Building an AI agent is easy. Deploying one that actually works inside a real business is not.
In this episode of the AI Guys Podcast, Lee Dickson and Rich Swier walk through a practical, end-to-end framework for building AI agents that deliver real value. The conversation breaks down what most teams get wrong, why AI agents fail after launch, and how to think about data, training, integrations, testing, and long-term maintenance the right way.
This episode serves as a crash course for teams looking to move beyond demos and proofs of concept and into production-ready AI agents that scale.
Key Takeaways
What an AI agent really is and how it differs from a chatbot
Why most AI agent failures come down to poor training and data prep
How to identify and structure your true source of truth
Why integrations are critical for autonomy and ROI
How to test, backtest, and simulate agents before launch
The importance of human-in-the-loop monitoring after deployment
How to choose the right first AI agent use case

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