added more docs

source/api_definitions.py

@@ -237,6 +237,28 @@ class AgenticSystemExecuteResponseStreamChunk:
 
 
 class AgenticSystem(Protocol):
+    """The Llama 3 models released by Meta in July should not just be seen as a model, 
+    but really as a system starting the transition towards an entity capable of performing 
+    "agentic" tasks. By that we mean the following specific capabilities: 
+    1. Ability to act as the central planner -- break a task down and perform multi-step reasoning. 
+    2. Ability to perceive multimodal inputs -- text, images, files and eventually speech and video in later iterations.
+    3. Ability to use tools -
+        a. built-in: the model has built-in knowledge of tools like search or code interpreter
+        b. zero-shot: the model can learn to call tools using previously unseen, in-context tool definitions 
+
+    Multi-step tool-use concretely helps address many common problems with LLMs that users may 
+    face:
+    1. Finding accurate and up-to-date information. LLMs are limited to training data and knowledge cut off date. 
+    2. Current LLMs are limited in their understanding and reasoning abilities for solving more complex math problems, processing and analyzing data. Tools like code-execution or APIs like Wolfram can help bridge the gap.
+    3. Users may need help with a task that requires multiple tools to execute or a task that has multiple steps (e.g., graph plotting, etc.)
+    4. Our current LLMs are not able to generate other modalities (images, voice, video) directly. 
+
+Finally, we want the underlying LLM to remain broadly steerable and adaptable to use cases which 
+need varying levels of safety protection. To enable this, we want to shift safety into a two-tiered 
+system: 
+    1. a set of "always on" safety checks are always performed at the model level, and
+    2. a set of configurable safety checks which can be run at the overall system level.
+"""
 
     @webmethod(route="/agentic_system/create")
     def create_agentic_system(