Introduction to Autonomous Agents

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1. Chains vs. Agents

• Static Chain: The developer hardcodes the workflow.
  • Example: User Input $\rightarrow$ Prompt Template $\rightarrow$ LLM $\rightarrow$ StrOutputParser. The model has no choice but to follow this exact path.
• Autonomous Agent: The model dynamically routes execution based on the user’s goal.
  • Example: If a user asks “Check the price of Apple stock and email it to my manager,” the agent decides to:
    1. Call a stock price search API tool.
    2. Read the results.
    3. Call an email API tool.
    4. Formulate a final response.

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2. Core Components of an Agent

              ┌──────────────────────────┐
              │           LLM            │
              │    (Reasoning Engine)    │
              └──────┬────────────┬──────┘
                     │            │
         ┌───────────▼──┐      ┌──▼───────────┐
         │   Memory     │      │   Planning   │
         │ (Short/Long) │      │ (Reflection) │
         └──────────────┘      └──────────────┘
                     │
         ┌───────────▼──┐
         │    Tools     │
         │ (APIs/Search)│
         └──────────────┘

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2.1 Planning

• Subgoal Decomposition: Breaking a large, complex task into smaller, manageable milestones.
• Reflection & Self-Correction: Analyzing tool execution outputs. If a tool returns an error (e.g. API authentication failure), the agent alters its plan and attempts an alternative route instead of failing.

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2.2 Memory

• Short-Term Memory: In-context conversation history. Allows the model to remember preceding turns in a chat session.
• Long-Term Memory: Storing past outputs and vector embeddings in a database, allowing the agent to recall information across days or weeks.

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2.3 Tools

• Interfaces that allow the LLM to interact with the physical world.
• Examples: Web search engines (Tavily), calculation modules, databases, shell execution tools, or API endpoints.

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3. The ReAct Design Pattern

User Input ──> Thought ──> Action ──> Observation ──> Thought ──> Final Answer

• Thought: The model reasons about the current state of the problem (e.g., “I need to find the population of Paris. I should use the search tool.”).
• Action: The model triggers a specific tool with generated arguments (e.g., Calling search_web("Paris population")).
• Observation: The application executes the tool, gets the raw output, and feeds it back to the model (e.g., Output: "2.1 million").
• Thought / Final Answer: The model evaluates the observation. If the goal is met, it outputs the final answer to the user.

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4. Practice Exercises

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Practice 1: Identifying Agentic Capabilities