Treat custom AI agents like junior employees, not finished software. They require daily check-ins to monitor for bugs, performance issues, and regressions. There is no "set and forget"—a human must actively manage the agent every day for it to succeed.
An overlooked benefit of custom-built tools is dramatically higher customer adoption. By designing a frictionless experience for your specific workflow, you overcome the common problem of customers refusing to log into and use yet another third-party SaaS application, boosting compliance.
The traditional Quarterly Business Review (QBR) is an outdated, reactive process based on past events. An AI agent can act as a continuous, real-time QBR, constantly monitoring customer progress, identifying gaps, and proactively engaging them, preventing issues before they happen.
To get the best results from AI code generation platforms, first use a conversational LLM like Claude to brainstorm and write a detailed product spec. This two-step process—spec generation then code generation—improves the final output and reduces costly iterations with the coding agent.
The true power of an AI agent is its capacity to handle the mundane, repetitive work that humans—both internal teams and external agencies—often neglect or de-prioritize. SaaStr couldn't find people willing to consistently manage hundreds of follow-ups, a task their AI now handles flawlessly.
Avoid storing sensitive data like contracts directly within your custom-built agent. Instead, use "agent hopping": have the AI call APIs to a secure system of record, like Salesforce, to access data on-demand. This adds a crucial security layer and prevents data liability.
The primary advantage of building your own AI tool is the ability to instantly respond to customer needs. Unlike off-the-shelf software with long roadmaps, non-technical teams can implement and ship simple customer feature requests on the same day, creating a magical user experience.
Don't try to build a complex AI agent from day one. SaaStr's AI VP of Customer Success started as a basic project management portal to replace a clunky tool. Its advanced, agentic capabilities were layered on over months as real user needs became clear post-launch.
Despite fears of high AI usage bills, the actual token costs for running multiple customer-facing AI applications can be trivial. SaaStr's entire suite of AI tools, including its AI VP of CS, runs on a total budget of less than $200 per month for all API usage.