– Effective May 1, marking a new era of AI-powered automation across industries – AgentOS expands to become a core operational infrastructure

Enhans (CEO Seung-hyun Lee), an Agentic AI company for the enterprise, announced that its core service, CommerceOS, will officially be renamed AgentOS starting May 1.

AgentOS is a corporate AI agent solution that autonomously generates workflows tailored to specific enterprise environments. It connects enterprise data, ontology, agents, workflows, views, actions, and web platforms in one operating system to complete tasks end-to-end. Previously, enterprises faced limitations in directly adopting AI into their operations due to the difficulty of integrating their unique knowledge bases and specific needs. Enhans solves this by applying ontology technology to create enterprise-specific agentic AI. This approach enables AgentOS to function as an active operational agent in real business settings.

With AgentOS, enterprises can build custom agents through natural language. The platform automates complex decision-making in real time through a multi-agent structure where specialized agents collaborate based on each enterprise’s proprietary data. It comprehensively manages overall corporate operations. This includes monitoring market data and internal workflows, analyzing trends in real time, optimizing resources, and formulating and executing strategies.

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As Enhans’s enterprise agentic AI technology proves effective across sectors, organizations across industries are actively seeking to adopt the solution. Enhans is proactively responding to this market demand, driving tangible business results such as revenue growth, cost optimization, and the discovery of new business opportunities.

The rebrand signifies a major expansion of the company’s AI agent technology across all industries, moving well beyond the commerce sector. The timing of this change is equally intentional. Choosing May 1 as the official date carries profound symbolic meaning.

Just as the historic 1886 labor movement sought to give people their time back through the eight-hour workday, AgentOS aims to free human workers from repetitive tasks. This allows professionals to reclaim their time and focus on strategic thinking and creativity. AgentOS embodies a vision where enterprise AI inherits the fundamental values from 140 years ago to reshape modern work practices.

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“The transition to AgentOS demonstrates that our technology has broken down the boundaries of specific industries to become a full-scale operational infrastructure,” said Seung-hyun Lee, CEO of Enhans. “We are committed to creating a new paradigm where AgentOS streamlines operations and significantly improves performance for businesses worldwide.”

Latest release introduces locally hosted AI, automated identity management, and continued accessibility improvements to help organizations automate business processes without compromising control.

Acquisition gives enterprise teams a governed path from AI experimentation to production without disrupting existing workflows or infrastructure

Those distinctions will become increasingly important. Done well, regulation can protect users, foster competition and sustain innovation. Done poorly, it risks entrenching the dominance of the largest technology platforms. In many ways, those same platforms are already best positioned to shape and absorb regulatory change, potentially leaving everyone else at a disadvantage.

The sheer momentum of AI to date makes it easy to feel helpless in the face of such a technological revolution. How can any of us hope to help shape and guide the ways in which AI is to unfold?

Fortunately, the digital media industry has faced a similar inflection point before in its recent history. The journey towards cookie deprecation offers a valuable lesson, and perhaps a blueprint, for what comes next.

The Privacy Sandbox experience

When browsers began phasing out third-party cookies, it triggered a wave of uncertainty and, in some cases, outright panic across the digital ecosystem. Advertisers, publishers and ad tech vendors all faced the challenge of maintaining addressability and monetisation while ensuring user privacy. Google’s Privacy Sandbox initiative was the most notable attempt to strike that balance.

The Privacy Sandbox was not perfect, but its intent is instructive. Rather than abruptly removing a foundational technology and leaving the ecosystem to adapt overnight, it introduced a standards-based framework designed to evolve over time. It sought input from across the industry (including publishers, advertisers, developers and regulators) and aimed to create privacy-preserving alternatives that could support the economic model of the open web.

One could argue that Google could deprecate cookies, as Apple did, and introduce its own unique way of targeting users in Chrome. Instead, it opened up the discussion with the ecosystem around collaboration and iteration. This created a space, however imperfect, for broader participation and conversation, demonstrating that large-scale ecosystem change can be coordinated by consensus rather than imposed.

The cookie deprecation process made it clear that simply “switching off” a core capability at scale is not viable. Sudden changes risk destabilising publishers who rely on advertising revenue, limiting the ability of smaller tech providers to compete, and forcing advertisers into narrower, less transparent buying environments. Meaningful progress required frameworks that could be refined in real time, informed by data and shaped by those operating across the ecosystem, not only those at the top of it.

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Regulating agentic AI

Today, the digital media industry faces a parallel moment with the rise of agentic AI. These systems are increasingly acting as intermediaries between users and the digital world. It’s shaping what content is discovered, which products are surfaced, and how decisions are made. In effect, they are becoming gatekeepers to information, commerce, and attention.

As control over these systems concentrates in the hands of a few large players, questions around transparency, fairness and access become more urgent. Regulation is clearly necessary, but it must be approached with care.

A “sandbox approach” to AI regulation, at its core, means developing standards collaboratively across the industry, rather than imposing rigid rules from the top down. It also necessitates creating environments where new approaches can be tested, evaluated and iterated before being scaled. Finally, it requires that any regulation evolves alongside the technology it seeks to govern.

Large technology platforms have a critical role to play in this process. As with the Privacy Sandbox, companies like Google have the scale, data and infrastructure to help develop and test new approaches. But with that role comes responsibility. Their contribution should be to support industry-wide solutions, not to define the rules in isolation.

Collaboration, transparency and iteration

There are already signs that the stakes are rising. As AI systems become more embedded in advertising, commerce, and content discovery, brands need to collaborate effectively with chat interfaces, which act as intermediaries, and with end users. Without clear and collaborative frameworks, the risk is that regulation, however well-intentioned, ends up reinforcing the very dynamics it seeks to address.

The transition from cookies to privacy-first alternatives showed that the industry is capable of navigating complex change. It also showed that the process matters as much as the outcome. As AI becomes the primary interface for digital decision-making, those same principles must guide the next phase of regulation. Collaboration, transparency, and iteration are not just desirable; they are essential.

A sandbox approach offers a way to balance innovation with accountability, and competition with control. The window to get this right is narrow; fortunately, the blueprint already exists.

About PrimeAudience

PrimeAudience (an RTB House company) is a, AI-driven, privacy-focused adtech platform designed to boost client acquisition and enhance targeting. It uses Generative AI to create custom audiences, reducing ad costs by up to 80% and providing up to 40-60% identity resolution of website visitors without relying solely on third-party cookies

AI agents can now connect to data sources, build pipelines, manage credentials, and run production data work end-to-end — with humans watching, not driving

Datris today announced a major expansion of its agent-native data platform that makes AI agents true first-class operators of data infrastructure. Agents working through Datris can now connect to and continuously pull from data sources — S3, databases, internal APIs, and enterprise systems like Workday, Salesforce, and ServiceNow — build pipelines from scratch, generate validation rules and transformations in plain English, manage their own credentials, and have every action observed in real time, without a human writing glue code or sitting in front of a console.

“The data industry spent twenty years building tools for human engineers, and the last two trying to retrofit them for AI,” said Todd Fearn, founder. “We started over — without a human in the loop.”

— Todd Fearn

While the rest of the industry has spent two years bolting chat interfaces onto traditional data tools, Datris took the opposite approach: it rebuilt the data platform around the AI agent. Every capability is exposed through Model Context Protocol (MCP).

What’s new

Agents stand up their own data feeds. Datris introduces “taps” — recurring or on-demand pulls from a source that land data where the rest of the platform can use it. An agent describes the source in plain English; the platform owns the connection, the schedule, and the execution.

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Agents build pipelines, not just query them. An agent describes the work it wants done — ingest a CSV every hour, drop malformed rows, normalize timezones, land it in a warehouse — and the platform generates the schema, writes the data quality rules, produces the transformation logic, and stands up the pipeline as a single atomic operation.

Agents own their own credentials. An agent can request, store, rotate, and delete the API keys it depends on, scoped to credentials it created. Human-owned credentials remain protected and untouchable by agents — the platform enforces the line.

Every agent action is observed. A live operations view shows which agent invoked which capability, against which pipeline, with what result, as it happens. When something goes wrong, the platform returns errors in language the agent can act on, not a stack trace a human has to translate.

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Open source, self-hostable

Datris is open source under AGPL-3.0. The full platform is on GitHub at github.com/datris/datris-platform-oss and runs on a single machine with Docker. Teams self-host the stack, inspect every line of code that touches their data, and extend the platform with their own MCP tools. There is no enterprise edition, no feature gating, and no telemetry. A hosted version is available at datris.ai for teams that prefer not to run the infrastructure themselves.

As 44% of users worry about AI acting without approval, Shift AI delivers context-aware intelligence, on the user’s terms

Shift, the world’s first fully customizable browser, announced the launch of Shift AI, a context-aware, privacy-focused AI experience built directly into the browser. Designed to reduce friction across workflows, Shift AI delivers real-time intelligence without forcing users to sacrifice control, privacy or choice.

As AI features rapidly proliferate across browsers, many are being introduced as defaults without clear controls or transparency. Shift AI is optional by design, allowing users to decide when, how, and if AI is part of their workflow.

“AI shouldn’t live in another tab. It should live where you work and it should work on your terms,” said Michael Foucher, Vice President of Product and Customer Success at Shift. “Shift AI is designed to reduce the friction of everyday work while ensuring users stay in control of their data, their experience, and their workflow.”

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AI That Works With You – Not Around You

Shift AI is an adaptive, context-aware system embedded directly into the browser, while remaining fully under user control. Shift AI introduces capabilities designed to streamline how users navigate tasks, tools, and information:

• Context-Aware AI — Understands active tabs and page content to deliver relevant answers without manual prompting
• Intelligent Omnibox — Seamlessly routes queries between search and AI automatically, reducing friction and decision fatigue
• Workflow Continuity — Keeps related tasks connected, eliminating disruptive tab switching
• Privacy- First Architecture — requests are proxied through Cloudflare’s Privacy Proxy and authenticated via the Privacy Pass protocol to protect users from persistent tracking or exposing their identity

AI Adoption Is Growing—But Control Is the Missing Piece

Findings from Shift’s 2026 AI Consumer Insights Survey of more than 1,400 adults highlight a growing disconnect between AI usage and user trust:

• 32% of users engage with AI daily
• 53% say it improves their experience
• 44% worry AI could act without their approval

The data reveals a clear tension: while AI adoption continues to accelerate, users are increasingly concerned about control, transparency and data privacy.

“Users aren’t rejecting AI, they just want control,” Foucher added. “The next phase of AI is about putting the user back in charge.”

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Purpose-Built, Customizable, and User-Controlled
Shift AI is designed for professionals who rely on the browser as their primary workspace, including developers, founders, creatives, consultants, tech professionals and multi-tasking consumers.

Built into the browser architecture, Shift AI enables highly customizable experiences and deeper integrations across tabs, apps, and workflows. This flexibility is key, as 51% of hybrid workers and tech professionals want greater control over how AI operates, reinforcing the need for user-driven experiences.

Privacy and Control by Design

To support a more transparent and user-controlled AI experience, Shift partnered with Cloudflare, to build an architecture that protects users’ privacy without sacrificing performance. By proxying requests through Cloudflare’s Privacy Proxy, users’ identities are separated from their AI queries. Users maintain full control over AI functionality at all times, with the ability to opt in, customize features, or disable them entirely.

Reimagining the Browser for the AI Era

With Shift AI, the browser evolves from a passive interface into an intelligent, customizable workspace—bringing together apps, profiles, and workflows into a single, unified experience designed to reduce digital overload and improve focus.

At Sensors Converge 2026, Azilen will highlight Headless IoT as the next step for autonomous, action-driven IoT systems.

Azilen Technologies, an enterprise AI development company, will demonstrate its Headless IoT Intelligence Stack at Sensors Converge 2026, scheduled for May 5–7, 2026, at the Santa Clara Convention Center. The company brings a system-first approach that connects sensing, data, and agent-driven action into one unified architecture.

Traditional IoT stops at visibility. With Headless IoT, we enable systems to take ownership of monitoring and routine decisions with speed, consistency, and control.”

— Tarak Joshi, VP – Sales, Azilen Technologies

At Booth #1152, visitors will experience how sensor-driven environments can operate with minimal human dependency – where systems sense, analyze, and act in real time. Built on Azilen’s expertise in IoT development services, the demonstration will highlight how organizations can transition from dashboard-based monitoring to system-led execution across edge, platform, and cloud environments.

In many industrial and connected environments, sensors continuously generate large volumes of data. However, most systems still rely on dashboards and human intervention to interpret this data and initiate actions. This creates delays in response, introduces inefficiencies, and increases dependency on manual monitoring, particularly in scenarios where real-time decisions are critical or human presence is limited.

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Azilen’s Headless IoT approach addresses this gap by enabling systems to operate independently of continuous human oversight. It allows systems to monitor environments, analyze incoming data, and execute actions based on defined logic and system intelligence. This shifts the operating model from human-led monitoring to system-driven execution, where human involvement is primarily focused on exceptions and higher-level control.

The Headless IoT Intelligence Stack is structured around a unified flow that connects sensing, connectivity, data processing, and action. Systems capture data from distributed sensors, ensure seamless data flow across edge and cloud environments, process and interpret data in real time, and use agent-driven decision systems to trigger actions. This closed-loop approach enables continuous operation without delays between signal detection and system response.

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Azilen brings strong engineering expertise across the IoT and AI stack, with capabilities in sensor data engineering, real-time data processing, and scalable edge-to-cloud architectures. The company also delivers AI Agent development services to build agentic systems that enable automated decision-making and action. With experience across industrial, healthcare, and smart infrastructure domains, Azilen delivers systems designed to perform reliably under real-world constraints and scale across complex environments.

Azilen positions Headless IoT as the next step in the evolution of IoT systems. While traditional IoT focuses on connectivity and visibility, Headless IoT focuses on enabling systems to act. By embedding intelligence and decision-making within the system, organizations can reduce response time, improve operational efficiency, and fully utilize the potential of sensor-driven environments.

Azilen Technologies will present its Headless IoT Intelligence Stack at Booth #1152 during Sensors Converge 2026, taking place from May 5 to May 7 at the Santa Clara Convention Center. Attendees will have the opportunity to explore how existing sensor ecosystems can evolve into systems capable of real-time, autonomous action.

__________

Hi Jana, take us through your time in martech and your role at Cognitiv?

I’ve spent more than two decades at the forefront of advertising innovation. First helping lead the adoption of programmatic across EMEA during its early emergence, launching programmatic at Spotify, to now helping marketers access AI-driven solutions to scale growth. I joined Cognitiv 8 years ago, at the time we were an early player in the deep learning space, with fewer than 10 employees. Joining a start-up is always a gamble, but I felt confident in the technology and the founding team and saw it as a rare opportunity to learn about AI.

Today as SVP of Partnerships, I focus on redefining how brands and media companies leverage deep learning AI to drive performance. In a media landscape that’s more complex than ever, that means building strategic partnerships that help publishers unlock new revenue streams while enabling brands to engage consumers in more meaningful, data-driven ways. I have developed strategic partnerships with major SSPs and DSPs to bring the industry’s most advanced AI-driven curation to media buyers.

We’d love to learn more about your new enhancement, AudienceGPT. Why should marketers pay attention to it?

AudienceGPT is a fundamental shift from reactive audience targeting to predictive, intelligence-driven marketing.

Traditionally, audience segmentation was manual, time consuming, static, and relied on outdated signals like clicks or page visits that didn’t tell you much about the actual stage of the journey a consumer was in.

AudienceGPT solves this by using Cognitiv’s deep learning advertising platform to develop synthetic consumer journey profiles that can then be found programmatically. The result is a more adaptive, predictive approach to audience strategy that aligns media delivery with true consumer intent. Audiences can be activated across web, CTV, social, and audio, meeting advertisers where they are.

Modern marketers manage different types of data and workflows today. What top best practices come to mind for those looking to optimize how they clean and use data to power better outcomes and customer journeys?

During my time at Cognitiv, I’ve evaluated probably 100 data providers across contextual, attention, measurement, and audience segments, so I’ve seen a wide range in data quality and approaches.

A few best practices really stand out. First is understanding the origin of the data, whether it’s deterministic or modeled. Deterministic data, especially in its raw form, tends to be more reliable and transparent, whereas modeled data can introduce assumptions that aren’t always clear or consistent.

Second is freshness and relevance. Marketers often overlook how frequently data is refreshed. An audience labeled as a “travel intender,” for example, is only as valuable as the recency and signal behind that classification. You have to ask: what behaviors actually qualified this user, and how recent were they?

Finally, validation is critical. At Cognitiv, we’re fortunate to test data directly by running it through our models offline to see whether it actually improves predictive accuracy. That kind of rigorous testing helps separate data that sounds good in theory from data that truly drives performance.

Ultimately, the best outcomes come from combining transparency, recency, and real-world validation, rather than relying on labels or assumptions alone.

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What’s the most exciting thing about how AI is leading to a shift in marketing processes and standards as well as a shift within marketing teams in terms of how teams are structured today?

AI is reshaping marketing in a way that feels very similar to the early days of programmatic, but at a much faster pace.

From a team perspective, the traditional silos between media, data, and analytics are starting to break down. We’re seeing hybrid roles emerge, people who understand both the strategic and technical sides of marketing, and are usually proficient in deploying and working with AI.

From a process standpoint, there’s a tendency to think about AI primarily as workflow automation. And while it can help with that, the bigger opportunity lies in real-time prediction and decisioning. That’s where the biggest performance gains will come from.

Five thoughts on the future of AI and martech?

1. Audience targeting shifts towards moments of intent: The combination of contextual signals, real-time behavior, and understanding of content will outperform audience segments. This goes beyond assigning someone to a segment, to predicting their likelihood to act in that moment based on live inputs.

2. Data quality becomes the true differentiator: The future will be built on better data—deterministic where possible, transparent in methodology, and validated against outcomes.

3. AI shifts from automation to intelligence: Today, AI in marketing is primarily focused on automating execution, not redefining strategy. The next phase will move beyond efficiency gains to deliver real intelligence—powering better decisions rather than just optimizing the manual levers we’ve relied on.

4. Personalization will scale without manual effort: AI will enable truly individualized experiences without the operational complexity that used to limit scale.

5. CTV Moves from awareness to performance: CTV is a great channel for reach and scale but we’ll increasingly see it used as a medium to drive performance. The ones who win in CTV will go beyond content targeting.

Some top martech innovations and martech innovators that you’d like to shout out to in this conversation?

Two martech innovators I want to shout out are Magnite and Index Exchange – specifically Paul Zovighian, VP, Marketplaces at Index Exchange, and Zach Pucci Global, Enterprise Sales at Magnite. Both are helping push real-time curation forward in a way that’s shifting intelligence to the sell side and accelerating innovation across the ecosystem.

Real-time curation turns live data signals into actionable inputs for AI, allowing for accurate, real-time predictions. This drives improved performance for buyers in the moment, not after the fact.

Cognitiv is a leading advanced performance partner powered by deep learning. Leveraging cutting-edge AI technology and data science since 2015 to more accurately predict consumer behavior and understand nuance, Cognitiv connects brands with their customers in more precise, relevant, impactful moments at scale. Cognitiv’s Deep Learning Advertising Platform provides marketers with unprecedented flexibility, activating as a Dynamic Deal run through the DSP of your choice, as a managed service DSP, or through its industry-first ContextGPT product. Cognitiv is on a mission to bring intelligence to advertising.

About Jana Jakovljevic

Jana, SVP of Partnerships at Cognitiv, brings two decades of experience driving innovation across the advertising industry. Before joining Cognitiv, Jana was the Global Head of Programmatic Solutions at Spotify, where she successfully launched the company’s programmatic arm and pioneered the first Private Marketplace (PMP) for audio ads. At Magnite (formerly Rubicon Project), Jana held various management positions, building out international buy-side partnerships and playing a foundational role in the company’s journey from start-up to IPO. Known for landing at companies that are at the forefront of the media landscape, Jana is now focused on leveraging AI to propel the ad industry forward. Her dedication to disruption and passion for constant improvement make her a key agent of change, unafraid to break the status quo in the name of innovation.

Company is now 100% channel-led under leadership of VP of Worldwide Partners & Alliances Timm Hoyt

Sublime Security, the agentic email security platform, announced the launch of its channel partner program. Now operating as a 100% channel-led company, the program is designed for exceptional partner success in expanding the reach of Sublime’s industry-first approach to email security. This launch represents a significant investment in building a world-class partner ecosystem, including new enablement resources, dedicated channel leadership, robust recurring margins and incentives, and a long-term commitment to selling with and through these trusted advisors.

The program is led by Timm Hoyt, VP of Worldwide Partners & Alliances, a seasoned channel executive whose career spans transformative partner-led growth initiatives at organizations including Sumo Logic, Druva, and PagerDuty. Hoyt has built a reputation for turning partner-convenient models into partner-centric engines, and is applying that expertise to build one of the industry’s most deliberate and relationship-driven programs.

“Email threats are evolving at machine-speed and our customers need a security solution that prioritizes speed, transparency, and organization-specific protection which traditional email security vendors cannot provide,” said Hoyt. “Customers are frustrated with legacy secure email gateways that are complex, expensive, and ineffective against modern phishing, business email compromise (BEC), and socially-engineered attacks. Partners are a critical component in alleviating those burdens, and our channel program provides partners with the education, training, and support they need to exceed customer demands.”

Marketing Technology News: MarTech Interview with Liat Barer, Chief Product Officer @ Odeeo

Core elements of Sublime’s channel partner program include:

• A Partner-First Go-To-Market Motion: Sublime is committing to partner-led opportunities and aligning its sales organization to support, not compete with, partners.
• Expanded Partner Program with Predictable Margins: Prioritizing healthy recurring margins, deal protection, and performance incentives designed to drive partner profitability.
• Dedicated Partner & Alliances Leadership and Resources: Investment in partner sales managers, system engineers, strategic alliances, marketing, and enablement teams focused exclusively on partner success.
• Enhanced Technical Enablement & Training: Enablement resources include accreditation programs, hands-on technical training, sales playbooks, and joint marketing resources to accelerate partner ramp time.
• Co-Marketing & Demand Generation Support: Sublime provides high-impact co-marketing programs, joint campaigns, and content support to help partners drive quality pipeline in a crowded cybersecurity market.

“Email continues to be one of the most common and impactful entry points for cyberattacks, and organizations are looking for more modern approaches to protecting their users and data,” said Mark Thornberry, SVP Partnerships at GuidePoint Security. “We’re excited to work with Sublime Security as they expand their channel program and bring their innovative approach to email security to more organizations. Programs that invest in partner enablement and collaboration ultimately help customers deploy stronger defenses against evolving threats.

“We look for partners who truly understand our differentiators, have a firm grasp of the cybersecurity landscape, have credibility in bringing new vendors to market, and who absolutely believe that incumbent email security providers cannot keep up with modern attacks,” added Hoyt. “We’re committed to working with strategic partners who can grow with us and where we’ll provide mutual benefit.”

This announcement follows a year of tremendous momentum for Sublime Security. In November, the company raised $150M in Series C funding to accelerate its agentic AI capabilities. Between April and September of 2025, Sublime released its first two AI agents: Autonomous Security Analyst (ASA) which investigates and triages threats in seconds, freeing teams from manual review and Autonomous Detection Engineer (ADÉ) which deploys new, tailored defenses to combat novel threats in hours, ending the vendor bottleneck delays that leave organizations exposed.

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Additional perspective from Sublime’s partner ecosystem:

“Security teams are under tremendous pressure to stop increasingly targeted email attacks without adding operational burden. Email is still by far the number one attack vector,” said Alpesh Shah, VP, Security Strategic Alliances at Myriad360. “Sublime Security stands out because it brings automation, transparency, and adaptability to a category long dominated by legacy approaches. Their new channel partner program reinforces a clear commitment to building with partners, and we’re excited to collaborate on bringing this innovative platform to market.”

“As AI accelerates the sophistication of phishing and social‑engineering attacks, organisations need email security that can adapt just as quickly. That’s why our partnership with Sublime Security is so exciting,” said Luke Kiernan, Head of Cyber Security at Bytes Software Services. “The transparency of the platform and the speed at which new protections can be deployed stand out. Their partner‑first approach and investment in enablement make it clear they see partners as a true extension of their business. We’re thrilled to bring this next generation of email security to our customers.”

Voyager visual developer workspace, MCP Server, and AI-optimized SDKs combine to go from first cluster to first query in as little as five minutes

Aerospike, Inc., today unveiled a new unified, AI-native application development experience that makes it simple for both humans and AI coding assistants to confidently prototype, integrate, deploy, and troubleshoot production applications built on Aerospike’s real-time NoSQL database.

The new release is purpose-built for modern engineering, combining AI coding assistants, agents, and developers of varying levels of experience. Optimized for hybrid AI and human collaboration, it includes: Aerospike Voyager, a visual developer workspace; an embedded MCP Server; and updated Aerospike Developer SDKs.

All of these combine into a new Aerospike experience where developers and their agents can explore data visually, query a cluster conversationally, and generate production-ready code in as little as five minutes. Learned patterns and code carry directly from concept to production scale, with no second system to learn, and no architectural rework when load inevitably increases.

AI-powered Development: Easy to Prototype but Fragile and Slow to Scale

The pace of AI-driven development has never been faster. AI assistants now take teams from concept to production in days. But speed alone isn’t enough. Developers still need to understand what AI-generated code is doing before they ship it, diagnose issues when behavior isn’t as expected, and explore the underlying data to make informed decisions about what to build next.

Faster Prototyping, Quicker Troubleshooting, and Confident Deployment

The new Aerospike developer experience is designed for faster prototyping, simpler integration, and quicker troubleshooting for AI apps built on Aerospike. Developers can onboard in minutes, and DevOps and SREs gain a more stable production environment.

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The new experience includes:

• Aerospike Voyager Preview is a visual developer workspace that creates a clear, opinionated onboarding. Available as a desktop application for macOS, Windows, and Linux, it provides one-click cluster connection, pre-built sample data libraries, a hierarchical data browser for visual data exploration, and server-side filtering with guardrails. In early testing, developers using Voyager went from the first cluster to the first filtered query in just five minutes.
• MCP Server that lets AI agents in Claude Code, Codex, Cursor, Gemini CLI, and other tools interact directly with Aerospike clusters to inspect data, query records, explore schemas, and access documentation without leaving development environments. Developers with no Aerospike experience can interact conversationally through agents without context-switching to documentation or CLI tools. The free MCP server is embedded within the new Aerospike Voyager, providing developers with a single download and configuration step.
• Aerospike Developer SDKs deliver native, chainable syntax and a clean separation between application logic and database operations. Developers implement business features using intuitive calls, while database administrators and SREs independently manage timeouts, retry policies, and cluster configuration. Combined with LLM-optimized documentation, the simplified syntax makes it easier for both humans and AI agents to write correct Aerospike code on the first attempt.

“The new Developer SDK makes Aerospike code easier to read and reason about. Voyager simplifies access to data for debugging and query exploration. And the MCP server enables developers to analyze data using the tools and environments they already prefer,” said Srini V. Srinivasan, founder and CTO of Aerospike.

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Aerospike: Best Enterprise NoSQL Database Platform

The new Aerospike experience is the latest in a series of innovations that analysts, media, and customers continue to praise. Recent accolades include:

• Four Data Breakthrough Awards for Graph Database, In-memory Solution, and NoSQL Solution of the Year (twice).
• SiliconANGLE Media’s 2026 Tech Innovation CUBEd Awards for the Most Innovative Data Platform.
• Inc. 5000 Fastest-growing Private Companies for four years in a row.