Recently, SmartBear unveiled exciting new features for its commercial Swagger toolset. These enhancements are designed to assist organizations in effectively governing, validating, and scaling APIs, especially in an era where AI coding tools are rapidly transforming software development.
The latest updates focus on two key features: an improved Swagger Catalog, which provides centralized visibility into API portfolios for platform teams, and contract testing with drift detection that continuously ensures API behavior aligns with OpenAPI specifications.
According to SmartBear, Swagger supports design, governance, and testing throughout the AI-enhanced API lifecycle, maintaining quality at each stage. It equips users to create APIs that cater not just to humans, but also to LLMs, agents, and ongoing innovation.
AI Is Outpacing Standard Specifications
The company presents these new features under the concept of “application integrity,” which SmartBear’s Chief Product and Technology Officer, Vineeta Puranik, describes as providing continuous and measurable assurance that software functions as intended while maintaining governance at the speed and scale of AI.
Puranik seeks to address a significant challenge: tools like GitHub Copilot and Claude can generate or modify vast amounts of code in a matter of minutes. However, the specifications that govern these APIs do not update themselves automatically. This discrepancy leads to what SmartBear identifies as “drift,” the gap between what an API contract stipulates and the actual behavior of the code.
“Platform leaders encounter fragmented discovery and lack of lifecycle visibility, while engineering and QA teams face silent spec-to-runtime divergence,” Puranik conveyed in an interview with The New Stack.
Addressing Drift Before It Becomes an Issue
SmartBear’s drift detection feature operates within CI/CD pipelines, identifying discrepancies before code progresses to production. This approach is distinct from what API gateways like Kong or Apigee provide, as these tools monitor traffic in production, where errors may have already escaped. SmartBear advocates for a “shift-left” strategy, where discrepancies are detected within the build cycle rather than after deployment, according to Puranik.
The Swagger Catalog enhances visibility by addressing this systemic issue. As AI tools generate and alter APIs at an unprecedented scale, platform teams can easily lose oversight of existing APIs, compliance levels, and those that are production-ready. The catalog offers lifecycle tracking and governance enforcement across an organization’s entire API portfolio, including APIs sourced from code repositories, CI/CD pipelines, and specifications imported from platforms like Postman.
Comprehensive Oversight in One Location
Jason Burch, a senior lead solution architect at an automotive company that beta-tested the features, noted that the catalog’s benefit lies not only in its technical functionalities but also in the organizational clarity it provides.
“When you compile hundreds of internal APIs in one location, it fosters visibility across product, development, and architecture teams, significantly enhancing governance compared to our current workflow,” he commented.
The announcement also includes several additional Swagger platform enhancements set to arrive this quarter. These include a new editor featuring AI-powered API generation, context-aware documentation, Spectral-based governance enforcement, support for MCP server facilitating natural-language API automation, and expanded support for OpenAPI 3.1, AsyncAPI 3.0, and GraphQL.
APIs as Infrastructure for Agents
The support for the MCP server is crucial due to the nature of agent-to-agent communication, which relies heavily on APIs. Puranik emphasizes that having machine-readable, current specifications is no longer just an advisable practice; it is now a necessity.
Drift not only disrupts testing in this environment but also jeopardizes integrations. Puranik articulated the issue succinctly: “What allows agents to communicate with one another? It is APIs.”
In addition to the catalog and drift detection features, SmartBear is promoting a new AI-native testing solution named BearQ, which aligns with their overarching application integrity strategy. This tool begins with a URL, autonomously navigates the application functionalities, generates test cases, executes them, and identifies failures—all without the need for script-writing expertise from the user.
“You can instruct it to explore certain functionalities, and it understands your intent,” Puranik explained. “There is no need for the user to provide any scripting language.” Future updates targeting bulk API testing are expected in Q2, she added.
A Comprehensive Platform Rather Than a Singular Solution
SmartBear’s Swagger tools are utilized by over 16 million developers across 32,000 organizations, including notable names like Samsung, Ford, and Marriott. A study by Forrester Consulting discovered that the platform yields a 227% return on investment over three years for a representative enterprise with 200 developers.
At the end of last month, SmartBear introduced the SmartBear Application Integrity Core. Like the recently launched features, this capability enhances and accelerates application testing to keep pace with the escalating speed and volume of AI-driven code creation.
The new features bring agentic and AI elements to human-led testing workflows, facilitating the integration of AI even for on-premises applications. This follows SmartBear’s recent launch of BearQ, further expanding its range of AI-driven application testing products.
Additional improvements include:
- Introduction of a new agentic capability in SmartBear’s test automation platform, Reflect. This enables developers and QA engineers to generate automated tests directly from their coding environment. By invoking Reflect via the SmartBear MCP server, teams can leverage richer context, accessing existing test assets, unified visibility, reporting, and development history. This facilitates the creation of context-aware tests agentically, thereby accelerating the adoption of automation without starting from scratch.
- New Rovo agent capabilities for Zephyr that enable natural-language queries within Atlassian Jira for assessing test coverage, searching test executions, and evaluating release readiness, so QA teams can swiftly identify gaps and prioritize their testing initiatives.
- Inclusion of AI functionalities in SmartBear’s on-premises tools for desktop testing and secure environments, which encompasses natural-language AI test generation in ReadyAPI, facilitating the creation of complex multi-step API tests, as well as enhanced AI-driven object detection in TestComplete. These improvements will bolster automation reliability for applications that are subject to rapid changes, while also implementing enterprise governance controls to uphold compliance and quality standards.
“SmartBear is truly committed to empowering QA teams to accelerate their testing processes and elevate application-level testing. We observe some teams racing towards fully autonomous solutions like BearQ, while others are implementing AI-enabled tools to complement human-directed automation or even manual workflows,” Puranik remarked. “We aim to support our customers at their respective stages in their AI journeys, enabling them to adopt AI confidently, scale their testing processes efficiently, and maintain application integrity as software delivery accelerates.”
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