SmartBear Innovation & Technology Culture

How do your teams stay ahead of emerging technologies or frameworks?

At SmartBear, staying ahead of new technologies starts with the people we hire and the culture we build. We look for employees who are curious, motivated and eager to make an impact. Those qualities are reinforced in a quarterly awards program where we celebrate people who demonstrate openness and curiosity. This sends a clear message that learning, asking questions and trying new things are part of the job, not something extra.

Open source is another big way for us to stay close to what’s coming next. SmartBear has a long history of contributing to projects like OpenAPI and we currently support OS projects including Swagger, Pact, SoapUI and Stoplight. The BugSnag team contributes to projects such as KSCrash, which helps us improve the tools we rely on ourselves. Working in the open keeps our teams connected to real developer needs, emerging standards and how technologies are actually used in practice.

Can you share a recent example of an innovative project or tech adoption?

A recent example is our early work with the model context protocol. As this AI-focused standard gained traction, we added MCP generation to Swagger and released the SmartBear MCP server across several products. This enabled teams to experiment with AI-driven workflows early without waiting for the ecosystem to mature.

We also hosted an MCP hackathon, uniting teams from our global offices. We worked with GitHub Copilot, Claude, Gemini and ChatGPT combined with SmartBear MCP server to quickly solve real customer problems and turn disconnected workflows into autonomous, intelligent systems.

One standout project was an MCP server tool that automatically detects and resolves discrepancies between live API implementations, documentation and contracts. The team built an MCP-powered workflow capable of automating the entire reconciliation process with a single prompt. Another standout was a QA intelligence assistant designed to unify data from across platforms and tools into a single, actionable view of product quality. The assistant created a single, trusted view of risk and quality — showing what’s being built, tracking what’s tested and revealing what fails in production.

How does your culture support experimentation and learning?

Experimentation and learning are built into how we work at SmartBear. We value curiosity and initiative and it’s visible through hiring, leadership support and recognition. We remove barriers by giving teams flexibility, budgets for experimentation and regular hackathons, making it easier to try new tools and approaches. In 2025, this was strengthened with a greater focus on AI, encouraging the company to get hands-on with new tools and workflows.

Experiments at SmartBear can start small. One employee started a side project that grew into an internal AI assistant. Available in Slack, BrainBear mines our internal wiki and helps employees find information about policies and projects. After proving its value, it gained the support of CEO Dan Faulkner, was shared companywide and saw rapid adoption. It’s a great example of how ideas are encouraged to grow when they solve real problems.

Most importantly, learning is expected to lead to real outcomes. When experiments show value, we invest in and scale them. Whether it’s MCP support across our products or internal tools like BrainBear, the message is consistent: try things, learn quickly and if it works, we take it further.

How does innovation show up in your company culture?

At SmartBear, innovation starts with a clear thesis: As technology transforms how software is built, quality must evolve just as fast. We’ve been innovating across the API lifecycle for years, from foundational design with Swagger to automated testing and observability, and we’re extending that leadership into the AI era. 

Our culture supports innovating alongside our customers. Through SmartBear AI Labs, our product leaders work with engineers and AI specialists to prototype and validate ideas such as autonomous testing and AI-driven governance. We move fast and validate rigorously using real-world customer examples. 

For example, a beta customer recently rebranded their product, but their legacy automation suite failed immediately due to a shifted cookie banner blocking the login. Our autonomous testing product adapted without interruption and even surfaced a regression. That’s the standard we’re building toward: Systems that self-heal while still catching what matters in production-grade, customer-validated situations.

What’s one recent innovation that improved user or employee experience?

The SmartBear Model Context Protocol Server enables AI coding assistants to securely interact with SmartBear tools through a governed interface. Developers can retrieve API definitions, trigger tests, and orchestrate workflows directly from their integrated development environment, bringing quality signals and governance into the same loop where code is written. 

We recently hosted an internal MCP hackathon, and the winning project, API Drift, is on the road to becoming a customer-facing offering. API Drift is an MCP-based tool that automatically detects and resolves discrepancies between live API implementations, Swagger documentation and PactFlow contracts. Instead of discovering misalignment weeks later, teams can identify drift in real time — before it impacts their consumers. 

We also integrated SmartBear Reflect with SAP Cloud Application Lifecycle Management, embedding AI-powered automation into enterprise release pipelines. This integration won SAP’s global ALM-athon and enables teams to test earlier and more frequently, ensuring that their SAP environments remain stable and performant. 

Across these innovations, the goal is the same: Reduce friction for our customers while increasing confidence in their software quality.

How do you balance experimentation with stability?

At SmartBear, we balance experimentation with stability by holding ourselves to the same quality standards as our customers do and by using our own products to enforce them. When we introduce new API capabilities, we rely on Swagger Contract Testing to ensure backward compatibility and prevent breaking changes. Our deep test management and automation capabilities validate new functionality continuously across products. And we instrument our platforms with BugSnag to provide real-time insight into application stability, allowing teams to detect and resolve issues early in development.

This approach allows engineers to push forward on agentic AI and autonomous testing while maintaining enterprise-grade reliability. For our teams, that means working on cutting-edge technology with the discipline and tooling required to operate at scale.

What’s it like to work on the AI and machine learning team at your company?

The AI/ML team at SmartBear is focused on building BearQ — our agentic QA platform. The core problem we're solving is that AI has fundamentally changed how applications are built and consumed and traditional testing approaches can't keep pace. Our team's job is to close that gap at AI speed and scale. 

Because of that, the work is genuinely broad. From the product side, I’m between two pieces of the work: shipping new product features and the ongoing improvement of how the agents behave based on what we and our customers see in production. Most weeks, I’m moving between engineering decisions, customer conversations and the patterns surfacing in real usage. 

The team is organized as cross-functional pods — product management, design and engineering working on the same problem at the same time. We ship small, watch what the agents do in real environments and adjust from there. We plan for the future but are highly dynamic. Multi-year roadmaps don’t hold up when you’re working with systems that behave unpredictably, so we plan and execute in shorter windows.

How is your team applying emerging technology in practical, business-relevant ways?

BearQ is an agentic quality assurance platform built on LLMs and runs three coordinated agents against a web application. There’s an explorer agent that maps the app’s UI and state transitions, a tester agent that validates user flows end-to-end and a QA lead agent that coordinates them. The aim is to remove the brittleness and bottlenecks that have held back automated UI testing — a problem AI coding velocity makes harder to ignore. 

Real web apps are messy. Auth flows, modals, race conditions, components that look the same but behave differently depending on state, etc. A significant part of our engineering work is getting agents to act reliably in that environment and just as importantly, knowing when to surface decisions to a human rather than proceed autonomously. We're intentional about where LLM judgment adds value versus where deterministic, auditable outcomes are required.

What should candidates know about the tools, collaboration or problem-solving involved in AI work at your company?

On tools: The team uses foundation models from the major providers and has built a meaningful amount of in-house agent orchestration. The model choice is constantly evolving to balance performance, speed and cost. Most of the interesting work, though, sits around the model rather than in it: orchestration, prompt design, agent coordination and the product surfaces that let us and our customers catch when an agent has gone off the rails. 

On collaboration: As mentioned, we rely on a cross-functional pod structure where product managers, design and engineering work on the same problem at the same time. The team tends to learn more from running something than from spec’ing it out in advance, so we lean toward shipping early and tuning live. 

On problem-solving: The quality assurance domain rewards people who take it seriously. You don’t need to be a quality assurance expert on day one, but understanding how real testers think about their work tends to matter more than the AI specifics. The work also asks for comfort with ambiguity. There aren’t widely accepted answers yet for how to build dependable agentic systems, so part of the job is forming an opinion and being willing to revise it.