Kore.ai has unveiled a radical reimagining of its core technology with the launch of the Artemis edition of its Agent Platform. This innovative system empowers enterprises to construct, oversee, and refine AI agents by leveraging artificial intelligence itself, dramatically reducing the typical months-long engineering timelines to mere days.
The introduction of the Artemis platform arrives amidst an intense competitive landscape, where major technology players like Microsoft, Salesforce, Google, and ServiceNow are aggressively vying to establish themselves as the definitive infrastructure for enterprise AI agents. Kore.ai’s strategy centers on neutrality, employing a proprietary intermediary language for agent definition, and championing a philosophy where AI, rather than human developers, bears the primary burden of development and optimization.
“Our aim is to fundamentally alter the paradigm for how organizations design, develop, deploy, and optimize agentic AI applications,” stated Raj Koneru, founder and CEO of Kore.ai, in an exclusive interview. “The overarching theme of our new approach is ‘AI with AI’ – encompassing design, development, testing, deployment, management, and optimization.”
A Novel YAML-Based Language Standardizes Enterprise AI Agent Definition and Governance
At the heart of the Artemis platform lies the Agent Blueprint Language (ABL). This compiled, declarative language, built upon YAML, standardizes the definition, validation, and governance of AI agents, workflows, and multi-agent systems. Kore.ai positions ABL as a crucial intermediary layer, bridging the gap between natural-language inputs from business users and the production infrastructure where agents operate.
ABL incorporates its own parser, compiler, and runtime environment. It natively supports six distinct orchestration patterns—supervisor, delegation, handoff, fan-out, escalation, and agent-to-agent federation—to orchestrate the collaboration of multiple agents on complex tasks.
Koneru highlighted ABL’s role in addressing a significant deficiency in the current AI ecosystem. “While code generation offers substantial value, developers use that code to build applications,” he explained. “We identified a critical gap between code generation and its actual deployment on infrastructure, encompassing the deployment, version management, governance, and observability required for production environments.”
Because ABL artifacts are YAML-based, they can be seamlessly integrated into version control systems like GitHub and managed through CI/CD pipelines. This design facilitates review by both developers and business stakeholders, effectively bridging the divide between low-code/no-code solutions and traditional software engineering practices. “The end product is ABL, a YAML-based construct that can be stored in GitHub and version-controlled,” Koneru added. “This provides a unified standard for business users, developers, and IT teams.”
Kore.ai’s AI Architect Translates Business Objectives into Production-Ready Agent Systems
A second key innovation is Arch, an AI system designed to translate business requirements into production-ready ABL code. Users articulate their specifications, data sources, and business rules using natural language. Arch then orchestrates the design of the multi-agent topology, selects appropriate orchestration patterns from the platform’s repertoire, generates the ABL code, produces test data, deploys the application, and monitors its performance in production.
Crucially, Arch also manages the optimization process. It continuously assesses whether deployed agents are achieving their objectives, identifies performance bottlenecks, and automatically regenerates and redeploys refined ABL to enhance overall efficiency.
“Consider it this way,” Koneru elaborated. “If the initial goal was 50% automation for a specific use case, and we’re initially achieving 30%, the optimization cycle, informed by actual usage data, iteratively adjusts the application to reach the target 50%.”
This closed-loop methodology—encompassing design, build, test, deploy, manage, and optimize—is Kore.ai’s strategic differentiator. It aims to surpass both the configuration-centric platforms that characterized earlier chatbot development and the code-intensive frameworks offered by companies like Anthropic and OpenAI, which Koneru believes place an undue burden on individual developers. “This represents a paradigm shift in how AI agents have historically been constructed, whether through no-code, configuration-based platforms like ours, or pro-code capabilities offered by services like Google Cloud’s Codex, which require developers to build their own supporting infrastructure,” he remarked.
Kore.ai’s ‘Dual-Brain’ Architecture Enhances AI Agent Safety in Regulated Industries
Perhaps the most architecturally significant feature of the Artemis platform is its Dual-Brain Architecture. This design employs two parallel cognitive engines: one for agentic reasoning powered by large language models (LLMs), and another for the deterministic execution of business rules. These engines operate in tandem through shared memory within a unified runtime environment.
This architecture stems from Kore.ai’s extensive experience deploying AI in highly regulated sectors such as banking, healthcare, insurance, and telecommunications. In these environments, delegating all decision-making solely to a language model is often unfeasible.
“Enterprises are not prepared to completely cede decision-making authority to an AI model,” Koneru emphasized, drawing a clear distinction with newer AI-native startups. “Many emerging AI companies primarily offer frameworks that act as wrappers around LLMs. This results in a heavy reliance on the LLM for decision-making, with the model itself responsible for implementing guardrails.”
Kore.ai’s approach reverses this dynamic. Input and output guardrails are enforced at the platform level, independent of the AI model. Evaluations are executed within the platform’s governance engine. Business rules can be processed deterministically where precision is paramount, while LLMs handle conversational aspects and reasoning where appropriate. In scenarios like prescription refill processing for millions of consumers in healthcare or portfolio management advice in banking, the consequences of inaccurate or flawed AI responses are severe. Kore.ai presents the Dual-Brain Architecture as an engineering solution to the trust deficit that has historically hampered enterprise AI adoption in regulated industries.
Kore.ai’s Strategic Partnership with Microsoft and Commitment to Vendor Neutrality
The Artemis platform is initially launching on Microsoft Azure, with deep native integrations supporting Microsoft Foundry, Microsoft Agent 365, Entra ID, and the Microsoft Graph API. Kore.ai is a launch partner for Agent 365 and is working towards becoming a native Azure service within Azure Foundry.
This partnership with Microsoft is comprehensive. Koneru detailed several co-development initiatives over the past year: agents built on Kore.ai’s platform can run on Azure Foundry leveraging its models and infrastructure; Kore.ai’s AI for Work product integrates with Microsoft Copilot, surfacing enterprise data and agentic workflows directly within the Copilot interface; and AI for Service is integrated with Dynamics 365 as a joint go-to-market offering.
“We share a profound relationship with Microsoft,” Koneru affirmed. “In fact, I am currently attending their CEO Summit, and will be here for the next three days.”
Stephen Boyle, CVP of Enterprise Partner Solutions at Microsoft, expressed support for the collaboration in the Artemis press release, noting that the platform “integrates with Microsoft Foundry and Microsoft Agent 365, providing customers with a governed environment to build, deploy, and operate AI agents.”
Concurrently, Kore.ai positions itself as a vendor-neutral alternative to hyperscalers like Microsoft. Koneru addressed this potential tension directly: “The vendors and tech companies you mentioned often have legacy systems they aim to protect,” he explained when asked why a CIO should choose Kore.ai over an incumbent. “There’s an inherent lock-in to their existing ecosystems, whether that’s a Salesforce application, a ServiceNow platform, or Microsoft Azure cloud.” Kore.ai’s platform supports over 175 distinct AI models, including those from OpenAI, Anthropic, and open-source providers. It can be deployed across Azure, AWS, Google Cloud, and on-premises environments, connects to any data source via tool calling or MCP, and delivers across more than 40 voice and digital channels.
Pharmaceutical Chain and Global Investment Bank Showcase Massive AI Agent Deployments
Kore.ai’s assertions of enterprise readiness are substantiated by deployments that rank among the most extensive AI implementations globally.
One of the largest pharmacy chains in the United States, though not publicly named by Koneru, annually handles approximately 750 million consumer calls. The chain partnered with Kore.ai in late March 2025. After deploying on its own infrastructure, it achieved partial deployment across 4,500 of its 9,000 stores within three months, and full implementation across all locations within six months.
“The speed at which they were able to develop highly complex functionalities—requiring an understanding of prescriptions, the ability to answer related queries, and seamless integration with backend systems for prescription fulfillment and refills—was remarkable,” Koneru commented.
A second case study involves the world’s second-largest investment bank, which deployed Kore.ai’s AI for Work product to 135,000 employees and contractors. This platform grants over 30,000 financial advisors access to proprietary research and client portfolio data through a conversational interface, with automated workflows managing routine tasks. The deployment progressed from initial user adoption to global rollout within a year. A third client, a major semiconductor manufacturer with 35,000 employees across multiple countries and languages, initially utilized AI for Work for HR functions such as onboarding, benefits management, and performance reviews, integrating with Workday. The platform’s scope has since expanded to include IT, legal, and facilities management workflows.
Kore.ai’s Analyst Recognition and Funding Bolster its Challenge to Hyperscalers
The Artemis launch enters one of the most fiercely contested arenas in enterprise technology. Competitors like Microsoft’s Copilot Studio and Agent 365, Salesforce’s Agentforce, Google’s Vertex AI Agent Builder, and ServiceNow’s AI Agents are all targeting similar IT budget allocations. Simultaneously, a surge of well-funded startups, from established automation players like UiPath to AI-native entrants, are saturating the market with agent-building frameworks and platforms.
Kore.ai’s competitive stance is fortified by several key factors. The company has consistently garnered recognition from leading analyst firms: it has been named a Leader in the Gartner Magic Quadrant for Enterprise Conversational AI Platforms (ranking highest for Ability to Execute), a Leader in the Forrester Wave for Cognitive Search Platforms (achieving the top score in Strategy), and an Emerging Leader in Gartner’s Emerging Market Quadrants for both Generative AI Engineering and GenAI Applications. Additionally, Everest Group has positioned Kore.ai as a Leader in its 2026 Agentic AI Products PEAK Matrix Assessment.
The company’s financial trajectory further solidifies its credibility. In January 2024, Kore.ai secured $150 million in funding led by FTV Capital, with participation from Nvidia, bringing its total funding to approximately $223 million. At the time, TechCrunch reported that the company’s annual recurring revenue exceeded $100 million, with its platform automating 450 million interactions daily. In January 2026, the company announced a strategic growth investment led by AllianceBernstein Private Credit Investors, with continued support from Vistara Growth, Beedie Capital, and Sweetwater Private Equity. Kore.ai now reports serving over 500 Global 2000 customers and partners, with 75% of its client base operating in regulated industries and supporting more than 300 enterprise integrations.
Implications of the Artemis Launch for the Future of Enterprise AI Agent Platforms
The Artemis platform is available immediately via kore.ai, initially deploying on Microsoft Azure, with broader cloud availability planned. Koneru indicated that existing customers, many of whom utilize Kore.ai’s prior no-code platform, are preparing to migrate to the new architecture, while all new clients will commence with Artemis.
The question of portability remains partially addressed. While ABL, being a YAML-based artifact, can be stored and managed by customers in their own systems, the necessary runtime environment is not yet offered as a standalone component. Koneru mentioned that a streamlined version of the runtime will be released in the future for customers seeking to operate ABL outside the full Kore.ai platform. However, he acknowledged that the initial release prioritizes delivering an integrated enterprise experience.
For Chief Information Officers navigating an increasingly crowded and rapidly evolving market for enterprise AI agents, the Artemis launch presents a distinct strategic decision: commit to a hyperscaler’s proprietary platform, accepting the inherent vendor lock-in, or adopt a neutral layer promising to orchestrate and govern agents across diverse models, clouds, and vendors. This latter approach, however, requires placing trust in a company that, despite its scale and analyst accolades, remains considerably smaller than its major competitors.
“Opting for a single hyperscaler or SaaS provider for an agentic platform inevitably leads to some form of lock-in,” Koneru observed. “What we need is standardization, a centralized method for building and deploying, and a unified approach to governance.”
This is a bold assertion from a company that has dedicated 12 years to developing the foundational infrastructure for enterprise AI, often overshadowed by more prominent players. However, if the next phase of the AI revolution is defined not by the intelligence of individual models but by the trustworthiness of platforms capable of safely managing agents at scale, then Kore.ai’s extensive experience in the often-overlooked domains of compliance, governance, and regulated industry deployments might prove to be its most compelling qualification.
Business Style Takeaway: Kore.ai’s Artemis platform introduces a significant shift towards AI-driven development and autonomous optimization of enterprise AI agents, promising accelerated deployment cycles and enhanced performance. Its emphasis on a neutral, standardized ABL language and a hybrid “Dual-Brain” architecture for safety and control addresses key enterprise concerns about vendor lock-in and operational reliability, positioning it as a strategic alternative for businesses in regulated sectors.
Details can be found on the website : venturebeat.com