Building Reliable AI Agents: How Glean Ensures Practical Performance in the Workplace

AI Agents in the Workplace

As artificial intelligence continues to make its way into everyday work tools, many companies are exploring how to build smarter, more reliable AI agents. These agents—essentially digital assistants powered by large language models—can help with tasks like writing emails, answering internal questions, or summarizing documents. But as anyone who has tried out a new AI tool knows, it’s one thing for an agent to give a good answer once, and quite another for it to consistently perform well in real-world situations. That’s why Glean, a company focused on enterprise search and AI productivity tools, recently shared insights into how they evaluate their AI agents to ensure they’re not just impressive demos but actually useful in practice.

Evaluation from the Start

At the heart of Glean’s approach is the idea that evaluation should be part of the development process from the very beginning. Rather than relying on generic benchmarks or hypothetical questions, Glean encourages teams to build evaluation sets based on real user needs and actual workflows. For example, if you’re building an agent to help sales teams write outreach emails, your evaluation should include examples of what a great email looks like—complete with personalization, clarity, and relevance. This hands-on method helps developers understand not only whether an agent works but also how well it meets the specific expectations of its users.

Simplicity in Performance Metrics

Glean also emphasizes simplicity when it comes to measuring performance. Instead of using complex scoring systems that can be hard to interpret or inconsistent across reviewers, they recommend binary grading—essentially a pass/fail system—for most metrics. This makes it easier to spot patterns and identify areas for improvement. Metrics are grouped around key qualities like completeness (does the response include all necessary information?), tone (is it appropriate and easy to understand?), and groundedness (are claims based on accurate data?). These clear criteria allow teams to make targeted adjustments, such as refining instructions or adjusting how much context the agent uses.

Scaling Evaluations Effectively

One particularly interesting aspect of Glean’s strategy is how they scale these evaluations across their platform. As more users interact with agents in real-world settings, feedback like upvotes or downvotes becomes valuable data for improving performance. Glean uses this feedback alongside automated grading powered by language models themselves—a kind of AI evaluating AI—to keep up with growing demand while maintaining quality. This loop of testing, learning from users, and refining is what helps move an agent from being just functional to truly helpful.

Glean’s Broader Direction

Looking at this announcement in context, it aligns closely with Glean’s broader direction over the past few years. The company has steadily expanded from enterprise search into more proactive AI tools that support knowledge work across organizations. In previous updates, Glean introduced features like contextual search and personalized recommendations based on workplace data. Their focus has consistently been on making information easier to find and use within large companies—a goal that naturally extends into building reliable AI agents that can assist with daily tasks.

Commitment to Quality

What stands out here is not a dramatic shift in strategy but rather a deepening of their commitment to quality and usability at scale. By sharing their internal practices around evaluation, Glean is offering transparency into how they aim to build trust in their AI systems—something that many businesses are still figuring out as they adopt these technologies.

Conclusion: Practical Insights

In summary, Glean’s latest update provides a thoughtful look at what it takes to move beyond flashy demos toward dependable AI agents that genuinely support workplace productivity. Their emphasis on real-world testing, clear metrics, and continuous improvement reflects a practical mindset that many companies may find relatable as they navigate their own AI journeys. While every organization will have its own unique needs and challenges, the core message here—that good evaluation leads to better outcomes—is one that applies broadly across industries exploring the potential of generative AI tools.