this is a good point around taking advantage of model/api provider features i agree that prompt caching is great! we make sure to use it in deepagents! but that alone doesnt lock in – you can switch pretty easily, just a bit more costly things like encrypted or serverside
@hwchase17
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Understanding Agent Harnesses in LangChain Development
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@sarahwooders helped educate me on some of the lock in as well https://
blog.langchain.com/the-anatomy-of
-an-agent-harness/
… is a great blog by @Vtrivedy10 on agent harnesses i think the deep agent docs are pretty good as well -
Prompt Caching vs Vendor Lock-in in AI Agents
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i agree that prompt caching is great! we make sure to use it in deepagents! but that alone doesnt lock in – you can switch pretty easily, just a bit more costly things like encrypted or serverside compaction make it harder to do so. blackbox long term memory makes it impossible
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Open Standards for Managed AI Agents Architecture
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i agree there should be managed agents, i just think they should be built on open harnesses and open memory standards
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Open Standards for Managed AI Agents Architecture
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i agree there should be managed agents, i just think they should be built on open harnesses and open memory standards check out deep agents deploy:
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Thoth: Advanced Agent Harness with State-of-the-Art Memory
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check out thoth – agent harness with sota memory built on langgraph
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Code and Prompts: Testing and Evaluation Strategies
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Combination of code and prompts. Need tests and evals
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Memory Technology in AI Still Emerging and Unfamiliar
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Because memory is still so new, it’s not commonly known
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Harness emerges as top alpha opportunity in AI market
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Harness is where all the alpha is at right now