Policy as code capability transforms compliance in regulated agentic AI workflows

Automated policy enforcement establishes guardrails, codifies compliance and boosts transparency across enterprise operations

Kyndryl (NYSE: KD), a leading provider of mission-critical enterprise technology services, today announced an innovative capability for creating policy-governed agentic AI workflows to enable enterprises to scale agentic AI across complex and highly regulated environments. Kyndryl’s policy as code capability translates customers’ organizational rules, regulatory requirements, and operational controls into machine‑readable policies that govern how agentic AI workflows execute, to support consistent, auditable, and trustworthy outcomes.

Customers want to reap the benefits of integrating agentic AI into their operations, but security, compliance, and control challenges inhibit trusted deployment of AI agents. In fact, 31% of customers cite regulatory or compliance concerns as a primary barrier limiting their organization’s ability to scale recent technology investments.

Kyndryl’s policy as code capability addresses these concerns by defining operational boundaries and designing agents actions to remain explainable, reviewable, and aligned with the customer-defined business and regulatory requirements. This combination also helps reduce costs, accelerate decision-making, eliminate errors and power AI-native workflows under defined policy guardrails.

“Kyndryl’s policy as code capability overcomes limitations of conventional AI agent controls and provides the structure customers need as they adopt agentic AI solutions,” said Ismail Amla, Senior Vice President, Kyndryl Consult. “By embedding and codifying business and regulatory requirements directly into AI agent operations, we can help customers execute AI workflow that is governed, transparent, explainable, and aligned with their organizational requirements.”

Policy as code is a critical element of the Kyndryl Agentic AI Framework, providing a logical enforcement layer that dynamically governs how AI agents execute, interact, and operate across systems. Kyndryl’s approach to codifying compliance into enterprise workflows is strengthened by insights drawn from decades of operating complex enterprise environments and the nearly 190 million automations the Company manages every month for these mission-critical systems. These operational foundations enable more reliable governance, improve agent explainability, and reduce unexpected behaviors in production environments.

Embedding Policy-Governed Agent Workflows into Business Operations

Kyndryl policy as code enables governance of agentic workflows and is bolstered via differentiated capabilities including:

• Deterministic execution – Agents only execute actions permitted and enforced by pre-defined policies, reducing operational risk.
• Eliminates hallucination impact – Guardrails block unpredictable or unauthorized actions along the workflow, eliminating operational impact of agentic hallucinations.
• Audit-by-design transparency – Each agent action and decision is logged and explainable, supporting compliance and oversight.
• Human supervision – Agents execute tasks aligned with established and testable policies that are observed via a dashboard to support consistent actions and decisions.

Kyndryl’s structured approach to managing agentic workflow execution supports controlled and safe deployment of policy-constrained autonomous agents in sectors such as financial operations, public services, supply chains, and other mission-critical domains where reliability and predictability are essential.

Learn more by connecting with a Kyndryl Consult expert to design, implement, and operate agentic AI solutions governed by the Company’s enterprise-grade policies, oversight, and compliance controls.

The post Kyndryl Unveils Agentic AI Workflow Governance first appeared on AI-Tech Park.

Origianl Creator: PR Newswire
Original Link: https://ai-techpark.com/kyndryl-unveils-agentic-ai-workflow-governance/
Originally Posted: Fri, 13 Feb 2026 08:45:00 +0000