business processes must run smoothly because the costs of downtimes have never been higher.

For businesses, uninterrupted operations are non-negotiable. **SAP's AI-driven support can anticipate issues before they arise**, helping to ensure critical processes run smoothly, even during high-volume peak events. SAP uses SAP Business AI to help prevent issues proactively, working to ensure a smooth experience by avoiding system outages, platform scalability issues, data overloads, or service overloads. During the peak sales event of Cyber Week 2024, SAP achieved **100% uptime for SAP Commerce Cloud customers**. As the Cyber Week 2025 numbers come in, we already have delivered 100% uptime and improved GMV for global sales events like Singles Day (GMV reached €7,108.72M, or +180.2% YoY, with 6,315.99K orders, or +46.4% YoY) and El Buen Fin (GMV hit €12,341.70M, or +13.18% YoY, and 10,385.74K orders, or +32.24% YoY).

## Scaling self-service with AI

Structured knowledge and curated content enable SAP to build AI and AI agents with high confidence levels. Today, **over 82% of customer issues are addressed via self-service**. This allows users to get instant resolution to issues or bridge knowledge gaps they face during the use, implementation, and continuous improvement of SAP's solutions.

## AI in instant response and resolution

When it comes to delivering instant response and resolution in customer support, the impact of AI-integrated services is remarkable. When SAP's **Auto Response Agent** is highly confident of the solution, based on the underlying data and knowledge, it can deliver highly relevant solutions that can save customers significant time and effort. Additionally, the **first contact resolution rate for cases answered automatically by the agent is at par with what human-human support interactions achieve**.

## Supporting SAP Business AI

SAP Business AI supportability is all about making AI real for customers through the right systems that drive successful adoption. As SAP delivers AI capabilities across its portfolio, we enable customers to have the right support when they encounter issues in early deployment.

As customers scale AI across their organizations, we have concrete processes and tools to help support them, so they can deploy new AI with the utmost confidence. For example, the **Incident Solution Matching service is integrated with SAP Joule for Consultants**, allowing efficient support information retrieval and helping to eliminate the hassle of searching through vast amounts of SAP documentation.

## Empowering support engineers with AI

AI is not just transforming customer outcomes, it's also transforming how our engineers and experts deliver precision and speed, freeing them from logistical tasks so they can focus on support requests that need specialized attention. Thanks to SAP's AI-integrated self-service offerings, we're able to **instantly resolve customer issues four out of the five times they come to us**.

AI-powered solution recommendations in self-service can eliminate the need for at least 10% of the cases being created. This is a big win for human-generated knowledge being delivered by AI-generated tools. **Every third case gets submitted with an AI-recommended product component for optimal routing and faster processing**.

In SAP's multi-location, multilingual, global setup, standardized communication is key. Around 10% of responses by support engineers take advantage of SAP's AI-assisted language optimization services.

There's more. We have **agentic case resolution, AI-assisted creation of SAP Knowledge Base Articles, and automatic error categorization**, covering use cases that help our engineers deliver their best work with greater accuracy and higher quality.

And, of course, SAP runs its own products and solutions, serving as a first reference for our customers. As Dr. Benjamin Blau, SAP's Chief Process and Information Officer, puts it: **"This is 'SAP runs SAP' in action. As customer zero, we validate every AI innovation in real-world complexity before it reaches you. We've architected this multi-agent AI on our own SAP Business Technology Platform, including the SAP AI Core foundation, and our service and support data lake. Agentic case resolution is a blueprint for enterprise-grade, responsible AI, proving the power and maturity of the SAP Business AI portfolio, empowering customers with faster resolutions for an elevated experience."**

## Will AI replace support teams?

Short answer: No.

To elaborate, let's take the example of an AI agent that automatically responds to customers. SAP's instant response and resolution are only activated when the system is very confident with its response. Our commitment to the relevant, reliable, and responsible use of AI helps ensure that there's no experimentation with customer cases that deserve hands-on attention from engineers and experts. The legacy of trust that SAP has earned over 50 years of industry leadership, which is also trusted by 90% of Fortune 500 companies, drives this rigor applied to AI.

What does this mean for our engineers? Any move to augment our work with AI is not about replacing people. It's about freeing time, energy, and creative space to focus on high-impact tasks that need critical thinking and human insight. **AI amplifies human expertise**. Customers benefit from this blend of machine intelligence and human insight, ensuring every solution is relevant and responsible.

It's also important to highlight that SAP is a growth company. The use of technology helps us deliver what customers expect from support teams and build ongoing knowledge that feeds AI systems for intelligent decision-making, also meeting the future demands of AI-augmented support.

Yes, the world is witnessing role reductions across the industry with the adoption of AI in business workflows, but we also see the emergence of critical new roles that help us navigate the current reality. How many of us had heard of AI trainers or carbon accountants 15 years ago?

These are exciting times for innovation. SAP's partnerships, such as our collaboration with Databricks and Snowflake, empower developers to turn business data and AI into real business outcomes.

We're truly at the crossroads of innovation and transformative tools that can turn imagination into impact. SAP's Chief Technology Officer, Philipp Herzig, summarizes it perfectly: **"AI is transforming business at every level, but it's people who turn transformation into progress. With SAP Business AI, we're combining the best of human ingenuity and machine intelligence to deliver impact that matters."**

---

Stefan Steinle is executive vice president and head of Customer Support & Cloud Lifecycle Management at SAP.

SAP’s customer support harnesses agentic AI to deliver smarter assistance, faster resolutions, and a stronger human–tech partnership.

How SAP's AI Revolution is Transforming Customer Support Forever

**How is Salesforce’s Agentforce transforming customer service, and why are 90% of deployments starting with Service Cloud?** David Brown, Chief Customer Officer for Service Cloud at Salesforce, shares insights on autonomous agents, multilingual support, and the future of service teams.

In this interview from the Agentforce World Tour in Amsterdam, David Brown discusses real-world implementations, from Heathrow Airport’s WhatsApp agent to Simply Health’s strategic email automation. Learn about the dramatic channel shift happening in customer service, how **AI-assisted agents can easily take the burden off your customer service team**, and how to implement Agentforce successfully in your organization.

![Why 90% of AI agent deployments start with customer service](https://www.youtube.com/embed/HrQeM_-8Q2k)

Discover the practical strategies companies are using to achieve measurable ROI, the critical importance of trust and accuracy in AI deployments, and why service teams are evolving from cost centers to revenue generators.

## Some insights on why AI dominates customer service in Agentforce deployments

**Question:** Why is service the area where we see most AI agent implementations, with almost all customer references being service-oriented?

**David Brown:** In Salesforce, if you look at our Agentforce deployments globally, depending on what month you look at, around **90% of the go-lives of Agentforce use cases start with Service Cloud**. It’s so tangible, isn’t it? People can tend to prove the cost savings quite quickly with service use cases, so we’ve tended to sign off on way more projects and get more go-lives in the area of service than anywhere else.

**Question:** Are there specific types of service use cases that stand out in terms of adoption?

**David Brown:** Most people try an agent in a chatbot to start with, within the chat channel. Most people try it as well, with fairly basic requirements, like an FAQ knowledge article being exposed to an agent. But there are many more advanced and exciting use cases now where agents are taking real actions, completing processes, and doing more complex work.

## Moving beyond traditional chatbots

**Question:** How do modern conversational AI agents differ from the earlier chatbots that people generally disliked?

**David Brown:** I think the only thing disliked more than a chatbot is IVR (Interactive Voice Response). If you were pushed into an IVR channel, it’s probably the worst of all. But these conversational experiences you can drive now through agents, it’s a dramatic difference to a static “if yes, no, go here.” Those kind of static chatbot experiences are becoming a thing of the past and very obsolete.

**Question:** Salesforce put help.salesforce.com entirely on Agentforce. What were the results?

**David Brown:** Help.salesforce.com. I think the stat now is that **86% of cases created are resolved with an autonomous agent**. Customer satisfaction has gone up as well because they’re getting answers faster. To resolve 86% without diverting to another channel or a human is pretty amazing, and it’s driven some incredible cost savings.

## Productivity gains from basic AI capabilities

**Question:** Beyond autonomous agents, what productivity improvements are companies seeing from basic AI features?

**David Brown:** Just the most basic of AI deployments in service, like a summarization of a call, can save companies minutes after a call finishes with note-taking. The Service Reply capability we’ve got, where you just take in your knowledge base and recommend things for the service agent to say, is **improving average handling time for some customers by 45%**. So, transcripts being summarized and recommendations being prompted during a call, which are just generative AI capabilities, are really supercharging service agents today.

## Multilingual support and translation

**Question:** What’s the status of language support for Agentforce across Europe’s diverse markets?

**David Brown:** Unfortunately, the Dutch language support comes a little bit after Spanish, French, and Portuguese support. English clearly is the first language we support; we GA’d (general available) that at Dreamforce. But French, Spanish, German, and Portuguese will come in the next round of languages, and Dutch will come very soon after.

**Question:** How will translation capabilities impact the service industry?

**David Brown:** We’re excited about the translation capability and the impact that’s going to have on the service industry. With outsourced contact centers, if I can now handle a customer conversation with an agent where you can speak in any language, then the days of me having to take up my contact centers to South Africa, Manila or India have changed. I think it’s going to have a bigger impact on interpersonal communication than we now think.

## Real-world example: Heathrow Airport’s Haley

**Question:** Can you share an example of an autonomous agent already live?

**David Brown:** I flew here through Heathrow Airport, and they have an autonomous agent called Haley. She can do wayfinding, find the route to your gate, the gate number, and how to get there. Obviously, the most important use case for me was finding out what’s on the British Airways menu. She handles menus, lounges, and routes. Haley sits within WhatsApp, which is an autonomous agent powered by Agentforce.

**Question:** What impact has Haley had on Heathrow’s operations?

**David Brown:** It’s only been live for a few months, but it’s taken the number of chats that a human now has to interact with to answer the same questions down by 40%. High-volume, low-value use cases. I think they’re trying to do two things, which are to improve the experience of me as a customer coming through Heathrow and also to take away high-volume, low-value work from service agents.

## The dramatic channel shift in customer service

**Question:** What changes are you seeing in how customers prefer to communicate with service teams?

**David Brown:** We did the seventh edition of the State of Service report, which interviewed 6,500 customers. Less than 10% of customers have a channel strategy. Most customers just think it’s great to provide support via all channels; they think the more channels the better. But what we found is that the customers who are delivering the highest customer satisfaction statistics tend to focus on the channel that’s right for the use case.

**Question:** Can you give an example of this strategic channel shift?

**David Brown:** Simply Health has this strategy to move from 70% of their inbound contact being on a phone to about 10% over the next three years, with WhatsApp going from 5% to maybe 50% of interactions. I think that’s having the biggest impact. **Channel shift is one of the biggest things that’s happening now because of the advent of AI**.

## From cost center to revenue generator

**Question:** Where do you think customers are moving next after implementing service agents?

**David Brown:** I met an electronics retailer in Germany who are using Marketing Cloud with Salesforce to send campaigns out. What they used to send at the beginning of every email is “do not reply.” But what they’ve done is they’ve put their service agent, a product expert, at the front of a marketing campaign. So if you wanted to talk about features and functions, and do some more due diligence on a product, use the product knowledge base and a support agent at the front of a marketing campaign.

**Question:** How does that change the role of service teams?

**David Brown:** That is turning your service team into a lead-nurturing role. And that’s the way service teams start to become revenue generators rather than just cost centers. So I think the promise of service centers not just being about cost, but being about profit.

Salesforce's David Brown reveals why 90% of Agentforce deployments start with service, how companies achieve 86% autonomous resolution.

AI Agents Are Revolutionizing Customer Service: Why 90% of Deployments Start Here

Amazon has announced a major expansion of its Nova AI portfolio, introducing four new models and innovative services designed to help companies implement AI at scale with greater control. This includes **Nova Forge**, a pioneering service for building custom AI models, and **Nova Act**, a service for creating highly reliable AI agents.

## Key Takeaways
- **Nova 2 models** deliver industry-leading price-performance across reasoning, multimodal processing, conversational AI, code generation, and agentic tasks.
- **Nova Forge** enables companies to build their own optimized variants of Nova by infusing proprietary data early in the training process through a unique "open training" approach.
- **Nova Act** achieves breakthrough 90% reliability for browser-based UI automation workflows built by early customers.

## Nova 2 Model Family
Tens of thousands of companies are using Nova for diverse applications, such as producing high-quality content, automating multi-step tasks, and accelerating development of AI agents. The new Nova 2 model family balances speed, cost, and intelligence across multiple AI domains.

### Nova 2 Lite
**Nova 2 Lite** is a fast, cost-effective reasoning model for everyday workloads that can process text, images, and videos to generate text. Customers can adjust how much step-by-step "thinking" the model performs before responding, balancing intelligence depth with speed and cost—ideal for **customer service chatbots**, document processing, and business automation. Nova 2 Lite delivers industry-leading price performance in its intelligence class.

![Benchmark comparison chart of AI models: Nova 2 Lite, Claude, GPT, Gemini](https://assets.aboutamazon.com/dims4/default/96af8d4/2147483647/strip/true/crop/1694x2990+0+0/resize/421x743!/quality/90/?url=https%3A%2F%2Famazon-blogs-brightspot.s3.amazonaws.com%2Fee%2F02%2F87e2494b433ca1a87bf33cbde2eb%2Fnova-2-lite-bolded-compressed-final-footnote.png)

### Nova 2 Pro
**Nova 2 Pro** is Amazon's most intelligent reasoning model that can process text, images, video, and speech to generate text. It’s ideal for highly complex tasks like agentic coding, long-range planning, and sophisticated problem-solving—where the highest accuracy is essential. The model can also serve as a "teacher" for knowledge distillation—transferring its capabilities into smaller, more efficient "student" models for specific domains and use cases.

![Benchmark comparison chart for AI models: Nova 2 Pro, Claude, GPT, and Gemini](https://assets.aboutamazon.com/dims4/default/d390210/2147483647/strip/true/crop/2062x3022+0+0/resize/507x743!/quality/90/?url=https%3A%2F%2Famazon-blogs-brightspot.s3.amazonaws.com%2F82%2Fad%2F1fa86b6d4633ad828df5e2719609%2Fnova-2-pro-bolded-compressed-final-footnote.png)

Both Nova 2 Lite and Nova 2 Pro have built-in web grounding and code execution capabilities, meaning they can search the web for current information and run code directly—ensuring responses stay anchored in up-to-date facts rather than relying solely on training data.

### Nova 2 Sonic
**Nova 2 Sonic** is Amazon's speech-to-speech model that unifies text and speech understanding and generation for real-time, human-like conversational AI. It features expanded multilingual support with expressive voices, higher accuracy, and a one-million token context window for sustained interactions, while enabling seamless switching between voice and text. The model handles tasks asynchronously, letting users continue natural conversations—even switching topics—while actions like booking flights complete in the background. It also seamlessly integrates with **Amazon Connect**, telephony providers (Vonage, Twilio, AudioCodes), and conversational AI frameworks (LiveKit, Pipecat), making it ideal for **customer service applications**, AI assistants, and interactive voice experiences.

### Nova 2 Omni
**Nova 2 Omni** is a unified multimodal reasoning and generation model that can process text, images, video, and speech inputs while generating both text and images—an industry first. It handles up to 750,000 words, hours of audio, long videos, and hundred-page documents, simultaneously analyzing entire product catalogs, testimonials, brand guidelines, and video libraries at once. This eliminates the cost and complexity of connecting multiple specialized models.

Organizations like Cisco, Siemens, Sumo Logic, and Trellix are using Nova 2 models for applications ranging from agentic threat detection to video understanding and voice AI assistants.

## Nova Forge: First-of-its-kind service for building your own frontier AI models
Organizations embedding proprietary knowledge into AI applications currently face three compromises: customizing proprietary models in ways that only scratch the surface for integrating an organization’s expertise, continuing to train open-weights models without access to the original training data, which risks the model regressing on foundational capabilities like instruction following, or building from scratch at enormous expense. What organizations need is access to both frontier model capabilities and the ability to deeply integrate their expertise.

**Nova Forge** empowers organizations to build their own optimized variants of Nova—we call them “Novellas”—by blending their proprietary data with Nova’s frontier capabilities. The service pioneers "open training"—giving exclusive access to pre-trained, mid-trained, and post-trained Nova model checkpoints so customers can mix their proprietary data with Amazon Nova-curated datasets at every stage of model training. The result is a customized model that combines Nova's full knowledge and reasoning power with deep understanding of each organization's specific business. Customers can start building their own Novellas with Nova 2 Lite today.

Beyond model checkpoints and data-mixing capabilities, Nova Forge offers three additional powerful capabilities: First, the ability to train AI using your own environments, which are referred to as reinforcement learning “gyms.” These gyms are synthetic environments where the models learn from simulated scenarios that reflect their real-world use cases. Second, option to create smaller, faster models that maintain their intelligence at a lower cost—trained on AI-generated examples from larger models through a process called synthetic data-based distillation. Third, access to a responsible AI toolkit that allows them to implement safety controls.

Organizations like Booking.com, Cosine AI, Nimbus Therapeutics, Nomura Research Institute, OpenBabylon, Reddit, and Sony are building their own models with Nova Forge to better serve their unique requirements.

Once customers create their own frontier model with Nova Forge, they can deploy it on Amazon Bedrock with the same enterprise-grade security, scalability, and data privacy as all other Bedrock models. This complete solution—from building their own frontier model to production deployment—ensures organizations achieve optimal AI performance tailored to their specific business needs, with exclusive use of their model securely hosted on AWS.

## Nova Act: New AWS service for building and managing reliable AI agents for UI-based workflows
**Nova Act** is now available as a service on AWS for building and deploying highly reliable AI agents that can take actions in web browsers. Powered by a custom Nova 2 Lite model, Nova Act provides the fastest and easiest path to build and manage fleets of agents that automate browser-based tasks. Nova Act delivers 90% reliability on early customer workflows and outperforms competing models on relevant benchmarks.

Nova Act achieves breakthrough reliability by training a custom Nova 2 Lite model through reinforcement learning, running thousands of tasks on hundreds of simulated web environments. This style of training allows Nova Act to excel on UI-based workflows like updating data in a customer relationship management (CRM) system, testing website functionality, or submitting health insurance claims.

![gif of Nova](https://assets.aboutamazon.com/dims4/default/e7b12e2/2147483647/strip/true/crop/1600x900+0+0/resize/1320x743!/quality/90/?url=https%3A%2F%2Famazon-blogs-brightspot.s3.amazonaws.com%2F53%2F02%2F0323944346649b5c1b0817fc7f35%2Fgif-rl-gyms.gif)

With Nova Act, developers can start prototyping an agent in minutes with a no-code playground using natural language prompts, refine that Nova Act agent in familiar IDEs like VS Code, and then deploy to AWS. What customers build and test locally scales in production, with comprehensive management tools and monitoring through the Nova Act AWS console.

Organizations across sectors are already seeing results with Nova Act:
- Startup Sola Systems integrated Nova Act to automate hundreds of thousands of workflows per month for their clients across business-critical tasks like reconciling payments, coordinating shipments, and updating medical records.
- 1Password used Nova Act to enable users to access their logins with fewer manual steps, and works automatically with a single simple prompt that works across hundreds of different websites.
- Hertz accelerated its software delivery by 5x and eliminated its Quality Assurance (QA) bottleneck by using Nova Act to automate end-to-end testing across its rental platform—which processes millions in daily bookings—transforming what used to take weeks into hours.
- Amazon Leo eliminated its QA constraint ahead of its satellite internet launch by using Nova Act to write test scenarios in natural language that automatically execute and adapt across thousands of web and mobile test cases, reducing what previously took weeks of engineering effort to minutes while running three times faster with zero AI costs after initial runs.

## Getting started with Amazon Nova
To learn more about Amazon Nova, visit [aws.amazon.com/bedrock/nova](https://aws.amazon.com/nova/). Start building for free at [nova.amazon.com/dev](https://nova.amazon.com/ap/signin?clientContext=132-7278988-8454135&openid.return_to=https%3A%2F%2Fnova.amazon.com%2Fdev&openid.identity=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0%2Fidentifier_select&openid.assoc_handle=amzn_agi_hyperion_us&openid.mode=checkid_setup&language=en_US&openid.claimed_id=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0%2Fidentifier_select&openid.ns=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0).

Amazon Nova introduces four new AI models, pioneering open training service, and AI agents enabling companies to implement AI at scale with control.

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