Key Components of AI Agents - A Practical Guide for Builders

Over the past year, our work with dozens of teams developing Large Language Model (LLM) agents has revealed a pattern: successful AI agents aren’t always built on complex systems. Instead, the best-performing implementations favor composable, transparent, and minimal patterns that scale with use—not complexity.

In this guide, we demystify the architecture of effective AI agents. Whether you’re building customer support bots, autonomous coding assistants, or anything in between, this breakdown will help you understand what goes into creating high-performing agentic systems.

What is an AI Agent?

An AI agent is not a monolith. Depending on who you ask, the term could refer to anything from a simple decision-based workflow to a fully autonomous LLM operating across multiple tools. To simplify, we define agents as systems where:

The LLM drives its own decision-making process.
The system can dynamically call tools or APIs.
It maintains control over how tasks are executed, based on context.

This is in contrast to workflows, which use predefined, rule-based logic paths where tools and outputs are hardcoded.

When Should You Use Agents?

While agents are powerful, they come with trade-offs:

Latency & cost increase with complexity.
Control & predictability decrease unless well-guarded.

Use agents only when tasks require flexibility, multiple reasoning steps, or unpredictable tool usage. For simpler or well-defined problems, chaining a few LLM calls with in-context learning or retrieval is often enough.

Core Architectural Patterns

AI agents are best understood by studying the patterns that make up their workflows. Below are five foundational patterns we’ve seen in production systems.

1. Augmented LLMs (The Foundation)

At the heart of all agents is an augmented LLM—an LLM enhanced with:

Memory
Retrieval
Tool use or API access

These capabilities allow the agent to:

Fetch relevant information (RAG systems)
Call external tools (search, file ops, APIs)
Remember past interactions (short-term memory)

2. Prompt Chaining

A basic, low-latency workflow where the output of one prompt feeds into the next.

Use Cases:

Step-by-step writing
Document summarization > rewrite > translate
DData extraction > cleanup > visualization

Best For: Clearly decomposable tasks with minimal branching.

3. Routing

The input is classified and then routed to specialized downstream agents or tools.

Use Cases:

Customer support triaging
Sending small queries to a lightweight model, and complex ones to a larger one

Best For: Systems that need cost control or performance optimization across varied input types.

4. Parallelization

Run multiple LLM calls simultaneously. It has two types:

Sectioning: Break the task into parallel subtasks.
Voting: Ask multiple agents the same question, then choose the best answer.

Use Cases:

Guardrails (offensive content screening + main response)
Multi-angle evaluations

5. Orchestrator-Worker

A central LLM (or controller) breaks a task into subtasks and delegates them to workers.

Use Cases:

Code editing across multiple files
Multi-hop search + synthesis

Best For: Tasks where subtasks aren’t predictable ahead of time.

6. Evaluator-Optimizer Loop

An LLM generates content. A second LLM evaluates and gives feedback. The loop continues until a threshold is met.

Use Cases:

Code review and refinement
Literary or marketing content generation

Autonomous Agents: The Final Pattern

Autonomous agents operate in loops, using tools, reasoning, memory, and plans until the task is done. They:

Self-correct based on tool output or environment
Decide when to pause, ask for help, or stop
Handle unpredictable paths and inputs

These are ideal for open-ended tasks like:

Writing code based on vague specs
Researching across diverse sources

Guardrails Required:

Set maximum turns or token use
Enforce stopping criteria
Log tool use for debugging

Real-World Applications

A. Customer Support Agents

Integrate with ticketing tools and databases
Generate dynamic responses
Automate common actions (refunds, lookups)

B. Code Assistants

Modify multiple files
Write tests and validate via outputs
Follow structured or natural language specs

Tool Design: The Hidden Bottleneck

Most agent failures aren’t due to bad prompts—they’re due to bad tooling interfaces.

Best Practices:

Prefer simple, natural formats (avoid escaped JSON if not needed)
Avoid requiring LLMs to count lines or manage state
Provide examples, docstrings, edge cases for each tool
Use absolute paths, not relative ones

Treat your tool interface like a developer API, with the LLM as your first-class user.

Closing Thoughts

Building effective AI agents doesn’t require a complex framework—it requires discipline, simplicity, and iteration. Start with low-latency prompt workflows, and only move to agents when the problem demands it.

Start simple. Scale when needed.
Use prompt chaining before full agents. Use tools that are intuitive. Add orchestration when the task demands it.

Core Principles for Builders:

Simplicity over abstraction
Transparent planning and reasoning
Robust, documented tool interfaces

By following these patterns and principles, you’ll be able to scale LLM-powered agents that are reliable, maintainable, and efficient.