most ppl talking about ai agents have never built one
here is the actual architecture rn tool-calling agent = llm brain + function registry + execution loop you define tools as structured schemas. the model picks which tool to call and passes args. your runtime executes it and feeds the result back thats the whole loop. no magic modern frameworks like langchain or openai function calling handle the routing. cloud ml platforms like vertex or bedrock handle inference scaling so you dont burn cash on idle gpus qwen 3.5 small models - 0.8B to 9B params - can run tool-calling locally on a single node. same foundation as the big models just less compute the edge isnt knowing ai exists. its knowing how to wire tools into a loop that actually ships output if you are building agents rn drop what framework you are using.
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most ppl talking about ai agents have never built one
here is the actual architecture rn
tool-calling agent = llm brain + function registry + execution loop
you define tools as structured schemas. the model picks which tool to call and passes args. your runtime executes it and feeds the result back
thats the whole loop. no magic
modern frameworks like langchain or openai function calling handle the routing. cloud ml platforms like vertex or bedrock handle inference scaling so you dont burn cash on idle gpus
qwen 3.5 small models - 0.8B to 9B params - can run tool-calling locally on a single node. same foundation as the big models just less compute
the edge isnt knowing ai exists. its knowing how to wire tools into a loop that actually ships output
if you are building agents rn drop what framework you are using.