Perforce Software is expanding its Perforce Intelligence platform with new capabilities designed to help enterprises manage AI-driven software delivery, testing and compliance.
The company announced updates that include Perforce Agentic Gateway, Perforce Autonomous Testing, and Perforce Intelligence Unified Compliance. The release is focused on giving enterprise software teams more visibility and control as AI becomes more widely used across development pipelines, testing workflows, infrastructure, and compliance processes.
Perforce is positioning the update around what it calls the AI-Driven Development Lifecycle, or AI-DLC. The concept reflects the company’s view that AI is becoming a more active part of how software is built and delivered. Instead of sitting outside the development process as a standalone productivity tool, AI is increasingly being connected to the systems and workflows that support code, testing, infrastructure, and governance.
Key Takeaways
- Perforce expanded Perforce Intelligence
- Three new AI capabilities were announced
- The update focuses on governance, testing, and compliance
Why AI Governance Is Becoming Essential
That creates practical questions for enterprise technology leaders. They need to understand where AI is being used, what systems it can access, how its actions are tracked, and whether its outputs meet security, compliance, and quality requirements. Those questions become more important in regulated industries and large-scale software environments, where development activity often touches critical applications, sensitive data, and business operations.
Perforce Agentic Gateway Centralizes AI Orchestration
Perforce Agentic Gateway is the company’s answer to part of that challenge. The gateway is described as an MCP-agnostic orchestration layer that provides centralized access to the Perforce MCP portfolio through a single install and guided setup experience. It allows AI agents and tools to access, coordinate, and automate workflows across Perforce products.
The gateway can also be used to manage third-party MCPs for compliance and reduced token consumption. That gives organizations a way to bring more structure to how AI agents interact with software delivery tools, while also giving teams more visibility into AI usage and cost.
Token consumption is becoming a more practical concern as companies expand AI usage across development teams. As more agents, copilots, and AI-enabled tools are added to workflows, organizations need to monitor usage patterns and understand whether AI activity is producing measurable value. Perforce is tying its gateway message to that broader need for control over both compliance and cost.
Anjali Arora, CTO of Perforce, said the future of enterprise AI will depend on how effectively organizations can control, scale, and operationalize AI workflows and models.
“The future of AI in the enterprise will be defined by how effectively organizations can control, scale, and operationalize AI workflows, models and deliver real business results with clear visibility and traceability,” Arora said.
Autonomous Testing Brings AI to Software QA
Perforce is also adding new capabilities around autonomous testing. The Perforce Autonomous Testing platform allows users to describe what they want to validate in natural language through a single chat interface. AI then helps execute the testing process.
The company said the platform supports functional, performance, mobile, desktop, web, and accessibility testing through a consolidated experience. Initial capabilities include natural language test configuration and execution, AI-assisted testing orchestration, execution workflows where AI determines how tests should run based on application context and testing objectives, and support for tests that can work across desktop web, iOS, Android, visual accessibility, and performance environments.
The testing update addresses a familiar software delivery issue. Many organizations have invested heavily in development speed, automation, and DevOps practices, while testing can remain fragmented across separate tools, specialized teams, and manual processes. Natural language test creation could make it easier for business users, QA teams, and developers to define validation needs without relying on traditional scripting for every scenario.
Perforce said the Autonomous Testing platform builds on its existing BlazeMeter and Perfecto technologies. Future versions are expected to include an integration with Delphix for test data and environment provisioning.
The ability to broaden testing participation could be important as AI-assisted development increases the pace of software change. If teams are using AI to generate code, suggest configurations, or automate parts of the delivery process, testing workflows need to keep up with that increased activity. A consolidated testing experience could help organizations improve coverage while reducing the friction involved in creating and running tests across different environments.
Unified Compliance Turns Policies into Code
The third major part of the announcement is Perforce Intelligence Unified Compliance. The platform is described as an intelligence layer that sits above existing Puppet deployments. It translates internal and external policies into code, routes enforcement to applicable infrastructure, monitors for drift, remediates violations, and maintains audit evidence.
That capability is aimed at one of the more difficult parts of enterprise compliance: turning written policy into enforceable technical controls. In many organizations, security and compliance requirements are documented in human language, then interpreted by technical teams and implemented across infrastructure. That process can be slow and inconsistent, particularly across on-premises, hybrid, and multi-cloud environments.
Perforce said Unified Compliance can support Kubernetes environments and help regulated enterprises maintain an auditable compliance posture. Current capabilities include cloud infrastructure cost and financial compliance, natural language policy intelligence, AI-driven drift remediation for infrastructure and Kubernetes, and executive-level compliance posture dashboards.
The company also said future iterations will include additional integrations across Perforce’s compliance enforcement tooling, including capabilities related to data governance, product lifecycle, open source software and supply chains, and safety-critical code.
The compliance announcement extends Perforce Intelligence beyond development productivity and testing into broader governance. As AI becomes more connected to software delivery systems, enterprises need a way to document whether policies were followed, whether systems drifted from approved configurations, and whether remediation occurred. That evidence is especially important for organizations operating in regulated or high-stakes environments.
Why It Matters
The release also reflects growing executive attention around AI return on investment. Perforce cited a 2026 Harris Poll survey commissioned by Dataiku that found 80% of CEOs said their role would be at risk if their company failed to deliver measurable business gains from AI by the end of 2026.
Jim Mercer, program vice president at IDC, noted that the ability to prove AI ROI depends on having control mechanisms and visibility into AI activity.
“Scaling AI across the software delivery lifecycle or AI lifecycle introduces new challenges around orchestration, governance, and compliance,” Mercer said. “There’s a lot of money being spent on AI, but the next evolution needs to be on ROI of AI, which can only be measured if you have control mechanisms in place and visibility into what it’s doing.”
The Bottom Line
For Perforce, the broader message is that AI adoption inside software delivery needs governance at the workflow level. The company is building Perforce Intelligence around orchestration, autonomous testing and continuous compliance, with an emphasis on environments where quality, security and reliability are central requirements.
That positioning fits Perforce’s existing customer base. The company serves teams building high-stakes software systems and revenue-critical applications, including enterprises in sectors where software failure can carry significant operational or compliance consequences. Perforce said its customers include organizations in more than 80 countries, more than 75% of the Fortune 100 and 50% of the Global 500.
As AI becomes more embedded in development and operations, enterprises will need to connect AI activity to the controls already used to govern software delivery. That means tracking which systems AI agents access, how workflows are executed, how policies are enforced, how testing is performed, and how evidence is preserved.
Perforce Intelligence is being positioned as a platform for that operating model. The latest release brings together three areas that are likely to become more closely connected as AI usage expands: agent orchestration, testing automation and compliance enforcement.