Agentic AI

(from Yeh course unless specified otherwise)

Links & Resources link

• Frameworks
  • Auto Jam – Simplifies complex AI workflows using pre-built templates.
  • Meta GPT & Crew AI – Enable highly customized multi-agent simulations, mimicking human roles
  • Wikipedia on multi agent systems
• Apps / Tools
  • Manus: https://manus.im
    • Mentioned in Yeh’s course
  • Crew AI: https://www.crewai.com

Background & Intro link

• Four behaviors of Agent behavior (from Yeh course)
  1. Reflection: Before responding, the agent assesses whether it needs more information.
  2. Tool Use: Agents access external resources, like checking live flight prices or retrieving updated data.
  3. Planning: They break down complex tasks into step-by-step solutions.
  4. Multi-Agent Coordination: Multiple agents work together like an efficient team, each handling different roles.

Yeh’s Framework (or “Equation”) link

• Yeh breaks down the Agent’s engagement and interaction into parts of a framework:
  • See: assume this is the ability for the Agent to see (process?) an initial prompt entered by the user
  • Think: assume this is a directive to the agent to engage in a particular way:
    1. Role: adopt a role as the Agent (“act as a helpful real estate agent”)
    2. Plan: step through a series of process steps to ultimately achieve the goal
  • Remember: use data to aid in the process, seems to come in a couple flavors:
    • Historical Data: assume this is past interactions the Agent has had with this use (?)
    • Contextual Data: assume this is application data (e.g. from a database or system) or third party data (e.g. stock prices)
  • Can: assume this is to execute on particular tasks or invoke other entities to execute tasks on the Agent’s behalf (e.g. initial a call, book an appointment)
• That whole package above ⬆️ is considered a big prompt to be executed by an LLM