As enterprises race to deploy AI agents across customer service operations, a new challenge has emerged: building an AI agent is easy, but building one that can reliably handle real customers, complex workflows, and production-scale demands is far harder.

Cresta aims to solve that problem with the launch of Conductor, a developer-focused agentic engine designed to automate much of the AI agent development lifecycle while maintaining the governance and oversight enterprises require.

The company claims Conductor can help engineering teams deploy production-ready AI agents twice as fast by using natural language, real customer conversations, and enterprise workflow intelligence to design, build, test, and optimize AI-powered customer experience agents.

The launch comes as businesses increasingly move beyond AI experimentation and into large-scale deployment. While countless platforms can generate agent prototypes and demonstrations, enterprises are discovering that production-grade AI requires extensive testing, system integrations, workflow orchestration, and ongoing optimization.

Cresta believes that gap between prototype and production is where most organizations struggle—and where Conductor is designed to help.

"Building production-ready AI agents is one of the hardest engineering challenges in the enterprise right now," said Ping Wu, CEO of Cresta.

Rather than functioning as another AI assistant, Conductor acts as what Cresta calls an "agent-building agent"—an AI system designed specifically to create and improve other AI agents.

Moving Beyond AI Demos

The rise of generative AI has dramatically lowered the barrier to creating conversational agents. However, enterprise customer experience environments introduce a level of complexity that many low-code or no-code agent builders cannot handle.

Customer service agents frequently need access to proprietary systems, payment platforms, reservation engines, CRM environments, internal APIs, and custom business logic. They also require strict governance, compliance controls, and extensive testing before interacting with customers.

Many organizations discover that creating a proof of concept takes days, while making that agent production-ready can take months.

Conductor is designed to automate much of that process.

Instead of starting with prompts alone, the platform begins with discovery. It reviews documentation, analyzes platform insights, examines customer interactions, and gathers business context before proposing a structured blueprint for the AI agent.

Developers then review and approve that blueprint before development begins.

The approach mirrors software engineering best practices, where architecture and requirements are validated before code is written.

According to Cresta, this reduces development errors and creates a more predictable path to deployment.

How Conductor Works

At the core of Conductor is a workflow that spans the entire AI agent lifecycle, from planning to post-launch optimization.

The first stage focuses on discovery and blueprint creation.

Conductor reviews enterprise documentation, knowledge bases, customer conversations, and existing system data to understand the intended use case. It can also ask developers clarifying questions to gather additional context before generating a comprehensive development plan.

Once approved, Conductor automatically generates the components needed to build the agent.

This includes prompt logic, sub-agent orchestration, configurations, integrations, and custom code required for deterministic actions such as payment processing, account updates, or reservation management.

That distinction is important because enterprise AI agents increasingly rely on more than conversational capabilities. They must execute real business actions safely and reliably.

Rather than simply generating responses, modern customer service agents are expected to complete tasks.

Conductor's architecture reflects that reality.

Testing Before Customers Ever See It

One of the biggest challenges facing enterprise AI deployments is quality assurance.

Unlike traditional software, AI systems can behave unpredictably when exposed to new customer inputs or edge-case scenarios.

To address that issue, Conductor integrates directly with Cresta's Testing Suite and Synthetic Customers platform.

The system automatically generates testing scenarios based on the approved blueprint and runs simulations before deployment.

If failures occur, Conductor identifies the underlying issue, proposes fixes, and re-tests the agent until it reaches predefined performance thresholds.

This automated feedback loop could significantly reduce the time developers spend manually validating AI behaviors.

As enterprises become more cautious about deploying customer-facing AI, testing infrastructure is increasingly becoming a competitive differentiator among AI platform vendors.

Companies are realizing that deployment speed matters far less than deployment reliability.

Post-Launch Optimization Built In

The challenge does not end once an AI agent goes live.

Customer interactions constantly evolve, products change, and new edge cases emerge over time.

Conductor includes post-launch monitoring and diagnostics designed to address those realities.

When issues surface in production, the system reviews customer transcripts, identifies root causes, and generates a prioritized list of recommendations.

For routine issues, Conductor can autonomously implement fixes, validate the results, and present proposed updates for developer approval before changes are deployed.

The approach resembles emerging AI-assisted software development workflows, where AI systems not only generate code but also participate in debugging, testing, monitoring, and optimization.

In effect, Conductor functions as both an AI developer and an AI operations assistant.

Why This Matters

The launch highlights a broader trend in enterprise AI: the rise of AI systems designed to create and manage other AI systems.

As organizations scale their AI investments, manually building and maintaining thousands of specialized agents becomes increasingly impractical.

Industry leaders including OpenAI, Microsoft, Salesforce, Google, and Anthropic have all emphasized agentic AI as the next major phase of enterprise adoption. However, creating reliable, business-ready agents remains a significant bottleneck.

The market is now shifting from agent creation to agent operations.

Questions around governance, testing, monitoring, orchestration, and optimization are becoming just as important as model performance itself.

Cresta's Conductor enters the market at a time when enterprises are searching for ways to accelerate AI deployment without sacrificing control.

By combining blueprint generation, automated development, testing, diagnostics, and optimization into a single workflow, the company is attempting to reduce the complexity associated with enterprise AI rollouts.

The Bigger Picture

The emergence of platforms like Conductor signals a new phase in enterprise AI adoption.

The first wave focused on building AI assistants. The second wave focused on deploying AI agents. The next wave may focus on AI systems that build, govern, and optimize those agents automatically.

For enterprises facing pressure to deploy customer-facing AI at scale, that evolution could prove essential.

As organizations increasingly treat AI agents as part of their operational infrastructure, the tools used to build and maintain those agents may become just as valuable as the agents themselves.

With Conductor, Cresta is positioning itself squarely in that emerging category—where AI doesn't just assist developers but actively helps create the next generation of enterprise AI systems

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The race to operationalize AI agents is moving beyond customer service and marketing—and into one of the most complex areas of the enterprise: data engineering.

Genesis Computing has announced a new partnership with Databricks that brings its autonomous data engineering agents directly into Databricks environments, allowing enterprises to automate data-intensive workflows without moving sensitive information outside their existing infrastructure.

The company has become a Validated Technology Partner of Databricks, a designation that signals technical compatibility with the data and AI platform's ecosystem. More importantly, it gives Databricks customers access to AI-powered agents designed specifically for data engineering tasks that have traditionally required significant human effort and institutional knowledge.

As enterprises continue investing heavily in AI initiatives, one challenge remains stubbornly persistent: the complexity of preparing, governing, migrating, and managing enterprise data. Genesis believes autonomous agents can help bridge that gap.

From AI Copilots to Autonomous Data Engineers

Much of the AI conversation over the past two years has centered on copilots—systems that assist workers by generating recommendations, code, or insights.

Genesis is targeting a different outcome.

Rather than offering suggestions for engineers to manually implement, its platform focuses on autonomous execution. The company's pretrained agents are designed to complete end-to-end data engineering tasks, including migrations, root-cause analysis, onboarding workflows, documentation, testing, and data pipeline management.

For organizations struggling with growing data estates and mounting technical debt, that distinction matters.

Enterprise data environments often span thousands of datasets, pipelines, governance policies, and business processes. Understanding how those systems interact frequently requires years of institutional knowledge.

Genesis says its platform addresses that challenge through what it calls the Genesis Context Graph, a system that continuously learns from an organization's existing data ecosystem.

The goal is to provide AI agents with a contextual understanding of enterprise systems, workflows, governance frameworks, and business rules before they begin executing tasks.

In effect, the platform attempts to turn tribal knowledge into machine-readable intelligence.

Why Databricks Customers May Care

For Databricks users, the partnership focuses on enabling AI-driven automation while maintaining existing security, governance, and compliance frameworks.

The Genesis agents operate directly inside customer-controlled Databricks environments, eliminating the need to move data into third-party systems for processing.

That architecture could be particularly attractive to heavily regulated industries where data residency, privacy, and governance requirements limit the use of external AI services.

According to Genesis, its agents are capable of understanding and interacting with key Databricks technologies, including Delta Lake assets and Unity Catalog governance controls.

This allows the agents to perform tasks while respecting existing permissions, policies, and compliance requirements already established within the Databricks environment.

Potential use cases include:

• Legacy data migration projects

• Data pipeline root-cause analysis and troubleshooting

• Customer data onboarding

• Data catalog management

• Automated documentation generation

• Pipeline testing and validation

• Workflow automation across enterprise data systems

By embedding directly within Databricks, the company aims to remove one of the primary barriers slowing enterprise AI adoption: trust.

Organizations often hesitate to grant AI systems access to critical data infrastructure if it requires moving information outside controlled environments. Genesis is positioning its deployment model as a way to maintain security while still benefiting from automation.

The Growing Market for Agentic Data Engineering

The announcement reflects a broader trend emerging across enterprise AI.

While early AI deployments focused on content creation, coding assistants, and customer support automation, attention is increasingly shifting toward operational workflows that generate measurable business outcomes.

Data engineering has become a prime target.

Organizations are facing unprecedented growth in both structured and unstructured data, while simultaneously dealing with talent shortages and increasing pressure to deliver AI-ready data pipelines faster.

This has created growing demand for tools that can automate routine engineering tasks while preserving governance and quality controls.

Industry leaders including Databricks, Snowflake, Microsoft, Google Cloud, and AWS have all expanded investments in AI-driven data management over the past year. Agentic systems capable of performing multi-step workflows autonomously are becoming a major focus area across the data ecosystem.

Genesis is entering that market with a strategy centered on contextual intelligence and execution rather than simple recommendation engines.

A Real-World Customer Example

To illustrate the platform's potential impact, Genesis highlighted results from healthcare data platform Abacus Insights.

According to the company, Abacus deployed Genesis agents within its Databricks environment to automate customer data mapping and pipeline development tasks.

The reported outcomes were substantial:

• Deployment timelines reduced from months to weeks

• Data discovery and mapping accelerated from weeks to days

• More than 50% reduction in pipeline engineering effort

While customer success stories should always be viewed through the lens of vendor-provided metrics, the results align with a growing enterprise goal: reducing the operational burden associated with preparing and managing data.

As AI initiatives expand, data engineering often becomes the bottleneck. Tools that can meaningfully accelerate those workflows are likely to attract increasing attention from enterprise technology leaders.

The Bigger Picture

Genesis' partnership with Databricks underscores a larger shift in enterprise AI adoption.

The next phase of AI is increasingly focused on execution rather than assistance.

Organizations no longer want AI systems that simply identify problems or generate recommendations. They want agents capable of understanding enterprise context, navigating governance requirements, and completing complex workflows autonomously.

For data teams, that could mean moving beyond AI copilots toward something closer to a digital workforce for data operations.

Whether autonomous data engineering becomes mainstream remains to be seen, but the momentum is clearly building. As enterprises search for ways to manage growing data complexity without proportionally expanding engineering teams, platforms that combine contextual intelligence with autonomous execution may become a critical part of the modern data stack.

With its Databricks integration, Genesis is positioning itself at the center of that emerging opportunity

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The AI coding boom has dramatically accelerated software development, but it has also exposed a growing problem: AI agents are getting faster at writing code than they are at proving the code actually works.

TestSprite believes it has found the missing piece.

The company has launched TestSprite CLI, an open-source command-line verification tool designed specifically for autonomous coding agents. Released under the Apache 2.0 license, the tool allows AI agents to test, diagnose, and validate their own work across both frontend and backend systems before marking tasks as complete.

At a time when AI coding assistants are evolving into fully autonomous software engineers capable of working for hours without human supervision, TestSprite is tackling what many developers now consider the industry's biggest bottleneck: verification.

The launch reflects a broader shift occurring across software development. For years, innovation centered on helping developers write code faster. Now the focus is increasingly moving toward ensuring AI-generated code remains reliable, maintainable, and production-ready.

In other words, the challenge is no longer code generation. It's quality control.

The Verification Problem Nobody Talks About

The latest generation of coding agents from companies like Anthropic, OpenAI, and Google can independently complete multi-hour development tasks with minimal human intervention.

But speed comes with tradeoffs.

AI agents frequently declare features finished despite introducing hidden bugs, broken interfaces, failed workflows, or regressions that impact previously functioning components.

Developers are increasingly discovering that an AI-generated feature can appear complete while quietly breaking something else elsewhere in the application.

This creates what TestSprite calls the verification gap.

Traditional testing tools were designed for humans actively reviewing code through IDEs, dashboards, and manual QA workflows. Autonomous AI agents operate differently.

They live inside terminals.

They execute tasks independently.

And increasingly, they make deployment decisions without direct oversight.

According to TestSprite, existing verification methods simply weren't built for that environment.

Its answer is to bring quality assurance directly into the agent workflow.

A QA System Built for AI Agents

Unlike conventional testing frameworks, TestSprite CLI is designed as part of an autonomous feedback loop.

An AI coding agent describes the intended behavior of a feature once.

From there, TestSprite executes tests against real applications rather than simulated environments. It interacts with live browsers and production-like APIs, avoiding mocked systems that can hide real-world issues.

When failures occur, the platform returns a comprehensive diagnostic package.

Instead of simply reporting an error, it provides:

• The failing step and surrounding execution context

• Screenshots of the issue

• DOM snapshots

• Test source code

• Root-cause hypotheses

• Recommended fixes

The AI agent then reviews the findings, updates the code, and reruns the validation cycle.

The process repeats until the software passes.

The result resembles an autonomous software development loop where coding, testing, debugging, and validation occur continuously without human intervention.

Perhaps more importantly, every successful test is retained and added to a growing regression suite.

That means each development phase increases the safety net protecting future changes.

Why Regression Is Becoming AI's Biggest Challenge

One of the most interesting aspects of TestSprite's announcement is its focus on regressions.

In traditional software development, regressions occur when new code unintentionally breaks functionality that previously worked.

For AI coding agents, the problem appears to be far more common than many organizations realize.

Because AI agents focus primarily on the task in front of them, they often fail to revisit older functionality unless specifically instructed to do so.

As projects become more complex, this creates an accumulating risk.

An agent may successfully complete Feature A, move on to Feature B, and unknowingly break Feature A in the process.

Without continuous testing, the issue may remain hidden until users discover it.

TestSprite argues that regressions represent the single biggest obstacle preventing truly autonomous software engineering.

The company's early findings suggest the concern is justified.

New Metrics for the Agentic Development Era

Alongside the CLI launch, TestSprite is introducing what it describes as a new category of AI development benchmarks.

Current industry evaluations largely focus on coding speed, task completion rates, token efficiency, or benchmark scores.

TestSprite says those metrics fail to capture how AI agents perform over long development cycles.

Instead, the company is tracking factors such as:

• First-attempt success rates

• Improvement after feedback

• Unresolved failures

• Regression rates

• Long-term feature stability

The goal is to measure how well AI agents maintain software quality across extended projects rather than isolated coding exercises.

That distinction could become increasingly important as organizations deploy AI systems for production software development.

A model that completes tasks quickly but introduces constant regressions may ultimately create more work than it saves.

What CoderCup Is Revealing

Many of the company's findings come from CoderCup, an ongoing public competition where leading AI coding agents build the same multi-phase web application under identical conditions.

The competition includes systems such as Anthropic's Claude Code, OpenAI Codex, and Google's Antigravity platform.

TestSprite serves as the independent verification layer, evaluating each phase through extensive end-to-end testing.

The results have revealed several noteworthy trends.

According to TestSprite, one AI agent started a development phase with none of its target features functioning correctly. After approximately ten rounds of automated testing, debugging, and verification, the same model achieved around 80% feature completion without changing the underlying model.

The only difference was access to a structured verification loop.

The company argues this demonstrates a new phenomenon: AI agents can effectively "self-evolve" when given reliable feedback mechanisms.

Equally significant was the prevalence of regressions.

Even the strongest-performing agent reportedly broke approximately 12% of previously working functionality during a single development run.

Less capable systems approached regression rates of 25%.

Those numbers help explain why developers remain hesitant to fully trust autonomous coding agents despite their rapid advances.

The Bigger Picture

The launch highlights an important evolution in AI-assisted software development.

For the past two years, attention has centered on making models smarter, faster, and more capable of writing code.

Increasingly, however, competitive advantage may come from verification systems rather than generation systems.

As autonomous agents become capable of building entire applications, the industry's next challenge is ensuring those applications remain stable over time.

Verification tools, automated testing frameworks, and AI-native quality assurance platforms are rapidly becoming critical infrastructure for the agentic software era.

Perhaps the most surprising takeaway from TestSprite's research is that stronger verification may reduce dependence on increasingly expensive frontier models.

The company found that smaller, more cost-efficient models were often able to achieve comparable feature completeness after multiple feedback cycles.

In other words, better testing may matter more than bigger models.

That insight could have major implications for enterprises looking to scale AI-driven software development without dramatically increasing infrastructure costs.

For now, TestSprite is betting that the future of autonomous coding won't be defined solely by how quickly agents can write software—but by how effectively they can prove that software actually works

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The advertising industry's AI ambitions are moving beyond chatbots and campaign assistants toward something far more transformative: autonomous agents that can negotiate, plan, buy, and optimize media on behalf of marketers and publishers.

Magnite is positioning itself at the center of that shift.

The sell-side advertising giant has launched Magnite Orchestration, a new coordination layer designed to connect AI-powered buyer agents with publisher-side seller agents across premium media inventory. The platform aims to create a shared environment where autonomous systems can discover inventory, evaluate audiences, package deals, and activate campaigns with minimal human intervention.

The announcement marks one of the clearest signs yet that agentic AI is beginning to reshape the mechanics of digital advertising itself—not just the creative and analytics layers surrounding it.

As part of the rollout, Magnite is expanding both its buyer and seller agent capabilities and is already testing the infrastructure with major industry partners, including dentsu and DIRECTV Advertising.

Moving From Automation to Agentic Advertising

For years, programmatic advertising has promised automation. Yet despite advances in bidding algorithms and audience targeting, much of the media buying process still relies on manual coordination between agencies, publishers, platforms, and data providers.

Magnite believes AI agents can eliminate much of that friction.

The company's new Orchestration layer acts as a connective framework that enables multiple AI systems to work together inside a common environment. Rather than requiring marketers to manually search for inventory, compare audience segments, negotiate packages, and activate campaigns, software agents can perform many of those functions autonomously.

The vision resembles a future where advertising transactions increasingly occur between machines rather than humans.

According to Sean Buckley, President of Revenue and Market Strategy at Magnite, the value lies not in AI alone but in integrating AI directly into the systems that already power media transactions.

By embedding AI into the advertising infrastructure itself, Magnite aims to connect campaign intent with execution more efficiently than traditional workflows allow.

What Magnite Orchestration Does

At its core, Magnite Orchestration serves as an interoperability layer for AI-driven advertising systems.

The platform enables buyer agents to connect directly with Magnite's seller-side infrastructure and access what the company describes as one of the industry's largest pools of premium omnichannel inventory.

This includes inventory across:

• Connected TV (CTV)

• Online video

• Audio advertising

• Display advertising

• Home screen advertising environments

Beyond inventory access, the platform also allows publishers, agencies, and data providers to expose audience intelligence directly to AI agents.

That capability could prove particularly important as advertisers increasingly prioritize first-party data and proprietary audience assets in a post-cookie environment.

Instead of audience data being managed separately from media inventory, Magnite is enabling those assets to be packaged together and surfaced dynamically when relevant campaign opportunities arise.

The result is a more connected ecosystem where inventory, audiences, and optimization signals can be evaluated simultaneously by autonomous systems.

How Buyer and Seller Agents Work

The launch introduces expanded functionality for both sides of the advertising transaction.

On the publisher side, the Magnite Seller Agent allows media owners to create customized inventory packages that combine audience targeting, pricing structures, and inventory preferences.

These packages can then be discovered directly by buyer agents operating within the Orchestration environment.

The goal is to enable agent-to-agent transactions where AI systems identify opportunities, evaluate fit, and facilitate activation without extensive manual intervention.

For advertisers and agencies, Magnite offers two paths.

Organizations can connect their own proprietary AI agents through open integrations, or they can use the Magnite Buyer Agent directly.

The Buyer Agent supports several key functions, including:

• Generating media plans from simple RFPs

• Discovering relevant inventory and audience opportunities

• Identifying campaign optimization opportunities

• Creating advertising creatives

• Launching omnichannel campaigns across multiple formats

• Managing activation through a unified workflow

This flexibility reflects a growing trend in enterprise AI, where companies increasingly want infrastructure that supports their own agents rather than forcing adoption of proprietary systems.

Why dentsu and DIRECTV Matter

The inclusion of dentsu and DIRECTV Advertising as testing partners highlights how Magnite is approaching interoperability.

One of the biggest challenges facing agentic advertising is connecting audience intelligence, inventory supply, identity frameworks, and optimization systems across multiple organizations.

dentsu's participation provides an example of how that ecosystem could function.

Through its existing integration with Magnite, dentsu has connected dentsu.Audiences segments into the Orchestration environment, allowing AI agents to surface proprietary audience data whenever campaign objectives align with available opportunities.

The integration effectively brings agency-owned audience intelligence closer to campaign activation.

For DIRECTV Advertising, the focus is on inventory accessibility.

As connected TV continues to attract a growing share of advertising budgets, ensuring premium television inventory can be discovered and activated through emerging AI workflows becomes increasingly important.

Together, the partnerships demonstrate how Magnite is attempting to bridge traditionally separate parts of the advertising value chain.

The Bigger Industry Trend

Magnite's announcement arrives as the advertising industry enters what many executives are calling the agentic era.

Major technology companies including Google, Salesforce, Adobe, Amazon, Microsoft, and OpenAI have all invested heavily in AI agents capable of automating increasingly complex business processes.

Advertising is emerging as one of the most promising applications.

Campaign planning, audience discovery, media buying, creative generation, optimization, measurement, and reporting all involve repetitive workflows that AI systems are well-positioned to automate.

However, achieving that vision requires infrastructure capable of connecting fragmented advertising systems.

This is where Magnite sees an opportunity.

Rather than building a standalone AI assistant, the company is focusing on becoming the transaction layer where autonomous advertising agents interact with inventory, audiences, and media owners.

That strategy mirrors a broader enterprise trend in which infrastructure providers are increasingly positioning themselves as platforms for AI agents rather than destinations for human users.

The Bigger Picture

Magnite Orchestration represents more than another AI feature rollout.

It signals a potential shift in how advertising transactions are executed.

The first generation of programmatic advertising automated bidding. The next generation may automate decision-making itself.

If AI agents become responsible for planning campaigns, selecting audiences, negotiating inventory, generating creative assets, and optimizing performance, the platforms that facilitate those interactions could become some of the most important infrastructure providers in digital advertising.

For Magnite, the launch is a strategic bet that the future of media buying will be increasingly machine-driven, interconnected, and agent-powered.

Whether the industry is ready for fully autonomous advertising remains an open question. But one thing is becoming clear: AI agents are rapidly moving from supporting advertising workflows to actively participating in them

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As advertisers continue shifting budgets into podcasts, music streaming, and other listening-first experiences, ensuring brand safety in audio environments has become a growing challenge.

DoubleVerify is looking to address that gap.

The digital media verification company has launched AI-powered brand suitability reporting for YouTube Audio Ads, extending its post-bid measurement capabilities to one of the fastest-growing segments of digital advertising. The move gives advertisers greater visibility into the audio content surrounding their ads, helping them evaluate whether placements align with brand standards and campaign objectives.

The announcement reflects a broader industry trend as marketers increasingly diversify media investments beyond traditional display and video channels. While audio advertising has gained momentum thanks to streaming services, podcasts, and creator-driven content, measurement and suitability controls have often lagged behind more mature advertising formats.

DoubleVerify is betting that advertisers want the same safeguards in audio environments that they already expect from video and display campaigns.

Why Audio Advertising Is Creating New Challenges

Audio has become an increasingly important part of modern media strategies.

Platforms such as YouTube Music, podcast networks, music streaming services, and creator-led audio content have opened new opportunities for brands to engage audiences during moments when visual attention is limited.

YouTube Audio Ads were specifically designed for these listening-first scenarios.

Rather than relying on traditional video experiences, the format combines audio messaging with lightweight visuals, typically static images or simple animations, allowing advertisers to reach audiences consuming music, podcasts, and other audio-centric content.

But as spending increases, so do concerns around transparency.

Unlike video advertising, where visual context can often be assessed quickly, audio introduces additional complexity. Advertisers need confidence that spoken language, themes, sentiment, and contextual discussions surrounding an ad align with their brand values.

Without reliable measurement tools, marketers may have limited visibility into where campaigns are actually appearing.

That concern has become more significant as brands face growing pressure to protect reputation while maintaining scale across increasingly fragmented media environments.

How DoubleVerify's AI System Works

The new reporting capability is powered by DoubleVerify's Universal Content Intelligence™, the company's AI-driven content classification engine.

The platform analyzes multiple forms of media signals simultaneously, including:

• Audio content

• Video elements

• Text-based context

• Visual imagery

• Metadata signals

For YouTube Audio Ads specifically, the system uses AI models to evaluate spoken language, contextual themes, sentiment, and other audio-based indicators that may affect brand suitability.

This multi-signal approach is designed to identify nuanced content categories that traditional keyword-based systems often miss.

For example, a conversation discussing a sensitive topic in a neutral or educational context may require different classification than content presenting the same topic in a harmful or controversial way.

The ability to distinguish those nuances has become increasingly important as advertisers seek greater precision in content evaluation.

Rather than applying broad exclusion lists, modern suitability frameworks focus on contextual understanding and risk assessment.

DoubleVerify's AI-driven analysis is intended to provide that deeper level of classification.

Extending Consistency Across Channels

One of the primary benefits for advertisers is consistency.

Many large brands already use DoubleVerify's verification and suitability tools across display, video, connected TV, and social media campaigns. Extending similar standards into audio environments allows marketers to apply a more unified approach across media channels.

This is particularly relevant as omnichannel marketing strategies become the norm.

Brands increasingly want a single framework for evaluating content quality regardless of whether an ad appears alongside a YouTube video, a podcast episode, a music stream, or a social media post.

By bringing post-bid suitability measurement to YouTube Audio Ads, DoubleVerify aims to close a measurement gap that has existed in audio-focused campaigns.

The launch also aligns with growing demand for accountability across every stage of the advertising supply chain.

Advertisers are no longer satisfied with simply reaching audiences. They want assurance that placements contribute positively to brand perception and campaign performance.

Part of a Broader AI Strategy

The announcement is also the latest example of DoubleVerify expanding its use of artificial intelligence across media quality and verification products.

The company recently introduced DV AI SlopStopper™ for Social, a solution designed to identify and help advertisers avoid low-quality AI-generated content appearing across social platforms.

Together, the launches signal a broader focus on addressing emerging challenges created by the rapid expansion of AI-generated and user-generated content.

As digital ecosystems become increasingly saturated with synthetic media, misinformation risks, and variable content quality, advertisers are placing greater emphasis on verification technologies that can provide trustworthy assessments at scale.

This trend is creating new opportunities for companies operating in the media measurement and ad verification sector.

Platforms that can analyze vast amounts of content across multiple formats—text, video, audio, and images—are becoming increasingly valuable as advertising environments grow more complex.

The Bigger Picture

DoubleVerify's expansion into YouTube Audio Ads highlights an important evolution in digital advertising.

Audio is no longer a niche channel. It has become a core component of modern media strategies, attracting dedicated budgets from brands seeking highly engaged audiences across podcasts, music streaming, and creator content.

However, as investment grows, so does the need for accountability.

Advertisers increasingly expect the same transparency, suitability controls, and measurement standards across audio that they already receive in video, display, and social environments.

By extending AI-powered brand suitability reporting to YouTube Audio Ads, DoubleVerify is helping close that gap.

The move underscores a larger industry reality: as media channels multiply and content formats diversify, AI-powered verification is becoming a critical layer of the advertising ecosystem.

For brands navigating an increasingly fragmented digital landscape, understanding not just who they reach—but the context in which they reach them—may become one of the most important performance metrics of all

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InterDigital has secured a significant win in its effort to expand beyond traditional device licensing and into the rapidly growing video streaming market.

The wireless, video, and AI technology research company announced a patent licensing agreement with Amazon that covers a broad range of Amazon products and services, including Prime Video. The deal also brings an end to ongoing legal disputes between the two companies, with both parties agreeing to resolve pending litigation and move remaining issues into binding arbitration.

While financial details were not disclosed, the agreement represents a strategic milestone for InterDigital as it seeks to monetize its growing portfolio of video technologies across streaming platforms in addition to smartphones, consumer electronics, and connected devices.

For Amazon, the deal removes a potential source of legal uncertainty around intellectual property rights tied to its streaming and hardware businesses.

A New Front in Patent Licensing

InterDigital has long been known for developing and licensing technologies that underpin wireless communications, video compression, and connected device ecosystems.

Historically, much of the company's licensing revenue has come from smartphone manufacturers and telecommunications companies that rely on patented technologies related to wireless standards.

However, the rapid growth of streaming media has created a new opportunity.

Video services increasingly depend on advanced compression, delivery, optimization, and playback technologies that are often protected by extensive patent portfolios. As global streaming consumption continues to surge, technology owners are looking for new ways to generate licensing revenue from digital content platforms.

The agreement with Amazon signals that InterDigital is making meaningful progress in that strategy.

According to Julia Mattis, InterDigital's Chief Licensing Officer, the deal represents an important step toward expanding the company's presence in video streaming services licensing.

By securing coverage for Prime Video alongside Amazon's broader device ecosystem, InterDigital gains recognition for technologies that increasingly play a role in both content delivery and user experiences.

Why Prime Video Matters

Prime Video is one of the world's largest streaming platforms, competing directly with major services such as Netflix, Disney+, and Max.

As streaming platforms continue investing in higher-resolution video, adaptive streaming, personalized content experiences, and AI-powered optimization, the importance of foundational video technologies continues to increase.

Patent licensing around video delivery has become a critical issue for technology providers seeking compensation for innovations that power modern streaming experiences.

InterDigital's agreement with Amazon suggests that video services are becoming a larger focus within the broader intellectual property landscape.

The deal also reflects a growing trend in which technology licensing companies pursue agreements that cover both devices and digital services under a single framework.

That approach can simplify negotiations while providing broader protection for technology usage across interconnected ecosystems.

Ending Litigation, Moving to Arbitration

An equally important aspect of the announcement is the resolution of ongoing legal disputes between the companies.

Patent litigation involving major technology firms can often stretch across multiple jurisdictions and continue for years before reaching a resolution.

Rather than continuing courtroom battles, Amazon and InterDigital have agreed to settle pending cases and use binding arbitration to determine the final terms of the agreement.

Arbitration has become an increasingly common mechanism for resolving complex patent licensing disputes because it can offer greater efficiency, confidentiality, and predictability compared to traditional litigation.

For both companies, the shift allows resources to move away from legal proceedings and toward business operations.

While arbitration will ultimately determine final commercial terms, the agreement itself indicates that both parties have already established a framework for cooperation.

Part of a Larger Licensing Strategy

The Amazon deal arrives as intellectual property licensing continues to evolve alongside changes in digital media consumption.

Companies that once focused primarily on hardware licensing are increasingly targeting software platforms, cloud services, streaming providers, and AI-powered applications.

InterDigital's portfolio spans wireless communications, video technologies, and artificial intelligence, placing the company in a position to participate in several fast-growing technology markets simultaneously.

As streaming services compete to deliver higher-quality content while controlling bandwidth costs, technologies related to video compression and efficient content delivery are becoming increasingly valuable.

At the same time, AI-driven enhancements to media experiences are creating new licensing opportunities across content discovery, personalization, optimization, and delivery infrastructure.

The Amazon agreement may therefore represent more than a single licensing win. It could signal the beginning of a broader effort to secure agreements with additional streaming and digital media providers.

The Bigger Picture

The deal underscores a broader reality in today's technology landscape: the battle for competitive advantage increasingly extends beyond products and platforms to the intellectual property powering them.

As streaming services continue to grow and video consumption becomes more central to digital life, patent portfolios covering video technologies are likely to attract greater attention from both licensors and service providers.

For InterDigital, the agreement validates its strategy of expanding beyond traditional wireless licensing into adjacent markets such as streaming media and AI-enabled services.

For Amazon, it removes a legal overhang while ensuring continued access to technologies that support a growing ecosystem of devices and content services.

With litigation resolved and arbitration set to finalize the remaining details, both companies appear focused on moving forward rather than continuing costly courtroom disputes.

In an industry where intellectual property battles often dominate headlines, that outcome may prove valuable for everyone involved

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As audio advertising becomes an increasingly important part of media strategies, advertisers are demanding the same transparency and brand protection tools they rely on across video, display, and social channels.

Integral Ad Science (IAS) is responding to that demand.

The digital media measurement company has expanded its AI-powered Total Media Quality (TMQ) platform to support YouTube Audio Ads, giving advertisers independent brand safety and suitability measurement for campaigns running in YouTube's growing audio ecosystem.

The move comes as brands continue shifting advertising budgets toward podcasts, music streaming, and listening-first experiences, where audience engagement is high but content visibility can be harder to assess.

By extending third-party verification into audio environments, IAS aims to provide marketers with greater confidence that their ads are appearing alongside content aligned with their brand standards.

Why Audio Advertising Is Becoming a Bigger Priority

Audio has quietly become one of the fastest-growing channels in digital advertising.

Consumers increasingly spend time with podcasts, music platforms, creator-led audio content, and background listening experiences that fit naturally into daily routines. As a result, marketers are dedicating larger portions of their media budgets to audio-focused campaigns.

The opportunity is substantial.

According to eMarketer projections cited by IAS, U.S. adults are expected to spend an average of one hour and 26 minutes per day consuming digital audio content in 2026.

YouTube has emerged as a major player in that market.

While traditionally viewed as a video platform, YouTube has become a dominant destination for podcast consumption and audio-based content. Google reports that the platform now serves more than one billion monthly active podcast users across its ecosystem, making it the largest podcast platform in the United States.

That scale is attracting advertiser attention.

However, as audio inventory grows, marketers face a familiar challenge: understanding the content surrounding their advertisements and ensuring it aligns with brand suitability requirements.

Unlike visual media, audio environments often require deeper contextual analysis to determine whether content is appropriate for a particular advertiser.

How IAS Is Using AI for Audio Measurement

The expansion is powered by IAS's Total Media Quality platform, which uses artificial intelligence to analyze content and provide brand safety and suitability insights across digital media environments.

With YouTube Audio Ads, the company is applying the same measurement framework advertisers already use for YouTube Shorts and long-form video campaigns.

The goal is consistency.

Rather than evaluating different formats through separate reporting systems, marketers can access a unified view of media quality across YouTube's expanding content ecosystem.

IAS says the solution delivers content-level transparency that helps advertisers understand exactly where their campaigns are appearing and whether those environments meet predefined safety and suitability standards.

The platform provides:

• AI-driven brand safety and suitability measurement

• Unified reporting across YouTube formats

• Content-level transparency and contextual insights

• Global availability across all supported regions

• Industry-aligned reporting standards

This approach allows advertisers to apply consistent media quality benchmarks regardless of whether an ad appears alongside a short-form video, a traditional YouTube video, or audio-centric content.

The Growing Battle for Audio Ad Verification

IAS's announcement comes shortly after several major ad verification providers began expanding their capabilities into audio advertising.

As marketers invest more heavily in podcasts, music streaming, and listening-first experiences, the market for audio verification and suitability measurement is becoming increasingly competitive.

Historically, brand safety tools were primarily focused on display and video advertising, where visual content could be more easily classified and analyzed.

Audio presents a different challenge.

Understanding context often requires advanced language processing, sentiment analysis, and interpretation of spoken content rather than visual signals alone.

That complexity is driving greater adoption of AI-powered classification systems capable of evaluating content across multiple formats.

For advertisers, the objective remains the same: ensuring media investments appear in environments that support campaign goals while protecting brand reputation.

As digital media consumption becomes more fragmented, achieving that level of consistency across channels is becoming increasingly difficult—and increasingly important.

Building on a Broader YouTube Strategy

The YouTube Audio Ads expansion is the latest step in IAS's broader partnership with YouTube and Google.

Over the past two years, IAS has steadily expanded its measurement and optimization offerings across Google's advertising ecosystem.

In 2024, the company introduced IAS Optimization for YouTube, enabling advertisers to improve contextual suitability through enhanced pre-screen controls.

IAS has also expanded verification capabilities across Google's Search Partner Network and achieved Media Rating Council (MRC) accreditation for integrated third-party YouTube video viewability reporting.

Together, these initiatives reflect a broader trend toward independent measurement and verification across major advertising platforms.

As brands face increasing pressure to demonstrate media quality, campaign effectiveness, and return on investment, third-party validation is becoming a critical component of advertising strategies.

The Bigger Picture

IAS's expansion into YouTube Audio Ads highlights an important shift in digital advertising.

Audio is no longer treated as a niche media channel. It has become a mainstream advertising environment attracting significant brand investment and audience attention.

As that transition accelerates, advertisers expect the same level of accountability, transparency, and media quality controls they already receive across video and display campaigns.

Verification providers are responding by extending AI-driven measurement frameworks into audio ecosystems where traditional monitoring approaches are less effective.

For marketers, the goal is straightforward: reach highly engaged audiences without sacrificing visibility into where campaigns appear.

For companies like IAS, the opportunity lies in becoming the trusted layer that helps advertisers navigate an increasingly complex media landscape.

As audio consumption continues to grow and YouTube strengthens its position as a podcast and listening platform, tools that provide independent measurement and contextual intelligence are likely to become essential components of modern advertising operations

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Quantum is betting that clearer storytelling will be just as important as storage innovation in the age of exploding data volumes.

The company has appointed Greg Knieriemen as Vice President of Marketing and Senior Technology Advocate, tasking the longtime enterprise technology executive with leading global marketing efforts, strengthening customer engagement, and sharpening how Quantum communicates its value in an increasingly crowded data infrastructure market.

The move comes as organizations face mounting challenges around data growth, AI adoption, compliance requirements, and rising infrastructure costs. While storage vendors have traditionally competed on performance and capacity, the conversation is increasingly shifting toward efficiency, sustainability, and intelligent data management.

Quantum appears to be positioning its latest leadership hire squarely around that transition.

A Marketing Leader With Deep Enterprise Technology Roots

Knieriemen brings more than two decades of experience spanning enterprise storage, infrastructure, hybrid cloud, sales enablement, and technology evangelism.

Most recently, he served at Hitachi Vantara, where he led Global Sales Enablement initiatives focused on improving seller productivity, aligning go-to-market messaging, and incorporating AI-powered tools into sales operations.

His background extends beyond traditional marketing leadership.

Throughout his career, Knieriemen has worked across product marketing, analyst relations, public relations, channel marketing, and technical advocacy roles, giving him a rare blend of business and technical expertise that many enterprise technology companies increasingly value.

That combination appears to have been a key factor in Quantum's decision.

According to Quantum Chief Revenue Officer Tony Craythorne, the company views Knieriemen as someone capable of translating complex technologies into business outcomes that resonate with both customers and partners.

For enterprise infrastructure vendors, that ability is becoming increasingly important.

As technologies such as AI, hybrid cloud, data lakes, and large-scale storage architectures become more sophisticated, buyers often care less about technical specifications and more about operational impact, cost efficiency, and business value.

Quantum's Growing Focus on Data Lifecycle Management

The appointment reflects a broader shift in how data infrastructure providers are positioning themselves.

Historically, storage companies focused heavily on hardware performance, scalability, and reliability.

Today, the challenge is far more complex.

Organizations are generating unprecedented volumes of structured and unstructured data through AI applications, video content, analytics platforms, IoT devices, and digital business operations.

Managing that growth requires more than simply adding storage capacity.

Companies increasingly need strategies that determine:

• Where data should reside

• When it should move between storage tiers

• How long it should be retained

• How to balance cost and performance

• How to reduce energy consumption

• How to meet governance and compliance requirements

Quantum's messaging increasingly centers on helping organizations place the right data in the right location at the right time.

That concept has become a major theme across the broader data management industry as enterprises seek ways to control infrastructure costs while maintaining accessibility and performance.

Knieriemen's role will involve helping communicate that value proposition to customers, partners, and industry stakeholders.

Why Technology Advocacy Matters Again

One notable aspect of the appointment is Quantum's emphasis on technology advocacy.

While product marketing and demand generation remain core responsibilities, the company is also positioning Knieriemen as a public-facing technology evangelist.

That strategy reflects a growing trend among enterprise technology vendors.

As markets become more crowded and product differentiation becomes harder to communicate, companies are increasingly investing in technical advocates who can educate customers, engage industry communities, and shape broader market conversations.

Knieriemen already has significant experience in that arena.

He is perhaps best known in industry circles as the founder and co-host of Speaking in Tech, one of the earliest enterprise technology podcasts. The show built a sizable audience and became a well-known platform for discussions around infrastructure, cloud computing, storage, and emerging technologies.

That experience could prove valuable as Quantum seeks to strengthen its visibility among enterprise buyers navigating increasingly complex technology decisions.

A Strong Channel Background

Another element that stands out is Knieriemen's extensive channel marketing experience.

Enterprise technology companies continue to rely heavily on channel partners, resellers, system integrators, and managed service providers to drive growth and customer engagement.

Before joining Hitachi, Knieriemen served as Vice President of Marketing at Chi Corporation, a long-time Quantum channel partner.

That experience gives him firsthand insight into how partners position enterprise technologies, communicate value to customers, and drive adoption in competitive markets.

As technology vendors increasingly pursue ecosystem-led growth strategies, leaders who understand both vendor and partner perspectives are becoming increasingly valuable.

The Bigger Picture

Quantum's appointment of Knieriemen reflects more than a routine executive hire.

It highlights how enterprise infrastructure companies are adapting to changing market dynamics.

Data growth is accelerating, AI workloads are placing new demands on storage architectures, and organizations are under pressure to balance performance, sustainability, compliance, and cost efficiency simultaneously.

In that environment, technology vendors must do more than build capable products. They must clearly articulate how those products solve real-world business challenges.

That is where Quantum appears to see an opportunity.

By bringing in a leader with experience across enterprise storage, marketing, sales enablement, channel strategy, and technology evangelism, the company is investing not only in brand awareness but in its broader go-to-market strategy.

As the data management market becomes increasingly competitive, the ability to translate technical complexity into customer value may prove just as important as the technology itself.

For Quantum, Knieriemen's appointment signals a renewed focus on ensuring that message is heard

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