Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Systems integrators and services companies are launching AI agents, releasing frameworks and trying to help enterprises build multi-agent systems. The big question is whether AI agents turn out to be a boon or a bust for systems integrators in the long run.
In recent days, we've heard from multiple systems integrators with more on tap talk about agentic AI. The extension of integrators into agentic AI makes sense given that they have the expertise to work across systems and processes. Consider:
Kyndryl, a services provider focused more on infrastructure, released the Kyndryl Agentic AI Framework, which orchestrates and dispatches AI agents that respond to shifting conditions. The framework is a way for Kyndryl to move up the stack to higher level offerings because it moves the integrator beyond infrastructure to workflows and processes.
According to Kyndryl, its Agentic AI Framework leverages algorithms, self-learning, optimizations and AI agents to run applications and processes.
Wipro said on its first quarter earnings call that enterprises are shifting discretionary funds to data and AI modernization. "AI is no longer a niche. It's becoming essential to how businesses operate at scale," said Wipro CEO Srinivas Pallia.
He added:
"Our AI capabilities are integrated into both industry and cross-industry solutions. By combining domain expertise with AI, we are able to deliver value through solutions such as hyper-personalized wealth management and predictive industrial insights. We have deployed over 200 AI-powered agents using advanced technologies from leading hyperscalers. These agents enables smarter lending, intelligent claims processing and autonomous network management."
At AWS Summit New York, there were multiple partners talking about the foundation needed for AI and agentic AI adoption. Deloitte's Chris Jangareddy, Managing Director of the company's AI, GenAI and Data Engineering, said the company will have nearly 180 agents on AWS Agent Marketplace.
According to Jangareddy, these agents are aimed at business problems, processes and specific tasks. Deloitte's AI agents are designed to be reusable Lego locks that will ultimately make up multi-agent systems. One offering is AI Advantage for CFOs that serve as a digital twin for CFOs, he said. The agents are built on Deloitte's institutional knowledge base of queries that are now prompts.
"These are not licensed, but are for clients," said Jangareddy, who noted that Deloitte is looking to transform its model from traditional billing to an outcome-based approach.
In a demo, Deloitte outlined Zora AI, which is part of an effort to produce AI agents that are product offerings. Deloitte views AI agents as digital labor that focuses on executing on processes. Zora AI is also integrated with SAP Joule.
AWS’ Brian Bohan, Director, Global Lead, Consulting Partner Center of Excellence, said during a talk that companies automating multiple business processes with agentic AI are seeing 30% to 40% productivity gains. He expects more efficiency to be unlocked.
Why? The cost of models is falling as are training and inference expenses. However, many AI projects aren't scaling due to a lack of architecture, data infrastructure and expertise. "There's just the complexity of integration," said Bohan.
Bohan added that change management, workflow optimization and the pace of innovation are all challenges. Enterprises will get to multi-agent systems across functions like finance, procurement and supply chain.
It's clear that systems integrators see AI agents as a booming business as well as a way to transform their businesses. The flip side of this transformation is that AI agents may ultimately hamper the systems integrator model.
Constellation Research analyst Holger Mueller said:
"As with any new technology, enterprises are looking at system integrators for help adopting them and AI is no difference here. The question is whether AI is so strategic that enterprises need AI skills inhouse, or can they rely on the integrator model. The experience depth is low for anyone as no one has more than two years in genAI experiences. Or more than 10 projects. It is likely going to be strategic for enterprises to have their own AI capacity and competency, especially once we move to inter-enterprise agents and the uptime and capability of frontline and backend agents determine success."
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Delta Air Lines is pricing about 3% of its domestic fares with an artificial intelligence system and plans to get to 20% by the end of 2025.
Speaking on Delta second quarter earnings conference call, Delta President Glen Hauenstein gave an update on the company's plan to leverage AI-driven dynamic pricing.
"We're optimizing revenue through our partnership with Fetcherr, leveraging AI-enhanced pricing solutions. While we are still in the test phase, results are encouraging. You have to train these models and give them multiple opportunities to provide different results. We like what we see and we're continuing to roll it out, but we're going to take our time and make sure that the rollout is successful."
Hauenstein added that the more data and cases Delta feeds to Fetcherr, the more it learns and optimizes offers. If Delta gets to the 20% mark, it should be able to scale dynamic pricing at a faster clip.
On its conference call, Delta emphasized that it continues to roll out its technology including Delta Concierge, a virtual personal assistant built into the Fly Delta app launching later this year.
Delta is also using AI to optimize maintenance and resource availability.
But the biggest wins appear to be revenue optimization via partnerships with Fetcherr. In the second quarter, Delta operating revenue was up about 1% from a year ago to $15.5 billion. Hauenstein said demand stabilized late in the second quarter and business travel was solid. "During the quarter, demand trends stabilized at levels that are flat to last year. Our teams did a great job optimizing revenue performance in this environment by leveraging Delta's structural advantages and engaging customers beyond flight to generate a revenue premium to the rest of the industry," he said. "Diverse, high-margin revenue streams continue to show resilience, growing mid-single digits year-over-year and driving double-digit operating margins. Premium revenue grew 5% over the prior year, outpacing main cabin."
The plan for Delta is to expand profit margins on multiple fronts. The company restored its financial guidance that it cut in the first quarter with earnings of $5.25 a share to $6.25 a share and free cash flow of $3 billion to $4 billion.
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
CxOs are being barraged with constant change where AI time frames are compressed to days before there's a new development. The breakneck pace can freeze enterprise technology buyers since they can't spend on every new development, need to show returns and don’t want AI tech debt.
At AWS Summit New York, the focus was putting the fundamental approaches in place to give enterprises the structure to adopt AI agents.
Angie Ruan, CTO Capital Access Platforms division at the Nasdaq, summed up the current AI situation. "Technology used to operate over a decade. If you weren't upgrading something in five years you were behind. Later it became 18 months. Last year it was six months. Today my mindset is you have five days before you don't know what's going on and you're behind," said Ruan. "I've have never seen a pace as fast as AI."
Ruan added that there's a balancing act. "Stay calm, be strategic and be agile so you can be ready to pivot and take very practical delivery steps," she said.
Practical real-world returns for AI projects--generative, agentic and everything in between--was a recurring theme at AWS Summit New York. AWS rolled out a bevy of updates and features including Amazon Bedrock Agent Core, customizable Nova models and lots of talk about frameworks for reliability, security, observability and agility.
In the end, Swami Sivasubramanian, AWS VP of Agentic AI, used his keynote to return to enterprise fundamentals. Sivasubramanian's talk in New York had a lot to do with the balance of innovation and foundational approaches that can change models and underlying technologies. To AWS, a strong foundation and approach enable and accelerate innovation rather than constrain it.
For enterprises, a focus on fundamentals was long overdue. Do you really expect a business to swap out an LLM every time there’s a latest and greatest model that scores 0.06% better on math, coding or reasoning?
“AI agents are a tectonic change. They are a shift in how software is deployed and operated, and how software interacts with the world. Making that possible involves building on the foundations of today. In fact, in a world with agents, the foundation has become more important than ever. Things like security, right sizing, access, control and permissions and data foundations enable the right data to be used at the right time with the infrastructure that offers the right price performance,” said Sivasubramanian.
It's a message that enterprises were receptive to. “We're realistic about what AI can and cannot do. This isn't the silver bullet, but that's true of all AI systems. The value comes when you pair it with strong engineering practices,” said Matt Dimich, VP Platform Enablement at Thomson Reuters.
AWS is setting itself up for AI agent production systems where stability matters and models for most use cases are good enough to last a while. As agentic AI becomes more enterprise ready, basics such as identity, authentication and stability matter.
Constellation Research analyst Holger Mueller said:
“Amazon is making its step into the agent platform business with Agent Core. The good news for Amazon and its customers is that the traditional small, atomic services approach that comes from the AWS DNA, may be exactly the right thing for enterprises to build their first agents. AWS is enabling AI agents in a modular, individual and use case driven way - picking from Agent Core what they need. Adoption in the next few months will be interesting to watch. On the infrastructure side, the S3 vectors announcement is huge, as it makes digital assets stored in S3 available for AI.”
The AI ROI mismatch
Rohit Prasad, SVP and Head Scientist for AGI at Amazon, said enterprises have been struggling with an expectations gap between AI deployments and real returns.
"As exciting as AI is today, ultimately the real world is the real benchmark," said Prasad. "You hear about these models that come out every day. If you're an enterprise CIO you're thinking about the practical applications. How do I make real world applications happen at scale?"
Prasad said the focus on AGI, a topic that borders on obsession in the AI industry, is often a misdirected. "I want to level set on AGI. I think the whole conversation about who gets to AGI first or whether you can get to it is meaningless," said Prasad, noting that Amazon is chasing AGI and building out a full layer stack. "I don't think there will be a switch when we are AGI. Let's focus on whether we can make AI useful in real life. And can we make the complex simple?
AWS announced the ability to customize its Nova models for enterprise use cases. AWS will provide optimization recipes, model distillation and customization to balance cost and performance.
Prasad noted that every enterprise needs to think about AI returns in terms of workflows and processes. "It comes down to measurement. You can only improve on things you measure. Look at the success criteria for every workflow."
He added that metrics can't be stationary because your organization constantly changes. "Just go with very open eyes that in lot of applications at scale, what you measure, what you on a daily basis, also needs to evolve over certain time period," said Prasad.
In terms of AI agent value, measurement will be critical. Prasad said:
"The bar to evaluate the agent should be the same as the bar that is used to evaluate a human from a perspective of safety reliability. I think it's the same thing you want in a reliable human being. I want you to be reliable, which means it's a function of accuracy and consistency and robustness to the environment. If you want to be safe, you should have the values that you want your brand to be about, what your values to be humanity and the society is. So AI agents should be held to the same bar."
Measuring AI value
Erin Kraemer, Senior Principal Technical Product Manager at AWS Agentic AI, said that AI has the potential to fundamentally change how value is delivered.
The problem? Most companies don't properly measure AI impact. "One of the missteps that I'm seeing is how we're measuring AI impact right now and how we're talking about it," said Kraemer. "I'm not sure we're doing it the right way. Organizations that figure out how to thoughtfully apply AI to meaningful problems and measure success, are the ones that are going to adapt quickly and position themselves for the future."
Amazon's approach is to focus on controlled inputs and continual improvement to solve problems whether it's scaling infrastructure, managing product catalogs or admin tasks.
Key takeaways:
Focus on business outcome metrics over volume when it comes to AI, she said. Too much conversation about AI volume revolves around volume-based metrics, especially when it comes to code.
Kraemer said business outcomes trump volume. "I'm going to argue that, rather than volume, value should ultimately be our metric of success. So in my mind, volume, it's an output focus, and it's not even probably the right outcome."
Indeed, Kraemer said the stat that irks her is the commonplace 30% of code is written by AI. "The 30% number. I hate this number so very much. It's a fundamentally flawed number. It tells us very little about what's going on, our systems, our customers," said Kraemer.
Focus on the bottlenecks. She said enterprises need to see AI through business outcomes. Specifically, AWS looks to AI to address bottlenecks in processes. "If bottlenecks tend to be around human reasoning, there's a reasonably good chance that AI is a well-placed solution to that," said Kraemer.
Specifically, human bottlenecks have been an issue for Amazon throughout its history. She said:
"We love automation a lot. We like streamlined processes. We have some pretty massive, complex systems to handle those processes, but for a lot of our work, where we ultimately get stuck is in humans. Human reasoning capability has persistently been our bottleneck. It's not the worst bottleneck to have, but whether it's software upgrades, cleaning up catalog content defects in our shipping network, we either had to build very complicated and sometimes fragile systems, or we literally could not build systems that could scale through bottlenecks. What we're seeing with AI is a technology that's starting to blow by some of these bottlenecks."
Problem-specific metrics demonstrate real value. For code-related AI, Kraemer asked: "Are we fixing defects faster? Are we improving the security posture? Are we able to build things to delay our customers at a rate that we were never able to do before."
Amazon is looking at AI through a customer experience too. Here's a look at specific metrics AWS is using to gauge AI returns.
Software development:
Defect resolution speed.
Development velocity.
Infrastructure cost savings. AWS saved "roughly $260 million in AI-assisted Java upgrades," said Kraemer.
Developer time savings. AWS saved an estimated 4,500 developer years of effort on Java upgrades.
Customer experience:
Catalog quality improvements.
Contact per order decreases.
Customer satisfaction.
Knowledge work:
Time saved using AI to answer more than 1 million internal developer questions.
Research time and data to decision time.
Amazon's approach to AI internally
A panel representing technology leaders from various Amazon units--Amazon Ads, Alexa, technology infrastructure and other areas--talked about AI being integrated into their products and metrics for success.
Here are a few examples:
Amazon Connect uses genAI to enhance customer engagement and automation with data context as well as entity resolution.
AI is generating images and video for Amazon Ads and its AI services.
Amazon Business is using AI to automate business verification, improve accuracy and reduce manual review time. Search relevance and bulk buying reviews are also designed to improve procurement experience for Amazon Business customers.
AWS Marketplace is using AI for seller onboarding and funding approvals and offering a comparison engine for product insights.
Alexa is getting a rebuild for more natural interaction and agentic AI actions.
The metrics for these projects revolve around cost, friction elimination and customer experience. As you deploy these key performance indicators and metrics, keep an experiment-based mindset focusing on customer needs and iterate.
Lak Palani, Senior Manager, Product Management Tech at Amazon Business said:
"My recommendation is straightforward. Don't use AI just for the sake of using it. Find the right business cases where AI will really add value. Start small, measure results and remember it's an iterative process. Then you can scale success. Stay super focused on the business value and customer experience."
There’s a method to AWS' meat-and-potatoes focus on agentic AI and fundamentals: Enterprise adoption of AI agents will trail the technology advances and vendor marketing speak. AWS is meeting customers where they are right now.
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Zoho has launched its own large language model called Zia LLM, 40 pre-built Zia Agents, a no-code agent builder with Zia Agent Studio and a model context protocol (MCP) server that will connect its AI actions with third-party agents. The combination means Zoho is looking to democratize and differentiate with an AI strategy that revolves around developing its own right-sized models, optimizing and passing on the savings to customers.
For Zoho, the series of launches fleshes out its agentic AI strategy with the aim of democratizing various use cases, workflows and automation for enterprises of all sizes.
CEO Mani Vembu said Zoho's goal was to build foundational AI internally to better provide value and an integrated approach that "allows us to bring customers around the world cutting edge toolsets at a lower cost."
Zoho's AI strategy is to prioritize privacy and value. Its generic AI models across the Zoho platform aren't trained on consumer data and don't retain customer information. the goal is to use right-sized models that don't break the bank.
Zia LLM was trained and built entirely in India using Nvidia's platform. The foundational model was trained with Zoho product use cases in mind and can handle structured data extraction, summarization, RAG and code generation.
In addition, Zia LLM is family of three models with 1.3 billion, 2.6 billion and 7 billion parameters and competitive performance against comparable open source models. Zoho plans to mix and match models for the right context and power to performance balance.
Zoho also announced two Automatic Speech Recognition (ASR) models for both English and Hindi that's optimized for low compute resources. Zoho plans to support more languages in the future.
According to Zoho, it will still support multiple LLM integrations on its platform including OpenAI's ChatGPT, Llama and DeepSeek bur reckons Zia LLM will feature a better privacy profile since customer data will remain on Zoho servers. Part of the cost equation for Zoho customers will be leveraging Zia LLM and AI agents without sending data to cloud providers.
Raju Vegesna, Chief Evangelist at Zoho, said the company isn't initially charging for its LLM or agents until it has a better view of usage and operational costs. "If there a big operational resource needed for intensive tasks we may price it, but for now we don't know what it looks like so we're not charging for anything," he said.
So far, Zia LLM has been deployed in Zoho data centers in the US, India and Europe. The model is being tested for internal use cases across Zoho's app and service portfolio. Zoho said Zia LLM will be available in the months ahead and feature regular updates to increase parameter sizes by the end of 2025.
Zoho said it is also planning to launch a reasoning language model (RLM).
“It’s good to see Zoho charting it's unique course into the AI era and is now adding its in-house Zia models,” said Constellation Research analyst Holger Mueller. “With its focus on privacy and cost effectiveness in-house built LLMs are the right strategy for Zoho. Now Zoho has to show that it can keep up with the LLM competition.”
Why build your own LLM? B2B models are different
Zoho decided to build its own LLM from scratch for multiple reasons:
Investing into its own LLM would give Zoho downstream effects that would improve its platform and enable new features.
Zoho already was having success with dozens of AI models that weren't LLM-based.
The company wanted control of the LLM layer since it would be a core part of the platform and the company would need to continually tweak. "We don't like black boxes," said Vegesna.
Cost to performance is critical for enterprises and B2B software providers. By developing its own LLM, Zoho doesn't have to pass on additional costs to customers.
Although Zoho started LLM development within the last two years, two developments accelerated the pace. First, Zoho partnered with Nvidia. "B2B models are different than B2C and part of the technical partnership was about knowledge sharing," said Vegesna, who said Nvidia was more experienced with B2C. "With B2B, you don't worry about broader concepts as much. You narrow things down because you don't need the biggest model for every single tasks."
Vegesna said open source models such as Llama and DeepSeek, both supported by Zoho, also provided insights that improved development after development had started. Zia LLM was started before open source options appeared.
Zoho also had insights on how to develop Zia LLM from its own visibility into how APIs were used. Narrow models and non-LLMs were often used. Vegesna said Zoho is focused on using the right model for the right costs and optimizing for workflows.
"For the majority of use cases, narrow to smaller models will do the job," said Vegesna.
Having observability into its own platform enabled Zoho to optimize Zia LLM for the most common scenarios. That optimization should keep costs low. Vegesna said Zoho will continue to enable customers to use third party LLMs and the company hosts top open source models such as Llama, DeepSeek and Alibaba's Qwen.
"The customer will decide on the models used and Zoho LLMs will be an option," said Vegesna. "We have customers that don't want to rely on third party LLMs and we saw many of them taking open source models and optimizing them for their environments. Now we have that core technology, we can play the long game."
The agentic AI play
Zoho's strategy for AI agents is to offer dozens of prebuilt agents that can perform actions based on enterprise roles such as sales development, customer support and account management. The company's 40 prebuilt agents will be native in Zoho Marketplace and available for quick deployment in Zoho apps.
Zia Agents can be used within a Zoho app, across the company's stack of 55 applications or customized to specific use cases.
A few of the prebuilt Zia Agents include:
A new version of Ask Zia, which is a conversational assistant for data engineers, analysts and data scientists, but can democratize information for business users. Ask Zia is set up to address pain points faced by each persona.
Customer Service Agent, which processes incoming customer requests, understand context and answer directly or offload to a human. This agent will be integrated into Zoho Desk.
Deal Analyzer, which provides insights on win probability and next-best actions.
Revenue Growth Specialist, which looks for opportunities to upsell and cross-sell existing customers.
Candidate Screener, which identifies candidates for job openings based on role, skills, experience and other attributes.
Ask Zia agents for finance teams and customer support teams will be added.
Building and connecting agents
A big part of Zoho's AI agent plan is Zia Agent Studio, which was announced earlier this year, but has been revamped to be fully prompt-based with an option for low code.
Zia Agent Studio can build agents that can be deployed autonomously, triggered with rule-based automation or called into customer conversations.
Zoho is betting that its ecosystem of 130 million users, 55 apps and its own developers can fuel the Agent Marketplace to cover multiple use cases. Agent Marketplace now has a dedicated section for AI agents.
The company said its MCP server is designed to work across multiple applications and runs natively in Zia Agent Studio. Zoho has a library of actions from more than 15 Zoho applications exposed in early actions.
Zoho said Zia Agent will be assigned a unique ID and mapped as a digital employee so enterprises can analyze and audit performance, analysis and workflows with guardrails.
According to Zoho, Agent2Agent (A2A) protocol support will be added to enable collaboration with agents on other platforms.
General availability for Zia LLM will be at the end of 2025. Zia Agents, Zia Agent Studio, Agent Marketplace and Zoho MCP Server are being rolled out to early access customers with general availability at the end of the year.
Going forward, Zoho outlined the following roadmap:
Scale Zia LLM model sizes with parameter increases through 2025.
Expand available languages used by the speech-to-text models.
Introduce a reasoning language model (RLM).
Add skills to Ask Zia with a focus on finance teams and customer support teams.
Vice President & Principal Analyst
Constellation Research
About Liz Miller:
Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement, and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing experience, Miller offers strategic guidance on the leadership, business transformation, and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform everything from…...
Vice President and Principal Analyst
Constellation Research
Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.
Coverage Areas:
Future of Work
Tech Optimization & Innovation
Background:
Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...
New ConstellationTV drop! 👀 In episode 109, co-hosts Liz Miller and Holger Mueller unpack summer's tech news landscape, including HPE's evolution and the intersection of #AI, networking, and #cloud technologies...
Next, Holger explains the emerging AI protocol standards reshaping inter-agent communication. Learn how these frameworks prevent vendor lock-in and create more interoperable AI ecosystems. 🤔
Wrap it up with a CR #CX Convo with Adobe's Shelly Chiang about AI transforming Digital Asset Management (DAM) from a storage tool to an intelligent, strategic #content engine. Discover how modern DAM supports creativity, brand consistency, and global scalability. 🌎
Watch the full episode & subscribe to never miss a technology update!
00:00 - Meet the Hosts
01:17 - Enterprise Tech News
11:44 - AI Standards Discussion
16:55 - CR CX Convo with Shelly Chiang
30:23 - Bloopers!
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Intuit's Chief Data Officer Ashok Srivastava Ph. D said the company is now deploying AI agents across its platform, GenOS and products.
Srivastava walked through AI agents deployed on Intuit, which is built on the AWS stack. "Two weeks ago, we formally launched our agent experiences," said Srivastava, speaking during the AWS Summit New York keynote.
We've detailed Intuit's data and generative AI journey. The company has been able to ride inflection points by getting its data architecture right and then leveraging AI. Now Intuit is looking for AI agents.
Srivastava kept with the practical AI theme at AWS Summit New York and said that enterprises shouldn't "become enamored with technology" and focus on business goals and outcomes.
He said:
"Don't get it out into the technology. Use AI only where it's necessary and use rules. Measure your ROI return on investment. Make progress and empower small teams to invest."
A few takeaways from Srivastava:
Intuit is focused on providing human experts because it will complement what AI offerings it offers.
The GenOS runtime, Intuit's operating system, is designed to orchestrate agents and models.
The interface with software will be conversational.
Agents are already driving returns and cash flow improvements for small business customers on Intuit.
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Amazon Web Services launched Amazon Bedrock Agent Core, a set of tools designed to deploy and operate AI agents at scale. Agent Core includes a secure serverless runtime, access to tools and support for open-source frameworks.
In the big picture, AWS is aiming to be the best place to build and run AI agents that can carry out tasks with minimal human involvement. AWS is also looking to give enterprise customers tools that can give them stability in a rapidly changing AI environment.
During a keynote at AWS Summit New York, Swami Sivasubramanian, AWS VP of Agentic AI, laid out the cloud provider's approach to agentic AI. The four pillars to AWS' agentic AI strategy revolve around embracing agility, ensuring security and trust, reliability and scalability and observability.
Those pillars will be critical for enterprises given that AI agents are software systems that feature foundational models, complete tasks, take actions, plan, remember context and learn with minimal oversight. AWS' argument is that the fundamentals of building AI systems are as critical as the near weekly advances in model capabilities.
According to Sivasubramanian, the fundamental frameworks and approaches will matter even more as AI agents scale. There will be billions of AI agents working alongside humans in multiple settings and that scale will bring excitement, complexity and a bevy of concerns.
He said:
"We are focused on making our agentic AI data set accessible to every organization by combining rapidly innovation, with a strong foundation of security, reliability, and operational excellence. Our approach accelerates progress by building on proven principles."
In the end, Sivasubramanian's talk in New York had a lot to do with the balance of innovation and fundamentals as well as foundational approaches that can change models and underlying technologies. To AWS, a strong foundation and approach enable and accelerate innovation rather than constrain it.
There's also a reality check behind AWS' rather practical approach: Enterprise adoption of AI agents will trail the technology advances and vendor marketing speak.
AWS is setting itself up for AI agent production systems where stability matters and models for most use cases are good enough to last a while. As agentic AI becomes more enterprise ready, basics such as identity, authentication and stability matter.
The goal for AWS is to leverage Amazon Bedrock Agent Core and its partner ecosystem to enable enterprises to go from experiments to production with AI agents designed to run mission critical business processes. That progression is what has enterprises nervous, Sivasubramanian said.
Here's a look at Amazon Bedrock Agent Core:
Agent Core features a secure serverless runtime with session isolation. The agent runtime provides dedicated compute environments for AI agents with session, service and memory isolation leveraging AWS' Nitro abstraction layer.
Access to tools and capabilities so AI agents can execute workflows with the right permissions, context and controls.
The use of any model or open source framework.
Identity services to manage permissions of an AI agent and authenticate them.
Built-in checkpointing and recovery for interruptions.
Observability that's built in for internal and third party AI agents.
Agent Code Gateway for integration with other agents and various systems.
Early customers in private beta for Amazon Bedrock Agent Core include Autodesk, Cisco and Workday.
Other items from AWS Summit New York include:
Customization for Amazon's Nova models. AWS announced the ability to customize its Nova models for enterprise use cases in SageMaker AI. AWS will provide optimization recipes, model distillation and customization to balance cost and performance. Nova has launched eight models in 6 months.
"Over 10,000 customers are already using Amazon Nova. What really matters is that these models have real world impacts," said Rohit Prasad, SVP and Head Scientist for AGI at Amazon.
Nova will also get customization on-demand pricing for inference.
AI agent availability on AWS Marketplace. Customers will be able to buy AI agents and tools within AWS Marketplace. These agents can be acquired with standardized central billing and license management via AWS.
Sivasubramanian said the aim is to make it easy to deploy agents easily. “Now you can test and run AI agent solutions from a range of vendors, then quickly push the production and scale,” he said.
Updates to Amazon Connect and AWS Transform. Both will get specialized AI agents.
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Anthropic is best known for its Claude large language model (LLM), but its enterprise software ambitions are clear as the company builds out its go-to-market team.
The company launched Claude for Financial Services and hired Paul Smith, an alum of ServiceNow, Microsoft and Salesforce. Smith recently stepped down as president of global customer and field operations at ServiceNow. ServiceNow hired him from Salesforce in 2020. ServiceNow CEO Bill McDermott said on the company's first quarter conference all that Smith "scaled our global go-to market organization and together we built a world class team and methodically nurtured the right leaders to take us to 2030 and beyond."
Enterprise software companies typically go horizontal and then drill down into industries. Once you land a big customer in one vertical others often follow. The go-to-market playbook for enterprise computing has worked repeatedly for the likes of Salesforce, SAP, ServiceNow and Microsoft. Cloud providers are following the same path with targeted offerings for multiple industries.
Now Smith will be expected to do the same for Anthropic, which has emerged as the enterprise and B2B AI player relative to more consumer LLM players. OpenAI tries to straddle the line between business and consumer, but leans toward the latter.
Daniela Amodei, President of Anthropic, said the hiring of Smith will "strengthen our commercial organization and help more businesses worldwide become AI-native when he starts later this year.”
Smith will already have some of the enterprise parts at Anthropic, which has launched offerings designed for specific industries. At ServiceNow, Smith oversaw expansions into financial services, public sector and telecom to name a few.
Here's a look at the moves from Anthropic that are positioning the company as an enterprise software player.
Anthropic launched Claude for Financial Services that aims to give finance pros a tool to unify data and feeds with research into a single interface. The aim is to leverage Claude in trading systems, proprietary models, analysis and compliance. Model Context Protocol (MCP) connectors will tie financial data and market intelligence together.
The Department of Defense awarded Anthropic a contract to advance AI in defense operations.
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
The US Department of Defense has awarded contracts to Anthropic, Google, OpenAI and xAI with a ceiling of $200 million to each vendor to leverage AI models for national security.
According to CDAO, the awards to the AI companies are aimed at developing "agentic AI workflows across a variety of mission areas."
Chief Digital and AI Officer Dr. Doug Matty said in a release that the awards are part of a strategy to implement commercial AI tools first. “Leveraging commercially available solutions into an integrated capabilities approach will accelerate the use of advanced AI as part of our joint mission essential tasks in our warfighting domain as well as intelligence, business, and enterprise information systems," said Matty.
In a Google Cloud post, the company said the DoD can deploy using its Contiguous United States (CONUS) infrastructure for AI. This infrastructure leverages Google Cloud tools like TPUs, Agentspace and broader offerings in a separate Google Public Sector cloud.
Anthropic noted that it is building out its public sector efforts and gaining traction by leveraging Claude at the Lawrence Livermore National Laboratory and in US defense workflows with Palantir. Anthropic also has a government version of Claude called Claude Gov for national security customers built on top of AWS infrastructure.
For its part, OpenAI recently launched its government initiatives and has had a tailored version of ChatGPT for US government agencies since January. OpenAI for Government promises expertise and customer models for the Defense Department. xAI launched Grok For Government alongside the DoD contract.
Editor in Chief of Constellation Insights
Constellation Research
Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...
Amazon Web Services launched Kiro, an integrated development environment (IDE) that uses AI agents to move from prompt to prototype to production.
In a blog post launching Kiro, AWS executives Nikhil Swaminathan and Deepak Singh explained that last production step is where applications often fall over.
Kiro is in line with AWS' approach to creating tools to make deployment easier. Kiro is an IDE that allows you to go from concept to prototype quickly via conversations about specifications and designs.
"As a user, you interact with it, and it creates these specifications and designs which then make for very reliable, robust code over time," said Singh, who noted that Kiro rhymes with efforts like Amazon Connect, an AI-based customer service system and AWS Transform, which modernizes applications with AI agents.
Kiro was launched ahead of AWS Summit in New York, which is expected to feature a heavy dose of AI agent news. "Kiro is great at ‘vibe coding’ but goes way beyond that—Kiro’s strength is getting those prototypes into production systems with features such as specs and hooks," said Swaminathan and Singh.
Here's a look at Kiro components:
Kiro specs, which are artifacts that are useful for refactor work and upfront planning. Specs are designed to guide AI agents to better implementations.
Kiro hooks, which are automations that trigger an agent to execute a task in the background.
The AWS blog post walks through a few examples of Kiro specs and hooks and how they can move an application along to production.
In addition, Kiro includes code editor features as well as Model Context Protocol (MCP) support, agentic AI chat for coding, various plugins and steering rules and context generated from documentations.
According to AWS, Kiro is part of a broader vision to make building software easier, enterprise ready, eliminate technical debt and preserve institutional knowledge.
Constellation Research analyst Holger Mueller said:
"Software development and coding are not the same anymore in the era of genAI and it starts with AI agents plugging into the IDE, the 'couch in the developer living room'. The challenge is to find the right balance between in the background vs. in the face - to establish the coveted 'vibe' setup. We will see in a few weeks if Kiro got that right."
