The topic of From LLMs to Agents: Build Smart AI Systems with Tools in LangChain is currently the subject of lively debate — readers and analysts are keeping a close eye on developments.
This is taking place in a dynamic environment: companies’ decisions and competitors’ reactions can quickly change the picture.
Tool → A function the AI can call
Agent → An AI system that decides which tool to use based on the question
Here you can see that the response output is in JSON string and not structured, so we need to add Output Format Structure to this.
Pydantic models provide the richest feature set with field validation, descriptions, and nested structures.
To display Name of the queried person, add Name in SearchOutputStructure definition
system Prompt controls how the model behaves overall instead of model guessing randomly.
Docstring → helps the model choose the right tool
system prompt → helps the model use tools correctly and produce the right output
In this blog, we moved beyond simple LLM usage and explored how to build AI agents using tools in LangChain.
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Why it matters
News like this often changes audience expectations and competitors’ plans.
When one player makes a move, others usually react — it is worth reading the event in context.
What to look out for next
The full picture will become clear in time, but the headline already shows the dynamics of the industry.
Further statements and user reactions will add to the story.