Agent (AI Agent)
Definition
An AI agent is an autonomous system that uses an LLM as its reasoning engine to plan, execute multi-step tasks, and interact with external tools to accomplish complex goals.
Why It Matters
AI agents represent the next evolution beyond simple chat interfaces. While a basic LLM interaction is stateless (you ask a question, get an answer), an agent can break down complex tasks, use tools to gather information or take actions, remember context across interactions, and iterate toward a goal.
For AI engineers, agents unlock applications that were previously impossible. Instead of building rigid workflows where you hardcode every step, you can create systems that reason about problems and dynamically choose what actions to take. A support agent can search your knowledge base, look up a customer’s order, and process a refund, all from a single user request.
The shift to agentic systems is where implementation skills become critical. Anyone can call an LLM API. Building reliable agents that don’t hallucinate actions, handle edge cases gracefully, and operate within cost constraints requires real engineering.
Implementation Basics
Agent architectures typically follow a loop pattern:
1. Reasoning/Planning The LLM receives the user’s goal plus context about available tools. It decides what action to take next: call a tool, ask for clarification, or return a final answer. Chain-of-thought prompting helps the model break down complex tasks.
2. Tool Execution When the agent decides to use a tool (API call, database query, code execution), your system runs that action and returns the result to the LLM. This is where function calling provides structured, reliable tool invocations.
3. Memory and Context Agents need to track what they’ve done and learned. Short-term memory holds the current task context. Long-term memory (often using RAG) stores information across sessions.
4. Iteration The loop continues until the agent completes the goal, hits a stopping condition, or reaches a maximum step limit. That limit is crucial, because agents without guardrails can spiral into expensive, unproductive loops.
Start with simple, constrained agents. Give them 2-3 well-defined tools and clear stopping conditions. Expand capabilities only after you’ve proven reliability at the simple level.
Source
A survey of LLM-based autonomous agents examining planning, memory, and tool use as core agent capabilities.
https://arxiv.org/abs/2308.11432