Create AI agents that serve customers and do work for you
This week adds an AI assistant that handles a narrow customer task. You will define its job, give it approved knowledge, set boundaries, and test when it should hand the conversation to a person.
An AI agent receives a goal, uses information or tools, makes limited decisions, and produces an action or answer. Start with a narrow, low risk task such as answering product questions. More freedom creates more ways to fail.
Give the assistant current, approved information such as product details, policies, setup steps, and frequently asked questions. Remove duplicates and contradictions. Tell it to say when the answer is missing rather than filling gaps with a guess.
List topics the assistant may answer, topics it must refuse, and situations that require a person. High stakes, account specific, payment dispute, legal, medical, or safety questions should have clear escalation paths.
Create normal questions, ambiguous questions, hostile prompts, unsupported requests, and questions with outdated assumptions. Record the expected response before testing. Review both helpfulness and restraint.
Approximately 4.9 million views
Why it fits: Explains the progression from basic language models to workflows and autonomous agents using accessible, non-technical examples.
Define an agent with one goal, three available tools, a clear input and a measurable successful output.
The video supports the lesson above — it doesn't replace it.
You are the customer assistant for [product]. Answer only from the approved knowledge provided. If the answer is missing or uncertain, say so and offer the correct human contact path. Never invent policies, prices, results, or account details. Keep answers concise and ask one clarifying question when needed.
Week 10 · Case study
Source note: Documented customer example from Replit. Knowledge-base size and usage counts are reported by the platform. The student-sized version below is an illustrative exercise, not part of the reported case.
In a recent Replit project, SaaStr created “Digital Jason,” an AI mentor grounded in approximately 20 million words of existing SaaStr material. Instead of acting as a general chatbot, it answers questions for SaaS founders using a defined knowledge base and recognizable subject expertise.
Replit reports that the assistant was used more than 100,000 times. Its value came from the combination of a narrow audience, substantial approved material, and a clear job.
Build an assistant for one limited knowledge source, such as a course handbook, club rules, product manual, or collection of frequently asked questions. Require it to admit when the answer is not present.
Which questions should the assistant refuse or send to a human?
Respond in your submission →Launch an AI assistant that answers customer questions about your product. Submit the live link or demonstration, agent brief, approved knowledge source, test cases, results, and escalation rules.