Episode Details

This is another episode from our AI PM series.

This time, we’re building an AI teammate that runs user research, writes product docs, and powers customer success end-to-end with GibsonAI founder, Harish Mukhami.

Brought to you by:

Amplitude: The market leader in product analytics

Linear: Plan and build products like the best

Maven: I’ve launched my own curation of their courses

Timestamps:

Preview – 00:00:00

Building AI Customer Success Agent (Tool Stack) – 00:01:46

Role of GibsonAI in Building Customer Success AI Agent – 00:07:29

Using Data from O3 Mini – 00:09:20

Ad (Amplitude) – 00:10:13

Ad (Linear) – 00:10:45

Directing GibsonAI – 00:11:45

Connecting GibsonAI via MCP – 00:17:38

Role of Cursor – 00:21:10

Python Script Inserting Data – 00:26:56

Understanding Cursor Modes – 00:29:00

Ad (Maven) – 00:30:38

Our Dashboard Is Ready – 00:31:01

AI Agent That Analyzes Data and Recommends Actions – 00:33:44

The Most Important Thing Agent Is Doing – 00:41:46

Aakash’s Reaction to Output – 00:50:51

Role of CrewAI – 00:52:01

Why He Built GibsonAI – 00:56:35

Final Thoughts – 01:00:15

Key Takeaways

1. Production Over Prototypes - Stop building prototypes and start shipping production-ready AI employees. Gibson AI, Cursor, and CrewAI let you go from concept to production in hours. Harish's agent was backed by a scalable database handling 10,000 users day one—no rebuilding required.

2. Amplify, Don't Replace - Your next 10x gain comes from making existing teams superhuman. AI agents analyze dashboards 24/7 and draft personalized outreach, while human CS agents focus on high-touch relationships and strategic decisions.

3. Three-Tier Implementation Strategy - Follow this roadmap: dashboard → human-approved recommendations → autonomous actions. Start with AI insights humans review, then AI recommendations humans approve, finally autonomous execution for low-risk tasks.

4. Human-Loop Insurance - Human-in-the-loop is customer relationship insurance. Harish built approval workflows because random AI emails "will only make the problem worse." AI should amplify human judgment, not bypass it.

5. Proactive Beats Reactive - Proactive churn prevention beats reactive win-back by orders of magnitude. AI agents monitor engagement patterns and usage metrics to address churn risks before customers consider leaving.

6. MCP Integration Magic - MCP makes AI tools actually talk to each other. Harish could query databases, update schemas, and deploy changes directly from Cursor—seamless integration without manual tool switching.

7. Information Processing Automation - Any role that "ingests information and sends out information" is automatable. SDRs, recruiters, executive assistants—if it involves processing data and taking action, AI handles the heavy lifting.

8. Specialized Model Selection - Different models excel at different tasks. Harish used O3 Mini for planning, Claude Sonnet for coding. Match your model choice to the specific job rather than defaulting to popularity.

9. Day-One Infrastructure - Production-grade infrastructure eliminates the prototype-to-product