Artificial Intelligence Archives - ITDigest

Intel and RadixArk Partner to Advance Open AI Inference

News Desk — Fri, 13 Nov 2026 10:13:53 +0000

Intel Corporation announced a collaboration with RadixArk to optimize SGLang, an open-source AI inference stack, for Intel platforms that aims to help developers and enterprises deploy leading AI models and build AI applications more efficiently and cost-effectively.

“Enterprise AI is moving beyond isolated performance benchmarks to solving full-system production deployments,” said Anil Nanduri, vice president, AI Products & GTM, Intel Data Center Group. “This collaboration ensures developers have Day-0 readiness and high-performance inference software directly optimized for Intel CPUs, GPUs and accelerators with an open approach and gives them the freedom to choose the tools, frameworks and technologies that best fit their needs.”

Why It Matters

Enterprises require heterogeneous, open inference infrastructure, as AI models grow larger and more complex. To deliver AI in production it requires that every prompt is routed to the right place, matching the different workloads with the right intelligence, right compute and right economics. Open inference runtimes provide the essential connective tissue that makes full-stack deployments practical, reliable, and economically sustainable across diverse enterprise environments.

Also Read: Plurai Raises $13 Millions to Build Trustworthy AI Agent Infrastructure

What It Delivers for Developers

This collaboration expands developer choice to deploy leading AI models on enterprise-proven Intel hardware. Model builders and software engineers can build and innovate on standard, open frameworks and interfaces while taking full advantage of Intel’s compute density and built-in hardware acceleration features, including Intel Advanced Matrix Extensions (Intel AMX). Developers gain faster time-to-production, reduced latency, and flexible deployment options across existing data center and cloud capacity.

“Our collaboration with Intel is a major milestone for open-source AI inference, enabling SGLang optimization directly to Intel’s platforms,” said Ying Sheng, CEO, RadixArk. “By upstreaming deep optimizations into the core framework, we are eliminating constraints and ensuring developers never have to compromise between open standards and high performance. Our mission to make frontier AI infrastructure open and accessible to every builder, and Intel’s enterprise-ready architecture gives development teams the flexibility and compute density they need to scale production AI workloads seamlessly.”

An Open Blueprint for Production AI

Bringing hardware and software optimization together reflects Intel’s approach of contributing improvements directly to community software, rather than treating them as separate deployment decisions. These upstream contributions to popular engines and frameworks aim to provide high-performance paths for turning breakthrough models into scalable, production-grade applications while preserving flexibility, choice and broad architectural freedom.

Source: Businesswire

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Plurai Raises $13 Millions to Build Trustworthy AI Agent Infrastructure

News Desk — Thu, 12 Nov 2026 12:35:56 +0000

Platform now available to all developers, after it cut design partners’ development cycles from months to hours, reduced costs by 97% and failures by 82%

Plurai, the simulation infrastructure for trustworthy, self-improving AI agents, announced $13M in seed funding and the general availability of its platform. The platform helps enterprises find and fix failures before their agents reach customers, then keeps them reliable, accurate and cost-efficient in production with small language models (SLMs) tailored to each agent’s tasks. Plurai has cut design partners’ agent development cycles from months to hours, reduced failures reaching real users by 82% and lowered the cost of running reliable agents by 97%. In joint work with NVIDIA and Nebius, models Plurai post-trained for a global enterprise customer outperformed OpenAI and Anthropic frontier models on accuracy at 40x and 44x lower cost, respectively.

The round was led by Team8, with participation from U&I, Velocity and Mercer, and backing from more than 20 angel investors and industry leaders, including CyberArk founder Udi Mokady. Microsoft 365 Copilot CTO Dr. Eyal Carmi has joined Plurai’s advisory board.

While AI agents are easier to create than ever, making them reliable and cost-effective with real customers is far harder. VentureBeat Pulse Research recently found that half of enterprises have deployed an AI agent or LLM feature that passed internal evaluations but still caused a customer-facing failure in production. Teams spend months manually checking their agents, but they can never anticipate every scenario agents will meet in production, and once agents are live, using frontier LLMs to run and to evaluate every interaction is too slow and too expensive to scale.

From simulation to production

Plurai builds a digital twin of an agent and its world based on the customer’s own knowledge base, tools and policies. It then runs thousands of realistic and adversarial users against the agent in a sandbox, covering the long tail of edge cases and exposing policy violations, hallucinations, incorrect actions, broken tool calls, failed tasks and sensitive data leaks.

The same simulation is then used to optimize the agent. Plurai generates fixes for the failures it finds and uses the simulation data to create small language models (SLMs) tailored to each agent. In production, these SLMs play two roles: they can replace frontier LLMs to run the agent, improving accuracy at a fraction of the cost, and they act as always-on evaluators and guardrails, checking every action and enforcing company policy. New failures they catch feed back into the simulation, so the agent keeps improving over time.

Previously limited to select partners and customers, Plurai is now open to every agent builder, with a free tier. Developers can start directly from the tools they already use, including Claude Code, Codex and GitHub Copilot, and teams can simulate and improve agents built on enterprise platforms such as Salesforce Agentforce.

Also Read: Bridging the Production Gap: How EXL’s Acquisition of Aimpoint Digital Accelerates Enterprise AI and IT Services

Founders shaped by autonomous driving

Co-founders Dr. Ilan Kadar (CEO, PhD in computer science and machine learning) and Dr. Elad Levi (CTO, PhD in mathematics) have spent the past decade bringing reliable AI systems into production in mission-critical environments. At Nexar, where Kadar was VP of AI and Levi was an AI architect, they led efforts to build real-time, scalable and reliable systems for autonomous driving companies like Waymo, which rely on simulation to validate self-driving systems before they reach the road.

Their research and open-source projects are used by hundreds of thousands of developers worldwide and Plurai’s technology is grounded in the company’s own original research. This includes BARRED, a new method for generating synthetic training data for custom SLMs, which was accepted at ICML 2026, one of the world’s leading machine learning conferences. Other research has shown how to serve hundreds of tailored SLMs in real time on a single GPU, making them economically viable at scale.

“AI systems fail when they hit challenges down the road which no one predicted,” said Dr. Kadar. “Waymo built trust by testing and training across more than 20 billion simulated miles, roughly 100 times its real-world driving. Plurai brings that same approach to AI agents: continuously challenging them with the hardest situations and rarest events before they reach users, learning from failures, and improving before those failures happen in the real world.”

Validation and traction: NVIDIA, Nebius and Gartner

Plurai is already working with enterprises across AI-native and highly regulated industries, including Fortune 500 and major financial companies. In addition to the work with NVIDIA and Nebius, Plurai was named a Representative Vendor in Gartner’s 2026 Market Guide for AI Evaluation and Observability Platforms, a category Gartner projects 60% of software engineering teams will adopt by 2028, up from 18% in 2025. It was also featured in Gartner’s “8 Emerging Practices for Testing AI Agents.”

“As agents move into core enterprise workflows, the missing layer is infrastructure that builds trust in them before they reach customers and keeps them improving in production,” said Aviad Harell, Managing Partner at Team8. “Simulation will become as fundamental to the agent stack as CI/CD is to software. Ilan and Elad bring deep AI expertise and over a decade of bringing AI into high-stakes production systems, and they’re the team to build that layer.”

Additional Quotes

“AI is probabilistic. Enterprises are not,” said Udi Mokady, founder of CyberArk, chairman of CHEQ and an angel investor in Plurai. “The founders have spent more than a decade building AI for mission-critical environments like autonomous driving, where systems don’t get second chances. That experience shows in how they approach agents, and it’s what separates AI that can be deployed from AI that can be trusted.”

“For enterprises, evaluating AI agents on real customer data is often blocked by privacy, security and compliance constraints, which makes simulation mission-critical,” said Dr. Eyal Carmi, CTO of Microsoft 365 Copilot and a member of Plurai’s advisory board. “What sets Plurai apart is how faithfully it recreates each company’s world, from its tools and policies to the users its agents actually face. And it doesn’t stop at finding failures. It turns what it learns into fixes and production safeguards, so teams can deploy agents with confidence.”

“Frontier models are incredibly capable, but using them to continuously evaluate agent behavior in production can quickly become expensive and impractical,” said Reah Miyara, Senior Director of Google Models at Google Cloud, former model evaluation lead at OpenAI and an angel investor in Plurai. “What I find compelling about Plurai is the idea of using smaller, tailored models to make evaluation continuous, not just something you do on a small sample of traffic.”

Source: PRNewswire

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Earning enterprise trust in agentic AI: a conversation with Uma Sandilya, Chief Commercial Officer, Kore.ai

Peter — Wed, 11 Nov 2026 12:07:40 +0000

Governance can’t be a checkpoint that agents wait in line for. It has to travel with the data.

Uma, your career has taken you from electricity transmission operations at Entergy to enterprise AI at C3 AI, strategy at McKinsey, and now global go-to-market leadership at Kore.ai. What has that journey taught you about earning enterprise trust in new technology?

I’ve seen enterprise technology from three seats: as the customer, the vendor, and the advisor. At Entergy, I was responsible for critical transmission operations, and when someone walked in with a new technology, my first question wasn’t what it could do. It was what would happen to the grid, and to the business, if it failed. During my early days at C3 AI, I was the one fielding those same questions, with the responsibility of developing the right products for customers across different sectors. At McKinsey, I was counseling executives on how to plan and execute large scale enterprise transformation projects.

The lesson from all three seats is simple: vendors sell capability, but customers really buy accountability. The excitement about what a technology can do is usually shared. The difference is that the customer is the one who lives with the consequences when it goes wrong. That gap is wider with agentic AI, because agents no longer just inform decisions. They make them and act on them inside core operations. More than 70% of Kore.ai’s customers are in regulated industries, so for us, earning trust starts with seeing the risk the way the customer sees it, and standing behind what we deliver by guaranteeing outcomes.

At Entergy, you worked in an environment where reliability and continuity were critical. What lessons from managing essential infrastructure still shape how you think about deploying AI in business-critical operations?

I co-led restoration work at Entergy after Hurricanes Katrina and Rita, and that’s where I learned what reliability actually means. When the power is out, hospitals, businesses and families are waiting on you. What got us through wasn’t heroics during the storm. It was the planning we’d done long before it. We knew which parts of the network had to come back first, who had the authority to make the calls, and how crews would keep working when the usual systems were down. Our process included a lot of detailed scenario analyses that informed where we would pre-position crews before the storm hit, so they were prepared and ready to begin restoration immediately.

Business-critical AI deserves the same discipline. Before an agent goes live, you should be able to answer three questions: How will it behave when something goes wrong? Who can step in? And how quickly can the work get back on track?

As AI agents move from answering questions to taking action across enterprise systems, the stakes around data access and privacy are changing. What should organizations rethink before allowing agents to work with sensitive information?

The first thing to rethink is identity: whose authority is the agent using? Today, many agents simply inherit the permissions of whoever built or deployed them. That was tolerable when an agent only answered questions. It isn’t when the agent can update a customer’s account, approve a refund or change a record. Now it’s acting on someone else’s authority, and the business needs to be able to say exactly what the agent was allowed to do, who authorized it, and what it actually did.

That means giving each agent its own identity, scoped to its role, with a named owner who is accountable for its behavior and an audit trail that ties every action back to a policy. None of this is new thinking. It’s how we already govern people who handle sensitive data. The mistake is assuming agents can skip it. If an agent is going to touch sensitive customer information, those controls have to be in place before it gets that responsibility, not after the first incident.

Also Read: ITDigest’s Interview With Borno Akhter, Head of Development at Epic Hello World

Gartner’s 2026 research found that only 13% of organizations believed they had appropriate governance in place for AI agents. What separates governance policies that look good on paper from controls that work reliably in live enterprise environments?

Most enterprises already have AI governance policies, and on paper they say the right things. The problem isn’t the policy. It’s enforcement at scale, when you have hundreds of agents, built by different teams, changing every week. Our own research this year found that 79% of enterprises have had to reverse an action an agent took, and 70% have dealt with a failure their teams couldn’t trace. Those organizations had policies. What they lacked was enforcement and visibility.

Two design choices make the difference. First, the rules that can’t be broken shouldn’t be left to the model. Let the model do the reasoning, but enforce critical business rules, eligibility checks and guardrails deterministically, outside the model, so they hold every single time. Second, governance can’t stop at go-live. You need to see what agents are actually doing in production, measure that against your rules and your business goals, and feed what you learn back into the agent. That’s the thinking behind Autoloop, the optimization engine we launched in October. It evaluates agents against real production traces, pinpoints what caused a miss, and scores every fix against all of the agent’s goals, including safety and compliance, so improving one thing never quietly breaks another.

Enterprise agents increasingly need to work across applications, data sources and workflows. How can organizations give them enough access to get useful work done without exposing information or permissions they should never have?

It’s tempting to give agents broad access, because broad access is what makes them useful across applications. But usefulness comes from the right access, not the most access. Even when an agent works across a CRM, a billing system and a knowledge base, access should be managed centrally and scoped to the task in front of it, so sensitive information stays where it belongs.

The bigger distinction is between access and authority. Being able to retrieve information doesn’t mean an agent should be allowed to share it, change it or act on it. In regulated industries especially, leaders should decide, deliberately and in advance, what an agent can do on its own, what needs human approval, and what it should never do at all. Then expand that autonomy as the agent earns it, with evidence, the same way you’d give a new team member more latitude once they’ve proven themselves. The boundaries can move with demonstrated performance, but they should never disappear.

As AI becomes part of everyday business operations, resilience means preparing for more than system outages. Drawing on your experience in critical infrastructure, how should organizations plan for agent errors, disrupted workflows and recovery when something goes wrong?

Critical infrastructure teaches you to plan for the worst case. On the grid, you don’t wait for a failure to figure out your response. You watch for early warning signs, and you have a recovery plan ready. Agents add a harder twist: an agent can complete a task that looks successful while the business outcome is wrong.

So, recovery has to be designed in from the start. Three things matter most. 1) Continuously evaluate real production interactions, not just test cases, so you catch the silent failures. 2) When something does go wrong, contain the impact first and keep the work moving, even if that means handing it to a person. 3) Treat every failure as input: trace it to its root cause, fix precisely that, and verify the fix against your business goals and existing controls before it goes back into production. In our experience, the organizations that recover fastest are the ones that learn fastest.

You led C3 AI’s hyperscaler partnerships and worked across strategic partnerships. As enterprises build AI systems across different models, clouds and business applications, where can partnerships make the biggest difference to security, governance and reliable deployment?

No single vendor can deliver enterprise AI on its own. You have cloud infrastructure, models, agent platforms and business applications, and they all have to work together. But the harder challenge is making them work with what the customer already has. Enterprises have spent years and significant capital on their technology estate. You can’t ask them to rip it out to adopt AI.

Running hyperscale partnerships at C3 AI taught me that “our products integrate” is the easiest sentence in any partnership, and the least meaningful. What matters is whether the partners have worked out, together, how the solution gets deployed, how the customer’s security and governance requirements are met, and who owns what when something breaks. That’s how we approach partnerships at Kore.ai. Our latest platform launched natively on Microsoft Azure, integrated with the identity, security and agent management tools Microsoft customers already rely on. With Atos in the UK, we’re delivering sovereign agentic AI for organizations that need to keep data and control in-country. And because we’re model- and cloud-agnostic, customers keep the freedom to choose, while governance and traceability stay consistent across every agent, whatever it runs on.

You have worked on enterprise AI strategy, pricing and value propositions. When governance, security and resilience require additional investment, how should leaders evaluate their value alongside more visible gains such as productivity and cost savings?

Enterprises need to change how they calculate AI ROI. If we measure agents the way we’ve always measured automation, by tasks completed, hours saved and costs cut, we miss half the ledger. An agent can complete thousands of tasks, but if a handful of them trigger rollbacks, customer remediation or a compliance finding, those gains can disappear quickly.

I’d evaluate governance, security and resilience on three lines. First, risk avoided: the cost of the errors, reversals and incidents that stronger controls prevent. Second, value unlocked: the higher-stakes work agents can take on only because the controls exist, work that would otherwise stay manual. Third, compounding returns: when governance is built into the platform, it becomes shared infrastructure, so the controls you build for your first agent carry over to your hundredth, and each new agent costs less to deploy safely than the last. Put those three lines on the same page as productivity and cost savings, and governance stops looking like a tax. It starts looking like the thing that makes the rest of the business case real.

As Chief Commercial Officer, you bring sales, partnerships, marketing and strategy together at Kore.ai. How does that broader commercial perspective shape the way you help customers balance the promise of agentic AI with concerns about data exposure and operational risk?

Bringing sales, marketing, partnerships and strategy together gives me a clear view of what customers want from AI and what’s holding them back. One shift stands out: AI buying decisions that used to sit within a single department now put the CIO, the CISO and the CFO at the same table. Each is asking a different question: Will it work? Is it safe? Will it pay off? A credible answer has to satisfy all three.

My starting point is always the business outcome, not the technology. Many companies are agentifying workflows without asking whether those processes should exist in their current form. If a process is poorly designed, an agent just helps it fail faster. And once agents are accessing data and taking actions across systems, those flaws become new risks. So, we work with customers to rethink the process first, then decide what data each agent needs, how much autonomy it should have, and where the business must retain control. That’s how you capture the upside of agentic AI without taking on risk you never needed to take.

As AI agents become more deeply embedded in enterprise operations, what will distinguish organizations that scale with confidence from those that struggle to move beyond pilots? And what role do you see Kore.ai playing in that transition?

Too many companies adopt AI before they’ve decided which business problem it should solve. The biggest difference I see is whether an organization makes AI part of its business strategy or treats it as one more automation tool. The organizations that scale start with business goals and rethink how the company operates: redesigning workflows, dividing responsibilities between people and agents, prioritizing investments, managing risk and measuring returns. Those decisions need business leaders to own them. Without that ownership, you can end up with a hundred successful pilots and very little business value.

At Kore.ai, our role is to help customers turn that strategy into production. We start with one high-value use case, define measurable outcomes and bring the right teams together around it. Our platform then builds the agent and its tests from the customer’s own operating procedures, enforces governance before anything goes live, and keeps optimizing against those same goals once it’s in production. The aim is to get that first use case live within weeks, prove the value, and then expand into more complex workflows with the same governance underneath. Scaling with confidence isn’t about deploying more agents. It’s about deploying agents you can trust, measure and improve.

Thanks Uma!

About Uma
About Kore.ai

Uma Sandilya is the Chief Commercial Officer at Kore.ai, where he leads the company’s global sales, go-to-market strategy, marketing, business development, and corporate strategy. With more than two decades of experience in enterprise AI and business transformation, he has held senior leadership positions at C3 AI and McKinsey & Company. During his tenure at C3 AI, he played a key role in developing enterprise AI solutions and building strategic partnerships. His expertise spans enterprise AI adoption, revenue growth, strategic alliances, and scaling technology businesses.

Kore.ai is a global enterprise AI company that helps organizations build, deploy, manage, and optimize AI-powered experiences and applications. Its platform enables businesses to leverage conversational AI, generative AI, and agentic AI to automate workflows, improve customer and employee experiences, and accelerate enterprise operations. With capabilities spanning AI agents, intelligent automation, orchestration, and enterprise-grade governance, Kore.ai helps organizations scale AI adoption while maintaining security, reliability, and control.

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DataBahn Appoints Vishesh Choudhary as SVP of Partnerships to Expand Channel Growth

ITDigest Bureau — Tue, 10 Nov 2026 13:06:04 +0000

DataBahn has appointed Vishesh Choudhary as Senior Vice President of Partnerships, strengthening its leadership team as the company accelerates its partner-led growth strategy across security, observability, and artificial intelligence (AI). The appointment comes alongside the company’s inclusion in CRN’s 2026 Stellar Startups list in the Data Platforms and Analytics category.

In his new role, Choudhary will lead DataBahn’s global partner program and expand its relationships with value-added resellers (VARs), global systems integrators (GSIs), managed security service providers (MSSPs), and technology partners.

Choudhary brings more than 22 years of experience helping enterprises and partners modernize their technology infrastructure using AI, cloud, and emerging technologies. His professional experience includes leadership and industry exposure at Amazon Web Services (AWS) and Deloitte.

The appointment comes after a strong year for DataBahn, during which the company reported more than 400% year-over-year revenue growth, over 180% net revenue retention, and a 97% proof-of-concept win rate. Its business growth has been driven primarily through its partner ecosystem.

Also Read: J.P. Morgan Private Bank Appoints Claire Moore as VP, Senior Events Manager for APAC

Commenting on the appointment, DataBahn co-founder and CEO Nanda Santhana said, “Vishesh brings the experience, relationships and operational discipline to turn our partner ecosystem into a global growth engine.”

Choudhary has already introduced changes to the company’s partner incentives, including a new SPIFF program, with additional details about its partner program expected in the first quarter of 2027.

DataBahn’s recognition by CRN further highlights its growing presence in the data platforms and analytics market. The company provides an agentic data control plane designed to help enterprises manage and act on data across security, observability, and AI environments.

As enterprises modernize security operations and develop next-generation AI platforms, DataBahn aims to strengthen its channel ecosystem and make its technology more accessible through strategic partnerships.

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Securing the Agentic Enterprise: Why Cisco’s Move Redefines Cybersecurity and Enterprise AI

ITDigest Bureau — Tue, 10 Nov 2026 12:56:55 +0000

As artificial intelligence evolves from passive text generators to autonomous agents capable of querying databases, triggering code, and orchestrating workflows, the enterprise threat model has fundamentally shifted. Addressing this transition, Cisco announced major enhancements to its AI Defense and observability suite. The networking and security giant unveiled frontier-grade automated red teaming powered by Anthropic’s Claude Mythos alongside new observability capabilities in Splunk designed specifically to govern autonomous agents.

The announcement marks a critical juncture for both the Cybersecurity and Enterprise AI / IT Infrastructure sectors, highlighting the shift from static perimeter defense toward continuous, real-time autonomous governance.

The Announcement: Preemptive Testing and Continuous Observability

Cisco’s release tackles the core challenge of the “agentic enterprise”: software agents possess tool-calling permissions and decision-making autonomy, making them attractive targets for novel attack vectors like indirect prompt injection, data poisoning, and tool-hijacking.

The update introduces several strategic pillars:

• Frontier Red Teaming via Claude Mythos: Integrating Anthropic’s advanced reasoning model into Cisco AI Defense allows organizations to automate multi-agent attack simulations. Rather than relying on static security checklists, autonomous “red teams” probe production systems across more than 200 risk categories, mirroring how malicious actors target AI logic.

• Tokenomics and Financial Observability: Through Splunk Agent Observability, Cisco added “Tokenomics” to tie agent token consumption directly to business output quality and ROI using telemetry from Amazon Bedrock and AWS CloudWatch.

• Low-Cost Evaluation Models: To eliminate the steep financial overhead of continuous monitoring, Cisco launched its Eval-G1 evaluation models, designed to run automated safety checks at up to 98% lower cost and 2.5 times the speed of traditional commercial LLMs.

• Closed-Loop Remediation with Cisco IQ: Connecting vulnerability telemetry with the Splunk Platform and ServiceNow connectors allows organizations to detect exposed infrastructure and trigger automated mitigations at machine speed.

Also Read: Bridging the Production Gap: How EXL’s Acquisition of Aimpoint Digital Accelerates Enterprise AI and IT Services

Effect on Cyber Security Sector

Cisco’s adoption innovates the market and compels security vendors and teams to move away from classic App Sec and closer to agent-based security:

1. Move Towards “Agent-vs-Agent” Security Architectures: Conventional WAFs and rule-based intrusion solutions lack the semantic awareness needed to safeguard against contextual attacks. Defence suppliers now need to deploy multi-agent solutions where security-related AI models will engage in attack and defence by arguing in counter to the attacker.

2. The buyout and dense integration of Splunk into the Cisco core stack proves that telemetry and threat prevention aren’t bifurcated spaces anymore. To prosper in the agent age, security tools providers will need to unify logs, API traces, tool executions, and security events into one unified operations fabric.

3. Red teaming activities tied to NIST and MITRE AI standards will create uniform expectations for auditors and are expected to inform cyber-insurance, oversight, and enterprise buying.

Broader Impact on Business Using AI

For companies willing to put so much into AI automation, Cisco’s reveal offers a bit of a guidebook – and a warning:

– Manage Hidden Runaway Costs Agents can often enter into wrapper loops or wandering scope paths that aren’t closely observed. New features such as Tokenomics allow the CFO and the engineering lead to take a look at the cost to the agent vs. the performance for the agent so you may avoid the “agent finances surprise” that has emerged in earlier releases.

– Agents consume cluttered third-party data, including supplier emails, customer documentation, and even data scoured from the web. In the absence of detailed harness testing and rapid injection isolation, your enterprise data can be exported from your enterprise via untrusted input.

– Safety ambiguity was the last remaining barrier to deploying AI proofs-of-concept to production. Enterprise-quality verification platforms give risk & compliance teams the tools to certify autonomous systems with quantifiable safety profiles.

The Way Ahead

There’s no returning from work happening within exploratory chatbots. But without deep observability, autonomy becomes enterprise-scale attack surface. The combination of frontier red teaming and granular telemetry from Cisco shows that the future of enterprise infrastructure isn’t just about deploying more capable models; it’s about being able to detect, defend and govern those models at scale, and in real time.

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Bridging the Production Gap: How EXL’s Acquisition of Aimpoint Digital Accelerates Enterprise AI and IT Services

ITDigest Bureau — Fri, 06 Nov 2026 11:14:22 +0000

World leading data and digital operations organization EXL has announced the acquisition of Aimpoint Digital, a rapidly scaling data and AI consultancy firm. This strategic move brings into EXL’s worldwide business roughly 250 world-class AI engineers, platform architects, decision scientists, analytics consultants and AI strategists, focusing on resolving the single greatest challenge to business transformation in the modern era: the gap that exists between AI proofs-of-concept and production-ready enterprise processes.

Following on the heels of the recent acquisition of model training and evaluation specialist iMerit by EXL, the consolidation of Aimpoint Digital will enable a comprehensive end-to-end delivery engine. Together, this new company connects strategic advisory and design with the building of models, platform engineering, data governance and managed digital operations. In addition, this acquisition will bring about a marked increase in technical muscle and certification capability for premium modern data and AI platforms, including Databricks, Snowflake, and Anthropic.

“Clients require a partner who can take them from AI ambition to impact at enterprise scale,” said Rohit Kapoor, chairman and chief executive officer, EXL. “Aimpoint Digital’s forward-deployed strategists and engineers bring the hands-on expertise to turn AI strategy into production reality on the platforms our clients trust most. By bringing their advisory and engineering talent together with EXL’s data foundations, industry expertise, agentic AI solutions, and operational scale, we can reimagine workflows and give clients a single, accountable partner for the entire journey from strategy and architecture through deployment and managed AI operations.”

Also Read: Securing the Citizen-Developer Boom: How CrowdStrike and Lovable’s Alliance Redefines Cybersecurity and IT Governance

Emergence of Forward-Deployed Platform Engineering

Conventionally, enterprise companies undergoing AI modernization had issues due to vendor fragmentation. Leadership used to employ expensive strategic management consulting firms to provide a conceptual roadmap for them. They used boutique software firms to develop demonstrations and offshore IT providers to maintain legacy data stores.

This multi-vendor model created critical handoff friction. Strategy consultants rarely possessed the platform-engineering depth required to build production data pipelines, while offshore maintenance vendors struggled to translate complex C-suite objectives into functional intelligence architectures. As a consequence, countless enterprise initiatives languished in “pilot purgatory.”

The integration of Aimpoint Digital addresses these operational bottlenecks through a forward-deployed delivery model:

Embedded Systems Architecture: Senior platform architects and data engineers embed directly within enterprise teams, translating strategic business objectives into robust software architectures and data models on Day One.

Modern Cloud Ecosystem Acceleration: Deploys pre-engineered data and AI accelerators across Databricks, Snowflake, and Anthropic, automating unstructured telemetry ingestion and accelerating enterprise Retrieval-Augmented Generation (RAG) deployments.

Closed-Loop Lifecycle Continuity: Eliminates vendor handoffs by integrating Aimpoint Digital’s architectural advisory with iMerit’s data evaluation protocols and EXL’s 74,000-strong global managed services engine.

Strategic Impact on the AI and IT Services Industry

The convergence of agile platform engineering and large-scale managed operations introduces structural shifts across the AI and IT Services landscape:

1. Transitioning IT Services from Billable Hours to Velocity-to-Value

Traditional systems integrators and IT outsourcing providers built business models around headcount expansion and multi-year time-and-materials contracts. As automated code generation and modern data fabrics compress routine software development timelines, enterprise buyers increasingly reject bloated staffing arrangements. EXL’s acquisition reflects an industry-wide pivot toward outcome-oriented service delivery, where IT firms must compete on their ability to deliver functional, production-ready AI applications rapidly.

2. Elevating the Modern Data Stack as the Critical AI Prerequisite

Enterprises cannot deploy reliable autonomous agents, fine-tuned models, or RAG frameworks on top of fragmented, undocumented legacy databases. By anchoring consultative engineering around modern platforms like Snowflake and Databricks, system integrators emphasize that AI success is fundamentally a data engineering challenge. This shifts data consulting from routine back-office maintenance to the strategic foundation of enterprise competitive advantage.

3. Redefining IT M&A Toward Specialized Technical Density

Historically, IT services mergers focused on acquiring offshore operations to capture geographic labor cost arbitrages. However, modern enterprise modernization demands deep specialization in high-performance cloud architectures and foundation models. Acquiring dense, highly certified engineering squads represents a critical strategy for legacy services firms seeking to remain indispensable to Fortune 500 decision-makers.

Conclusion

The EXL acquisition of Aimpoint Digital represents an important maturing moment for enterprise technology services. By bringing together the capabilities of AI consulting and front-line platform engineering, the deal eliminates the friction that has always undermined digital transformation initiatives. From the standpoint of the overall AI and IT services industry, this deal sends the message that the future of enterprise technology services depends upon the realization of the value of AI from theory to production practice.

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Governing the Non-Human Workforce: How Citrix’s Agentic Workspaces and Governance Framework Reshape Enterprise AI

ITDigest Bureau — Thu, 05 Nov 2026 11:56:44 +0000

Citrix, the digital workspace and virtualization leader and a Cloud Software Group unit, has released an array of enterprise governance functionalities, isolated agentic execution environments, and consumption-based licensing approaches. The release comprises both Citrix SecurSpaces and Citrix SecurAccess, which are specifically designed to solve one of the key operational challenges of artificial intelligence: the disconnect between fast adoption of autonomous AI agents in enterprises and inadequate security guardrails.

With the development of enterprise AI solutions moving beyond chat assistants into the territory of multi-step autonomous agents that operate business logic within the enterprise systems, businesses find themselves in a rather dangerous situation. According to Deloitte research published in 2026, despite the growing use of autonomous agents in enterprises’ operations, only 21% of companies have a sufficiently developed governance framework for managing them. It is here that Citrix provides a solution by considering autonomous agents not as software scripts, but as privileged non-human identities that need to be isolated and audited with Zero Trust DLP.

“The organizations that will benefit most from AI won’t be the ones that move the fastest. They’ll be the ones that can adapt the fastest,” said Hector Lima, president of Citrix. “That requires a foundation of visibility, governance, and control across users, applications, and AI agents. With these innovations across Citrix SecurSpaces, Citrix SecurAccess and a new credit-based licensing model that spans our Citrix portfolio, we’re helping customers build adaptive enterprises that can securely embrace AI, govern how work gets done, and evolve as business needs and technology continue to change.”

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Sandboxed Agent Execution and Session Attribution

Traditionally, enterprise IT shops wanting to implement their own autonomous agents had to make a tough architectural choice: either provide access to agents directly from workstations at risk of credential leakage, prompt injection, and lateral movement in the network or limit access of agents to sandboxed containers where they can’t use any of the required tools to do any productive work.

The new architecture of AI from Citrix solves the operational frictions on three technical layers:

SecurSpaces for Agentic Workloads: Isolated, ephemeral containerized environments for attended and unattended agentic work. The outbound proxy services inject enterprise credentials at runtime, thus giving agents a chance to query databases and MCP servers without storing any of the secrets.

SecurAccess & Session Insights: Combines zero trust network access ZTNA and browser-based telemetry to analyze agent and human sessions in real time. Machine learning identifies abnormal or risky sequences of events, thus creating an immutable audit trail for forensic analysis.

Citrix Platform Flex (Consumption pricing): Credit-based flexible consumption pricing allows scaling compute power and security policies dynamically as AI loads grow without locking in to fixed upfront user licenses.

Strategic Impact on the Artificial Intelligence (AI) Industry

Establishing secure runtime environments and session observability introduces fundamental structural shifts across the broader Artificial Intelligence landscape:

1. Transitioning Non-Human Agents into “Privileged Users”

For the past two years, the AI sector treated LLM security primarily through the lens of prompt filtering and model alignment. However, once an agent connects to external tools and files, the threat model shifts from output moderation to identity and access governance. Citrix’s release formalizes an emerging industry standard: AI agents must be treated as privileged non-human identities. They require dedicated access scopes, least-privilege credentials, and continuous authentication rather than open-ended API keys.

2. Resolving the Autonomous Agent “Auditability Trap”

A primary reason enterprise Chief Information Security Officers (CISOs) block autonomous agents from production is the lack of causal attribution. If an agent deletes a record, changes a database schema, or exfiltrates files, legacy application logs show only that an authorized API key performed the task—not why or how the decision was reached. Delivering real-time session capture and visual timeline tracking bridges this attribution gap, giving risk officers the auditable proof required to approve autonomous agents in regulated environments like banking and healthcare.

3. Standardizing Sandboxing for Citizen-Built and Open-Source Models

Enterprise employees are increasingly deploying open-source models, local LLMs, and citizen-developed agent scripts. Without isolated runtimes, these ungoverned workloads risk polluting corporate networks with rogue processes. Providing sandboxed workspaces decouples the experimental freedom of AI development from underlying corporate infrastructure, accelerating innovation without endangering core enterprise systems.

Conclusion

The Citrix implementation of Agented Workspaces and Session Governance is an important milestone for enterprise artificial intelligence infrastructure. With the use of isolated application virtualization along with Zero Trust identity security and dynamic consumption, Citrix makes the leap from autonomous intelligence to corporate compliance possible. In relation to the wider AI market, this launch serves to show that the large-scale deployment of autonomous intelligence cannot be achieved simply by making smarter AI models.

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ModelOp Launches Full-Stack AI Policy Enforcement with NVIDIA OpenShell Integration

ITDigest Bureau — Thu, 05 Nov 2026 11:35:59 +0000

ModelOp has launched Full-Stack AI Policy Enforcement with NVIDIA OpenShell that extends the governance of AI policies into the operating system level for organizations. This platform helps organizations ensure that their autonomous AI agents adhere to corporate policies while working within production environments. This platform manages and enforces the AI policies at four different levels including process, execution, operating system (OS), and network levels. According to ModelOp, this integration of the platform with NVIDIA OpenShell, an open-source secure runtime for AI agents, introduces OS-level control that regulates the files, processes, and network connections that the agents can use.This solution provides deny-by-default kernel-level controls that work independently of an agent’s process. This platform also integrates sandbox permissions with the purpose of the agent, the owner, risk tier, and approved version of the agent.

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This allows organizations to centrally manage the policies, synchronize the enforcement configurations, and route any blocked actions or policy exceptions through review processes.“The biggest misconception in enterprise AI governance is that approving an agent means it will adhere to all of your policies in production,” said Dave Trier, CEO of ModelOp. “An agent can pass every risk assessment and approval and still violate policy the moment it starts operating. If your governance ends at deployment, you’re not actually enforcing your policy. You’re documenting what you hoped it would do. ModelOp bridges this gap.”The integration builds on ModelOp’s existing enforcement across workflows, execution platforms and gateways. It aims to give enterprise technology, security and governance teams a centralized record of policy decisions and compliance across AI agents.

TestGrid Unveils TestGrid ONE, Delivering an AI Operating System to Modernize Enterprise Quality Engineering

ITDigest Bureau — Thu, 05 Nov 2026 10:49:44 +0000

AI-native testing technology provider TestGrid has announced the general availability of TestGrid ONE, the architectural successor to its TestOS platform.

The new version moves the TestGrid unified testing environment to become an artificial intelligence operating system built specifically for enterprise quality engineering. With the combination of specialized autonomous agents, cross-platform automation, physical testing infrastructure, context-based diagnostics, and enterprise DevOps integration capabilities, TestGrid ONE helps companies get a unified basis to drive quality assurance operations in fragmented application landscapes.

Unification of Disparate Testing Frameworks and Infrastructures Without Proprietary Lock-In

Engineering and Quality Assurance (QA) operations face increased friction in their activities today. Faster validation cycles lead to siloed testing capabilities across various open-source frameworks, legacy toolchains, and separate device farms. Moreover, with the increasing interest in autonomous testing agents, adoption of these technologies is normally associated with a need to rewrite code or vendor lock-in that would render previous work irrelevant.

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These issues become a thing of the past with the help of TestGrid ONE. The tool allows developers to run existing tests in any test framework such as Selenium, Playwright, Appium, Cypress, Espresso, XCUITest, WebdriverIO, Detox, Robot Framework, and CodeceptJS without modifying the code.
Platform Architecture consists of five operational competencies:

Autonomous AI Agents: These include the CoTester agent that uses vision-language intelligence to generate test cases out of the product specification while retaining human approval gates and the AgentRx which gives self-healing locators for UI tests in case of changing front-end interface. Other purpose-built agents include visual regression, accessibility auditing, change impact assessment, root cause diagnostics, and DevSecOps orchestration.

Hybrid Test Infrastructure Management: The skill set gives access to proprietary physical test devices for iOS and Android platforms and browser test infrastructure for testing on Google Chrome, Mozilla Firefox, Apple Safari, and Microsoft Edge browsers. Companies that manage their own physical lab can combine their customer-owned devices with the TestGrid test device management infrastructure via the same management interface.

Flexible Enterprise Setup: Flexible private cloud setup; on-premises and hybrid deployment; supports air-gapped deployment suitable for highly-regulated verticals. MDM support and Virtual USB allow for testing specialized hardware and internal configurations.

Multifaceted Application Testing: Testing goes beyond the basic web and mobile app surface area to include REST/SOAP API testing, performance testing, audio testing, and IoT testing, as well as enterprise ERP and CRM solutions like Salesforce, SAP, ServiceNow, and Microsoft Dynamics.

Complete Failure Analysis: Provides recording of a test session in sync with raw system logs and network data upon application failure; allows to analyze defects through ADB, Appium Inspector, and native browser development consoles.

Executive Commentary and Industry Perspective

“We have always believed testing infrastructure and testing software should operate as one system,” said Harry Rao, Founder and CEO of TestGrid. “AI agents extend that idea much further. With TestGrid ONE, enterprises can apply specialized agents alongside the infrastructure, automation, evidence, and workflows their teams already rely on, giving quality engineering a foundation that can evolve as software development changes.”

Toolchain Interoperability and Ecosystem Scale

To ensure seamless integration across enterprise continuous integration and continuous delivery (CI/CD) pipelines, TestGrid ONE incorporates more than 100 pre-built connectors. Development teams can trigger automated test executions directly from platforms such as GitHub Actions, GitLab CI/CD, Jenkins, and Azure DevOps, while routing real-time failure triage and analytical metrics to Jira, TestRail, and Slack.

The post TestGrid Unveils TestGrid ONE, Delivering an AI Operating System to Modernize Enterprise Quality Engineering appeared first on ITDigest.

Bridging the Data Highway: How Lumen and Microsoft’s Expanded Alliance Redefines AI and Enterprise IT Infrastructure

ITDigest Bureau — Tue, 03 Nov 2026 10:53:43 +0000

Leading telecommunication and network provider Lumen Technologies Inc. along with leading Microsoft Corp. have taken their strategic partnership to the next level with co-selling of enterprise AI ready networks. As a result of the collaboration, Lumen’s extensive suite of high-speed networking and connectivity is incorporated into Microsoft Azure and the Microsoft Marketplace. Thus, the alliance addresses one of the most difficult challenges associated with digital transformation – bridging dispersed enterprise data with advanced AI processing.

The transition of enterprise AI approaches from isolated pilots to distributed agentic models that operate in production raises a persistent physical issue. Mission-critical enterprise data are distributed in data centers, edge computing nodes, and branch locations. The traditional infrastructure of a corporate network designed to handle stable web traffic finds it difficult to address the needs of high speed and low latency of AI inference and Retrieval-Augmented Generation (RAG).

“AI is reshaping where applications run and how organizations use and move data,” said Katy Brown, corporate vice president, Americas Markets and Industries at Microsoft. “Microsoft is committed to giving customers choice as they connect existing environments to Azure. Our work with Lumen is focused on helping customers align their broader network transformation with their Azure cloud and AI priorities, while taking advantage of Azure’s native networking, security, scale, and innovation.”

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Behind the Scenes: Network Fabric & Hyperscale Compute Come Together

Traditionally, the cloud infrastructure design and the corporate network have been two different worlds. Engineers set up their GPU clusters on Azure but quickly realized that transmitting terabytes of live telemetry from their own databases introduced latency and high egress fees.

This new partnership will bridge the gap between these practices through the following architectural approaches:

Market Alignment: The sales teams work together to address potential customers early and design the network architecture while deploying Azure AI services.

Simplified Market Purchase: Companies can now buy Lumen network infrastructure, such as a multi-cloud orchestrator called Alkira, based on Azure consumption commitment.

Low-Latency Agentic Rails: The partnership ensures that the fiber interconnects will work for Microsoft runtimes and facilitate data transfers for autonomous AI agents.

Strategic Impact on the AI and IT Industries

Integrating carrier-grade fiber directly into hyperscale cloud environments drives vital structural shifts across Artificial Intelligence and Enterprise IT:

1. From Parameter Count and Raw GPU to Data Movement

In the past, the AI industry was obsessed with parameters count and raw GPU performance. In reality, model efficiency is hampered by context consumption. Modern RAG systems and autonomous agents need to have constant data synchronization across distributed databases. By focusing on the carrier-grade network infrastructure, one ensures that high-performance data movement becomes a key performance characteristic that guarantees no data starvation in a live model.

2. Convergence of NetOps, CloudOps, and SecOps

The partnership between Lumen and Microsoft leads to the dissolution of traditional IT silos. Instead of having network specialists, cloud architects, and security officers working separately, it is necessary to combine connectivity, cloud computing, and data security into one unified practice starting at the early project stage.

3. Simplified IT Procurement and Multicloud Visibility Gaps Solution

The use of Microsoft Marketplace for ordering enterprise network links via pre-committed Azure credits significantly simplifies FinOps. Moreover, software-defined network fabrics replace fragile point-to-point VPNs and scripts, giving IT leadership a unified control plane for implementing the same security policy and monitoring traffic globally.

General Impact on Enterprises That Are Operating in This Industry

The Lumen-Microsoft Partnership has set the following guidelines for enterprises, cloud providers, and telecom companies:

Increasing Pressure for Other Hyperscalers: The other cloud providers have increased pressure to make co-selling partnerships with the top fiber carriers because of increasing demands from enterprises to buy their AI services with connectivity included.

Transformation of Traditional Telecom Services: The legacy telecommunications companies that are offering their services through commoditized bandwidth will experience thin margins unless they create software-based, API-accessible platforms that integrate with cloud marketplaces.

AI Implementations Becoming De-Risked: The enterprise CIOs can now pursue their complicated plans with confidence since there is proper integration between the network bandwidth, data ingestion latency, and cloud computing capacity.

Conclusion

The growth of the relationship between Lumen and Microsoft signifies a necessary stage of maturity within enterprise technology. Through the combination of the capabilities of Azure’s AI ecosystem and the physical infrastructure of Lumen’s network, this relationship solves the challenge of connectivity that previously limited the design of hybrid clouds. In the wider world of enterprise intelligence and information technology, it signifies that enterprise intelligence depends not only on computing capacity, but on network capability.

The post Bridging the Data Highway: How Lumen and Microsoft’s Expanded Alliance Redefines AI and Enterprise IT Infrastructure appeared first on ITDigest.

Artificial Intelligence Archives - ITDigest

Intel and RadixArk Partner to Advance Open AI Inference

Why It Matters

Also Read: Plurai Raises $13 Millions to Build Trustworthy AI Agent Infrastructure

What It Delivers for Developers

An Open Blueprint for Production AI

Plurai Raises $13 Millions to Build Trustworthy AI Agent Infrastructure

From simulation to production

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Founders shaped by autonomous driving

Validation and traction: NVIDIA, Nebius and Gartner

Additional Quotes

Earning enterprise trust in agentic AI: a conversation with Uma Sandilya, Chief Commercial Officer, Kore.ai

DataBahn Appoints Vishesh Choudhary as SVP of Partnerships to Expand Channel Growth

Also Read: J.P. Morgan Private Bank Appoints Claire Moore as VP, Senior Events Manager for APAC

Securing the Agentic Enterprise: Why Cisco’s Move Redefines Cybersecurity and Enterprise AI

The Announcement: Preemptive Testing and Continuous Observability

The update introduces several strategic pillars:

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Effect on Cyber Security Sector

Broader Impact on Business Using AI

The Way Ahead

Bridging the Production Gap: How EXL’s Acquisition of Aimpoint Digital Accelerates Enterprise AI and IT Services

Also Read: Securing the Citizen-Developer Boom: How CrowdStrike and Lovable’s Alliance Redefines Cybersecurity and IT Governance

Emergence of Forward-Deployed Platform Engineering

Strategic Impact on the AI and IT Services Industry

Conclusion

Governing the Non-Human Workforce: How Citrix’s Agentic Workspaces and Governance Framework Reshape Enterprise AI

Also Read: Securing the Citizen-Developer Boom: How CrowdStrike and Lovable’s Alliance Redefines Cybersecurity and IT Governance

Sandboxed Agent Execution and Session Attribution

Strategic Impact on the Artificial Intelligence (AI) Industry

Conclusion

ModelOp Launches Full-Stack AI Policy Enforcement with NVIDIA OpenShell Integration

Also Read: FactSet and Hamilton Lane Target Unified Portfolio Management With Data Integration

Read More: ModelOp Unveils Industry-First Full-Stack AI Policy Enforcement With NVIDIA OpenShell

TestGrid Unveils TestGrid ONE, Delivering an AI Operating System to Modernize Enterprise Quality Engineering

Unification of Disparate Testing Frameworks and Infrastructures Without Proprietary Lock-In

Also Read: CrowdStrike to Acquire Automated AI Security Testing Pioneer Dreadnode to Secure Agentic Systems Before Deployment

Executive Commentary and Industry Perspective

Toolchain Interoperability and Ecosystem Scale

Bridging the Data Highway: How Lumen and Microsoft’s Expanded Alliance Redefines AI and Enterprise IT Infrastructure

Also Read: TestMu AI Introduces One-Click Migration From Katalon to KaneAI

Behind the Scenes: Network Fabric & Hyperscale Compute Come Together

Strategic Impact on the AI and IT Industries

General Impact on Enterprises That Are Operating in This Industry

Conclusion