glean agent building - tips

Hi Michael, great question! The main difference is that Glean agents don't just chat; they actually work with tools and workflows to get things done. Think of a standard chatbot as a librarian who can only tell you where a book is, whereas a Glean agent is like a librarian who can check the book out, renew it, and even order a new one based on your reading history. They use search to route queries to the right systems, ensuring they follow company policies and permissions every step of the way.

It really depends on your background and what you need, but Glean offers four main approaches to suit different skill sets. If you are a Python developer, you might jump straight into LangChain for a fast deployment with rich tooling. On the other hand, if you want maximum control or are working in a multi-language environment, you'd use their Direct API integration, though that does come with higher complexity.

Absolutely, Sky loves the Glean Agent Toolkit and their visual no-code builder for those scenarios. The visual builder lets you design agents by dragging and dropping logic, which is perfect for creating workflow agents that follow specific processes without writing code. You can even "vibe code" by conversing with the builder to refine steps and connect data sources quickly.

You're right to be concerned because Glean builds security and compliance directly into every step of the process. When you connect agents to live company data, the platform automatically enforces user permissions so that agents only see and act on information they are allowed to access. This means you can safely connect them to sensitive data without worrying about accidental leaks or unauthorized access.

The best advice is to start with a solid plan that outlines exactly what the agent will do and what data it needs to succeed. You don't need a massive document, but you should define the high-level logic, like whether the agent should answer questions or trigger a specific action like filing a leave request. For example, a plan might specify that general HR questions come from policy documents, while parental leave inquiries require checking an employee's tenure first.

Glean provides deep observability so you can monitor performance, track error rates, and see how often users upvote or downvote the agent's responses. This data helps you understand which parts of the agent are working and which ones need fixing, allowing you to iterate and improve over time. It essentially turns agent experiments into measurable ROI by showing you exactly what's delivering value.

Sure, the key takeaway is that you don't need to choose between a powerful, secure agent and a simple user experience; Glean provides the flexibility to build exactly what you need. Whether you choose the visual no-code builder for speed or the direct API for deep customization, the platform ensures your agents are grounded in enterprise context and secure by default.