Agentic topics
Agentic topics
Different names
- function calling
- agents
- planning
- Take a number of actions in sequence
- Agent takes more and more actions, the results of those actions are fed back to the LLM
General observation
- Planning and reasoning is still not at the level it?s needed to be for real-world tasks.
Issues
- prompt being passed into the LLM simply does not contain enough information for the LLM to make a reasonable decision
- Lack of visibility to see intermediate data easily to evaluate what is not working
- Test data sets that can be evaluated to understand the calculated flows because the underlying mechanism is brittle
- randomness and unpredictability.
Cognitive Architecture
- It is a term that is used to "describe" the "body" or "brain" of your "genai" application.
- Particularly used in agentic systems as there is "reasoning" by LLM using a number of strategies including Prompt directing the LLM decisions to call which step next.
- It is a way stemming from the desire to classify what kind of logic your have in your genai app and give it a name.
Examples
- Straight forward question answer
- basic RAG (no agentic)
- Manual flows to control multiple calls to LLMs
- Agentic RAG where "the routing logic is in the prompt" with multiple calls to the LLM and the APIs
- Long term planning with goals
Few Advanced Cognitive architectures
- Plan and Solve
- Reflexion architecture
- Or in combination with Domain specific "controlled" flows/architectures
Domain specific cognitive architecture: A manual (graph) flow in other words
- And rather than tell the LLM to do in the prompt
- - where it may ignore it, not understand it, not have all the details -
- they told the system to do it
- by constructing a domain specific cognitive architecture.
Essentials
- Takes LangChain as an example
- Explains: Reason + Act
- Introduces "Thought, Action, Observation"
- A good article