How We Trained Your AI Agent, Part 2: Reinforcement Learning

To improve, AI agents need feedback on their performance (just like people do). This second phase is where the reinforcement learning begins.
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To improve, AI agents need feedback on their performance (just like people do). This second phase is where the reinforcement learning begins.
Sometimes data pipelines can seem like they’re working, not trigger any alerts, and actually have something massively wrong. This is where we recommend Impossible Reports.
Your approach to training an AI agent is just as important as your approach to training any new hire. While AI (obviously) learns differently than people do, setting it up for success with a solid foundation is crucial to making it an effective part of your team.
When the Yankees Entertainment Network wanted to launch a new DTC offer, they turned to Pickaxe to help.
Long before BritBox was streaming much loved shows to audiences around the world, they were looking for an experienced team to help them take the idea from concept to launch.
Nexstar needed a better way to understand user behavior, ad anomalies, and advertising performance across the hundreds of federated stations and websites that they operate from a mix of technology platforms built by them, or that they had acquired from multiple legacy parent companies over the years. The result was inconsistent and disparate reporting.
To improve, AI agents need feedback on their performance (just like people do). This second phase is where the reinforcement learning begins.
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