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#12 Implementing AI Agents—From Vision to Execution
Manage episode 459716318 series 3607822
A tartalmat a Andrew Psaltis biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Andrew Psaltis vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
In this episode of 'AI Demystified for Executives,' host Andrew Psaltis provides a comprehensive guide on implementing AI agents within organizations. The discussion includes aligning AI strategies with business goals, scoping use cases, and identifying tasks suitable for automation. Practical advice is offered on the build versus buy dilemma, pilot programs for quick wins, and developing internal expertise. The importance of selecting the right large language model (LLM), integrating tools, and continuous learning for AI agents is emphasized. Additionally, the episode addresses ethical considerations, regulatory compliance, and the development of governance policies to ensure responsible AI deployment. Real-world examples and research references are provided to support these strategies.
References:
StreamBench - Towards Benchmarking Continuous Improvement of Language Agents
Code - https://stream-bench.github.io/
Paper: https://arxiv.org/abs/2406.08747
GTA: A Benchmark for General Tool Agents
Code - https://github.com/open-compass/GTA
Paper - https://arxiv.org/abs/2407.08713
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