From LLMs to Agents

You now understand how LLMs process text, use context windows, and call functions. But an LLM on its own is reactive — it waits for a prompt and responds once.

What happens when we put an LLM in a loop, give it goals, and let it decide what to do next? That's an agent. This section shows how the pieces you've learned combine into autonomous systems.

The Observe-Think-Act Cycle

Every AI agent -- regardless of framework, model, or use case -- runs on the same fundamental loop. Understanding this loop is the single most important concept in agentic AI.

The Loop

1. Observe -- The agent reads its current context: user instructions, tool outputs, environment state, and prior conversation history.
2. Think -- The Large Language Model (LLM) reasons about what to do next. It weighs the goal against what it has learned so far and selects a strategy.
3. Act -- The agent executes an action: calling a tool, writing output, requesting human input, or deciding the task is complete.

The loop repeats until the agent reaches its goal or determines it cannot proceed.

Why This Matters

Traditional software follows a fixed path: input goes in, output comes out. An agent is different. Each iteration of the loop can change direction based on what the agent discovers. A coding agent might:

• Observe: Read a failing test and its error message
• Think: Determine the root cause is a missing null check
• Act: Edit the source file and re-run the test
• Observe: See the test now passes
• Think: Decide the task is complete

This adaptive behavior is what separates agents from static pipelines. The agent does not need every step pre-programmed -- it figures out the path as it goes.

The Loop Has Guardrails

In production systems, the loop is bounded by constraints:

These guardrails are not optional. Without them, an agent can run up costs, get stuck in loops, or take destructive actions.

A Critical Note on Safety

Before we go further, pause and consider this: agents that can take actions can also cause harm if unchecked.

An agent with database access can delete records. An agent with email access can send messages to customers. An agent with file system access can overwrite important data. These aren't hypothetical risks — they're direct consequences of giving AI systems the ability to act.

Guardrails, human oversight, and bounded permissions aren't optional extras — they're architectural requirements.

As you learn about tools, RAG, and multi-agent systems in the sections ahead, keep this principle in mind: every new capability needs a corresponding control. More tools means more potential for misuse. More autonomy means more need for oversight. More power means more responsibility in the system design.

We'll cover safety and governance in depth in a dedicated section, but this mindset should inform everything you build from here forward. The question isn't just "can the agent do this?" — it's "what happens when it does this wrong, and how do we limit the damage?"

Key Takeaways

Before you move on, here's what to remember from this section:

• The agent loop — observe, think, act — is the core pattern driving all agent behavior
• Agent patterns vary by need: ReAct for adaptability, Planner for structured tasks, Tool-User for simple actions
• Architecture components include the LLM (brain), tools (hands), memory (context), and orchestration (control flow)
• Not everything needs an agent — use agents when tasks require multi-step reasoning, tool use, or adaptive behavior
• Guardrails are essential — iteration limits, loop detection, and evaluation steps prevent runaway costs and errors