You use ChatGPT.
But being an AI-powered PM means also using AI agents.
In my slack poll, only 2% of you said you use AI agents for productivity.
So I want to break that down and make it dead clear: 1) why you should use AI agents and 2) how you should build them.
So in today’s episode, I’ve brought in Jacob Bank, former Director of PM at Google (Gmail, Calendar) and now CEO of the AI agent builder company Relay.app.
He shares all his secrets - his 12 agent EA, his 40 agent marketing team, and his agent to synthesize agent updates.
I hope you enjoy.
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⏰ Timestamps:
00:00 Intro
01:49 Meet Jacob: The AI Agent Pioneer
02:18 Managing Agent Notification Overload
04:13 Current AI Agent Limitations Explained
06:59 Relay's Growth & Bootstrap Strategy
10:25 The Bull Case for AI Agent Market
15:14 Ads
17:18 Who's Adopting AI Agents Fastest
20:46 Top 10 AI Agent Use Cases for PMs
22:48 Choosing the Right Agent Platform
28:44 Jacob's 55-Agent Marketing Team Breakdown
31:47 Ads
34:45 Building AI Agents Into Your Product
38:10 MCP Protocol & Future of APIs
41:43 Why Jacob Left Google Director Role
44:25 Brutal Truth: PM-to-Founder Reality Check
48:52 Outro
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Key Takeaways
1. Real agents need five components working together
Intelligence (LLM), Knowledge (proprietary data), Memory (interaction history), Tools (APIs that change world state), Guardrails (validation and safety). Most "agents" are just LLM wrappers missing the other four components.
2. No-code tools compress development cycles 100x
Langflow + v0 enable 30-minute prototype-to-production workflows. Build competitive analysis agents live on screen. The cost barrier disappeared while customers still can't articulate what they want until they see it working.
3. Cart-before-horse development beats traditional PM process
Skip months of research. Build working prototypes first, test with real users, iterate based on feedback, then write focused PRDs. Speed beats perfection when technology moves this fast.
4. FAANG salaries reflect desperate demand
Level 6-7: $750K+ total compensation. Level 8+: $1.2-1.5M total compensation. OpenAI: $900K+ for comparable roles. Growth rate: 2-3x faster than traditional PM positions because supply can't meet demand.
5. The proven 18-month roadmap works systematically
Months 1-3: master fundamentals, build working agent solving personal problems. Months 4-9: scale to 10-20 real users, learn evaluation systems. Months 10-18: contribute to open source, prove you outperform existing team members.
6. Vibe coding interviews test product judgment, not technical skills
Demonstrate structured thinking through prompt engineering, incorporate user insights in second iterations, show measurement frameworks in third iterations. They're evaluating product sense through AI interactions.
7. Target problems with three characteristics for defensibility
Domain expertise you already possess, unstructured data requirements, complex decision-making processes. This combination creates competitive moats that simple AI features cannot replicate easily.
8. Evaluation frameworks must come before coding
Measure usage adoption, outcome achievement, and user experience satisfaction. Include speed metrics (prompts to completion) and accuracy benchmarks (goal success rates) to validate that AI actually democratizes building.
9. Company cultures reward different AI approaches
Microsoft: innovation without business constraints. Amazon: profit-focused execution speed. Meta: collaboration with world-class engineering talent. Google: user experience perfection with iteration time.
10. Essential PM tools everyone needs
Customer interaction analyzer across all channels, AB testing simulator using AI personas at scale, document reviewer trained on your manager's specific feedback patterns an
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Related Content
Related Podcasts:
* He built the top AI agent startup
* AI Agents for PMs in 69 Minutes
* How to Build AI Agents (and Get Paid $750K+)
Realated Newsletters:
* AI Agents: The Ultimate Guide for PMs
* Ultimate Guide to AI Prototyping Tools
* How to Land a $300K+ AI Product Manager Job
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