content.raw: 🤖 𝗔𝗱𝗼𝗯𝗲'𝘀 𝗰𝗼𝗱𝗲-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁 𝗿𝗲𝗮𝗰𝗵𝗲𝘀 𝘁𝗵𝗲 𝘁𝗼𝗽 𝗼𝗳 𝗚𝗔𝗜𝗔 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱 - and their paper cites my work!
content.type: text
content.value: 🤖 𝗔𝗱𝗼𝗯𝗲'𝘀 𝗰𝗼𝗱𝗲-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁 𝗿𝗲𝗮𝗰𝗵𝗲𝘀 𝘁𝗵𝗲 𝘁𝗼𝗽 𝗼𝗳 𝗚𝗔𝗜𝗔 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱 - and their paper cites my work!
content.type: new_line
content.type: new_line
content.raw: 💡 Reminder: In short, Agentic systems are a vehicle in which you put your LLM to allow it access to the outside world.
content.type: text
content.value: 💡 Reminder: In short, Agentic systems are a vehicle in which you put your LLM to allow it access to the outside world.
content.type: new_line
content.type: new_line
content.raw: ➡️ The team of researchers at Adobe started from the idea that current agentic systems lack the ability to define their own tools. So they decided to make an agent that writes actions as code, thus allowing it to write python functions that can be re-used later as tools!
content.type: text
content.value: ➡️ The team of researchers at Adobe started from the idea that current agentic systems lack the ability to define their own tools. So they decided to make an agent that writes actions as code, thus allowing it to write python functions that can be re-used later as tools!
content.type: new_line
content.type: new_line
content.raw: Here's what the LLM generations can look like with the proper prompt:
content.type: text
content.value: Here's what the LLM generations can look like with the proper prompt:
content.type: new_line
content.type: new_line
content.raw: Thought: I need to access the excel file using a different method.
content.type: text
content.value: Thought: I need to access the excel file using a different method.
content.type: new_line
content.raw: Action:
content.type: text
content.value: Action:
content.type: new_line
content.code: def access_excel_file(file_path)
  	... # rest of the code (the agent does writes it, but I don't have room in this post)
  	return rows
content.lang: py
content.raw: ```py
  def access_excel_file(file_path)
  	... # rest of the code (the agent does writes it, but I don't have room in this post)
  	return rows
  ```
content.type: code_fence
content.type: new_line
content.type: new_line
content.raw: Then your system executes this and appends the observation to the agent's memory.
content.type: text
content.value: Then your system executes this and appends the observation to the agent's memory.
content.type: new_line
content.type: new_line
content.raw: Why is this code formulation better than classical tool use formulation as JSON? The paper explains:
content.type: text
content.value: Why is this code formulation better than classical tool use formulation as JSON? The paper explains:
content.type: new_line
content.type: new_line
content.raw: "Most existing work uses text or JSON as the representation of actions, which significantly lacks the two criteria mentioned earlier: generality and composability. In contrast, DynaSaur can utilize available actions or create new ones if necessary, using code as a unified representation. In principle, acting with code enables agents to solve any Turing-complete problem."
content.type: text
content.value: "Most existing work uses text or JSON as the representation of actions, which significantly lacks the two criteria mentioned earlier: generality and composability. In contrast, DynaSaur can utilize available actions or create new ones if necessary, using code as a unified representation. In principle, acting with code enables agents to solve any Turing-complete problem."
content.type: new_line
content.type: new_line
content.raw: The idea of using code is not new: in fact, we do it in transformers.agents (thus the citation that I got). They implementation adds further refinements, like using RAG to retrieve relevant functions before generating an action, which increases performance further.
content.type: text
content.value: The idea of using code is not new: in fact, we do it in transformers.agents (thus the citation that I got). They implementation adds further refinements, like using RAG to retrieve relevant functions before generating an action, which increases performance further.
content.type: new_line
content.type: new_line
content.raw: And they observe that code agents perform much better, reaching the top of GAIA leaderboard! 🥇
content.type: text
content.value: And they observe that code agents perform much better, reaching the top of GAIA leaderboard! 🥇
content.type: new_line
content.type: new_line
content.raw: Go take a look, it's really clear and informative!
content.type: text
content.value: Go take a look, it's really clear and informative!
content.type: new_line
content.type: new_line
content.raw: Paper added to my agents collection 👉 
content.type: text
content.value: Paper added to my agents collection 👉 
content.raw: https://huggingface.co/collections/m-ric/agents-65ba776fbd9e29f771c07d4e
content.id: m-ric/agents-65ba776fbd9e29f771c07d4e
content.type: collection
content.type: resource
content.url: https://huggingface.co/collections/m-ric/agents-65ba776fbd9e29f771c07d4e
rawContent: 🤖 𝗔𝗱𝗼𝗯𝗲'𝘀 𝗰𝗼𝗱𝗲-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁 𝗿𝗲𝗮𝗰𝗵𝗲𝘀 𝘁𝗵𝗲 𝘁𝗼𝗽 𝗼𝗳 𝗚𝗔𝗜𝗔 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱 - and their paper cites my work!
  
  💡 Reminder: In short, Agentic systems are a vehicle in which you put your LLM to allow it access to the outside world.
  
  ➡️ The team of researchers at Adobe started from the idea that current agentic systems lack the ability to define their own tools. So they decided to make an agent that writes actions as code, thus allowing it to write python functions that can be re-used later as tools!
  
  Here's what the LLM generations can look like with the proper prompt:
  
  Thought: I need to access the excel file using a different method.
  Action:
  ```py
  def access_excel_file(file_path)
  	... # rest of the code (the agent does writes it, but I don't have room in this post)
  	return rows
  ```
  
  Then your system executes this and appends the observation to the agent's memory.
  
  Why is this code formulation better than classical tool use formulation as JSON? The paper explains:
  
  "Most existing work uses text or JSON as the representation of actions, which significantly lacks the two criteria mentioned earlier: generality and composability. In contrast, DynaSaur can utilize available actions or create new ones if necessary, using code as a unified representation. In principle, acting with code enables agents to solve any Turing-complete problem."
  
  The idea of using code is not new: in fact, we do it in transformers.agents (thus the citation that I got). They implementation adds further refinements, like using RAG to retrieve relevant functions before generating an action, which increases performance further.
  
  And they observe that code agents perform much better, reaching the top of GAIA leaderboard! 🥇
  
  Go take a look, it's really clear and informative!
  
  Paper added to my agents collection 👉 https://huggingface.co/collections/m-ric/agents-65ba776fbd9e29f771c07d4e
reactions.reaction: 🔥
reactions.users: mkurman jessepisel sidbin thomwolf celinah AtAndDev
reactions.reaction: 🚀
reactions.users: sidbin John6666 thomwolf celinah AtAndDev