Kore.ai Wants to Let AI Build, Govern, and Optimize Your AI Agents

Thu, 21 May 2026 15:00:00 +0000

Enterprise AI has a production problem. Organizations can build agents in the lab. Getting them into production — governed, auditable, and scaling across departments — is another story. Kore.ai thinks it has a fix.

The company today launched the Kore.ai Agent Platform, which it describes as the first AI-programmable, AI-native foundation for building, governing, and optimizing the agents, systems, and workflows running across an enterprise. The platform is generally available now on Microsoft Azure, with broader cloud availability to follow.

A New Language for Agents

The centerpiece of the launch is Agent Blueprint Language (ABL), a compiled, declarative foundation language that standardizes how AI agents, systems, and workflows are defined, validated, and governed. Think of it as a common language that spans however many agents a company is running — regardless of where those agents were built or deployed.

ABL ships with six built-in orchestration patterns for multiagent coordination: supervisor, delegation, handoff, fan-out, escalation, and agent-to-agent federation. The goal is to give enterprises a consistent, repeatable way to wire agents together without custom engineering each connection from scratch.

The other major piece is Arch, Kore.ai's AI agent architect. Arch takes plain-language business objectives and translates them into production-ready ABL. It handles the full agent lifecycle — designing the underlying agent topology, validating agents before deployment, and continuously refining them using real-world production traces.

Together, Arch and ABL are what Kore.ai means when it says AI is building AI.

The Third Pillar: Dual-Brain Architecture

The platform also introduces what Kore.ai calls a Dual-Brain Architecture — two cognitive engines running in parallel through shared memory. One handles agentic reasoning. The other handles deterministic flows. Both are authored in ABL and governed by a single runtime.

The practical implication: governance isn't left to the agent. Deterministic constraints and flow controls are enforced at the platform layer. Every agent action is logged, timestamped, and traceable. That matters for enterprises operating in regulated industries where audit trails aren't optional.

Who This Is Built For

Kore.ai frames the business case squarely around the enterprise C-suite.

For the CIO, the platform consolidates fragmented agents — third-party and home-grown — into a single foundation, and compresses delivery from quarters to days. For the CISO, governance is enforced outside the model's control and every policy decision is traceable to a specific regulatory requirement. For the CFO, the economics shift over time: because Arch, ABL, and the runtime are shared infrastructure, the marginal cost of each additional agent decreases as more agents run on the same foundation.

Raj Koneru, CEO and Founder of Kore.ai, put it plainly: "Enterprise AI is entering its third wave, where governance, observability, and trust define success at scale."

Azure First, With Enterprise Compliance Built In

The platform launches on Microsoft Azure and is built natively on the Azure stack across compute, identity, AI, and security. It is also a launch partner for Microsoft Agent 365.

On the compliance side, the platform is SOC 2 Type II, ISO 27001, and PCI DSS certified, FedRAMP Moderate Authorized, and HIPAA-aligned. Real-time PII tokenization, tenant isolation, and immutable audit trails apply to every agent action. Customers can deploy in public cloud, sovereign regions, private cloud, or on-premises, with data residency guaranteed by region.

At launch, the platform supports 40+ voice and digital channels, with 300+ integrations covering Microsoft A365, Salesforce, HubSpot, Jira, GitHub, and systems across banking, healthcare, retail, and telecom.

The Bigger Picture

For developers and architects, the ABL approach is worth paying attention to. The push toward standardized agent definition languages reflects a broader recognition that the multiagent era needs structure — not just capability. The ability to build production-grade agents in days instead of months is a meaningful claim, though organizations will want to pressure-test that against the complexity of their own environments.

Kore.ai has been in the enterprise AI space for a decade, with 450+ Global 2000 customers. That install base gives the company a real-world proving ground for a platform that, if it delivers, could significantly change how enterprises approach AI operations at scale.