Agents: Building Autonomous, Tool-Using AI Systems

Until now, most GenAI systems we discussed respond once to a prompt.

Agents go a step further.

They can reason, decide, act, observe results, and repeat — without human intervention at every step.

Why Agents Exist

Real-world problems are not single-step.

Tasks like booking flights, analyzing logs, debugging systems, or answering multi-part questions require multiple actions.

Agents exist to handle these workflows autonomously.

How to Think Like an Engineer Before Building an Agent

Before writing any code, ask:

• What is the agent’s goal?
• What tools can it use?
• When should it stop?

An agent without boundaries is dangerous.

Core Agent Loop

Almost every agent follows this cycle:

• Receive goal
• Reason about next step
• Call a tool or model
• Observe result
• Decide whether to continue

This loop continues until the goal is satisfied.

Minimal Agent Skeleton

This is the simplest conceptual agent loop.

while not goal_completed:
    thought = llm(reasoning_prompt)
    action = decide_action(thought)
    result = run_tool(action)
    observe(result)
  

This structure separates reasoning from action.

Why Separation of Reasoning and Tools Matters

LLMs are good at reasoning.

They are not reliable at executing real-world operations.

Agents use LLMs for decisions and external tools for execution.

Example: Tool-Using Agent

Here, the model decides *what* to do, but the system decides *how* it happens.

def agent_step(state):
    response = llm(state["context"])
    
    if "search" in response:
        result = web_search(state["query"])
    elif "calculate" in response:
        result = calculator(state["expression"])
    else:
        result = response
    
    state["context"] += str(result)
    return state
  

The agent updates its own context after each action.

What Actually Happens During Execution

Step by step:

• The LLM generates a plan
• The system parses intent
• A tool executes safely
• The result feeds back into the agent

This prevents hallucinated execution.

Stopping Conditions

Agents must know when to stop.

Common stopping rules include:

• Goal achieved
• Maximum steps reached
• Error threshold exceeded

Why Infinite Agents Are Dangerous

Without limits, agents can:

• Loop endlessly
• Consume excessive tokens
• Trigger unintended actions

Production agents always have guards.

Agents in RAG Systems

Agents often orchestrate RAG pipelines:

• Decide whether retrieval is needed
• Choose data sources
• Refine queries iteratively

This makes retrieval dynamic instead of static.

How Learners Should Practice Agents

Effective practice involves:

• Building agents with one tool only
• Logging every step
• Manually inspecting decisions

Understanding failures is more important than success.

Practice

Quick Quiz

Recap: Agents transform LLMs into autonomous systems capable of reasoning, acting, and iterating toward goals.

Next up: Memory — enabling agents to retain and use past context effectively.