Product design question
How would you let non-technical employees build their own Glean agents?
- Glean
- Product design
- Medium
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What this question tests
Product design for democratizing agent creation, balancing simplicity for non-technical builders against the real complexity of reliable automation.
How to approach it
- Clarify the target user: employees like HR, ops, or marketing staff who understand a repetitive task well but cannot write code or complex logic.
- Design a natural-language agent builder: the employee describes the task in plain language, and the system proposes a draft workflow (data sources to check, steps to take, output format).
- Offer templates for common cross-team automations, such as new-hire document summaries or weekly status roundups, so users start from a working example.
- Add a test-and-preview mode where the employee can run the draft agent on sample data and see the output before deploying it broadly.
- Add guardrails: require explicit approval before an agent gets write access to any system, and log every action it takes for auditability.
- Define success as number of non-technical employees who successfully deploy a working agent, and reduction in IT/engineering tickets requesting custom automations.
What a strong answer includes
- Targets the real barrier (translating a known task into reliable logic) rather than assuming natural language alone solves it.
- Proposes concrete scaffolding (templates, test-and-preview) suited to a non-technical builder rather than a raw configuration interface.
- Adds explicit guardrails around write access and auditability, recognizing the risk of broadly distributed agent-creation power.
- Uses reduced IT ticket volume as a business-relevant proxy for the feature actually replacing ad hoc requests.
- Balances ease of use with safety, avoiding a design that trades all control for simplicity.
Common mistakes
- Assuming a chat-style prompt box alone is sufficient without templates or a testing step.
- Ignoring guardrails around agents getting write access to sensitive systems.
Likely follow-up questions
- How would you handle an agent that behaves unexpectedly once deployed?
- What approval process would you require before an agent goes live org-wide?
- How would you measure whether these employee-built agents are trustworthy?
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More questions from Glean
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop