Foundation Core Concepts

Introduction to AI Agents

In the rapidly evolving landscape of artificial intelligence, AI agents represent a significant leap beyond conventional software applications. At their core, agents are software entities designed to perform autonomous actions by:

• Observing their environment
• Making decisions based on observations
• Taking actions to achieve specific goals
• Learning from outcomes

The Evolution of AI Systems

AI technologies have progressed significantly. Understanding the stages helps clarify how agents go beyond traditional software:

1. Traditional Applications

   • Rely on predefined rules or fixed logic.
   • Perform predictable tasks with minimal flexibility.
   • Example: Basic command-line programs or simple scripts.

2. AI-Enhanced Applications

   • Use machine learning or large language models (LLMs) to provide better responses.
   • Still mostly follow predefined flows, with small amounts of context awareness.
   • Example: Chatbots that generate dynamic replies but can’t independently plan multi-step processes.

3. Agentic Systems

   • Operate with a high degree of autonomy, adapting to changing conditions.
   • Can create and refine their own plans over multiple steps.
   • Example: A research bot that iteratively retrieves information, analyzes it, and updates its plan to find the best solution.

Understanding System Types

Modern AI implementations generally fall into three categories:

1. Automations

   • Execute predefined, rule-based tasks

   • Follow fixed decision trees, best for deterministic tasks

   • Best for: Repetitive, well-defined tasks

   • Example:

     def process_request(type):
           if type == "email":
              send_email()
           elif type == "report":
              generate_report()

2. AI Workflows

   • Combine LLMs with predefined processes

   • Follow structured but flexible paths

   • Best for: Complex but predictable tasks

   • Example:

     async def handle_customer_query(query):
           intent = await llm.classify_intent(query)
           response = await llm.generate_response(intent)
           if need_human_review(response):
              return escalate_to_human()
           return response

3. True AI Agents

   • Dynamically direct their own processes

   • Maintain control over task execution

   • Best for: Open-ended, adaptive tasks

   • Example:

     class ResearchAgent:
           async def solve_problem(self, query):
              plan = await self.create_plan(query)
              while not self.goal_achieved():
                 next_step = self.determine_next_step(plan)
                 result = await self.execute_step(next_step)
                 plan = self.adjust_plan(result)
              return self.compile_results()

Core Components of an Agent

A generic agent architecture, consists of several key components:

1. Model Layer

   • Central decision-making engine
   • Processes input and context
   • Generates reasoning and plans

2. Orchestration Layer

   • Manages the execution flow
   • Coordinates tool usage
   • Monitors progress towards goals

3. Memory System

   • Short-term working memory
   • Long-term knowledge storage
   • Context retention

4. Tool Integration

   • External API connections
   • Data processing capabilities
   • Action execution interfaces

The ReAct Framework

AI agents typically operate using the ReAct (Reason-Action) framework, which follows a cyclical pattern:

Framework Components:

1. Thought

   • Internal reasoning about the current state
   • Analysis of available information
   • Planning next steps

2. Action

   • Execution of chosen steps
   • Tool usage and integration
   • External system interaction

3. Observation

   • Gathering results
   • Analyzing outcomes
   • Updating understandingd