Agents, Assemble: A Field Guide to AI Agents

If you’ve been anywhere near the AI space lately, you’ve probably heard the buzz: AI agents are here to do the work, so you don’t have to. They can think for you, they can write for you, and they can sell for you. But what exactly are these agents? Are they here to replace you? (Spoiler: no, but they will make you look like a rockstar.)

Whether you’re diving into the world of autonomous agents for the first time or just need a quick refresher, this blog will break down the different levels of AI agents, their use cases, and the workflow running under the hood.

What Are AI Agents?

Think of an agent as your AI-powered virtual assistant, but instead of fetching your coffee (we wish), they execute tasks autonomously—or semi-autonomously—with varying levels of complexity. Agents are powerful tools capable of automating complex tasks and processes.

From sending simple automated responses to orchestrating complex workflows, agents are the worker bees of the AI hive. Before we offload our jobs to the robots and head to the beach, we must understand that not all agents are created equal.

Some are the digital equivalent of a Post-it note. Others? They're full-on self-serving robots, complete with self-reflection and memory. Feeling lost in the waves of innovation? Let this be your field guide covering different types of AI Agents.

Let’s explore how agents are built, what they look like in practice, and how to make the most of them. Already familiar with agents and putting them into production — help your digital colleagues operate their best with the help of Galileo.

Your Field Guide to Agents

Level -1: Fixed Automation – The Digital Assembly Line

This level of AI agents represents the simplest and most rigid form of automation. These agents don’t adapt or think—they just execute pre-programmed instructions. They are like assembly-line workers in a digital factory: efficient but inflexible. Great for repetitive tasks, but throw them a curveball, and they’ll freeze faster than Internet Explorer.

Level 0: LLM-Enhanced – Smarter, but Not Exactly Einstein

These agents leverage Large Language Models (LLMs) to provide contextual understanding and handle ambiguous tasks while operating within strict boundaries. LLM-Enhanced Agents balance intelligence and simplicity, making them highly efficient for low-complexity, high-volume tasks.

Level 1: ReAct – Reasoning Meets Action

ReAct agents combine Reasoning and Action to perform tasks that involve strategic thinking and multi-step decision-making. They break complex tasks into manageable steps, dynamically reasoning through problems and acting based on their analysis. These agents are much like your type-A friend who plans their weekend down to the minute.

Level 2: ReAct + RAG – Grounded Intelligence

ReAct + Retrieval-Augmented Generation (RAG) agents combine reasoning, action, and real-time access to external knowledge sources. This integration allows them to make informed decisions grounded in accurate, domain-specific data, making them ideal for high-stakes or precision-critical tasks (especially when you add evaluations to the table). These agents are your ultimate trivia masters with Google on speed dial.

Level 3: Tool-Enhanced – The Multi-Taskers

Tool-enhanced agents are versatile problem solvers integrating multiple tools, leveraging APIs, databases, and software to handle complex, multi-domain workflows. They combine reasoning, retrieval, and execution for seamless, dynamic task completion. Think of them as tech-savvy Swiss Army knives capable of combining reasoning, retrieval, and execution seamlessly.

Level 4: Self-Reflecting – The Philosophers

These agents think about their thinking. Self-reflecting agents introduce meta-cognition—they analyze their reasoning, assess their decisions, and learn from mistakes. This enables them to solve tasks, explain their reasoning, and improve over time, ensuring greater reliability and accountability.

Level 5: Memory-Enhanced – The Personalized Powerhouses

Give an agent a little memory, and you have the ultimate personal assistant. Memory-enhanced agents bring personalization to the forefront by maintaining historical context and remembering user preferences, previous interactions, and task history. They act as adaptive personal assistants, providing tailored experiences and continuous, context-aware support. These agents remember your preferences, track your history, and theoretically — would never (ever) forget your coffee order.

Level 6: Environment Controllers – The World Shapers

Environment-controlling agents extend beyond decision-making and interaction—they actively manipulate and control environments in real time. These agents are equipped to perform tasks that influence the digital landscape or the physical world, making them ideal for automation, robotics, and adaptive systems applications. Think smart thermostats, but on steroids.

Level 7: Self-Learning – The Evolutionaries

The holy grail of AI agents: those that can improve themselves over time. They learn, adapt, and evolve without needing constant babysitting. These agents improve themselves over time, learning from interactions, adapting to new environments, and evolving without constant human intervention. They combine elements of reasoning, memory, environment control, and self-reflection with autonomous learning capabilities to adapt and optimize their behavior. Are they the future of AI? Potentially. Are they also terrifying? Without evaluations, observation, regulation, and oversight, very much so.

So, Why Does This Matter?

Here’s the kicker: AI agents can drastically reduce the busy work, freeing you to focus on higher-level problem-solving. However, it’s not just about saving time—agentic tools will enable folks to unlock new possibilities, from streamlining workflows to augmenting human decision-making.

And yes, even the fanciest AI agent still needs a human in the loop. We must keep observability and evaluations in mind to ensure that we’re building with integrity and in the direction we want at the end of the day.

The Road Ahead: Swarms, Multi-Agent Workflows and Beyond

We’re on the cusp of something big: networks of AI agents—swarms—working together to tackle massive, interconnected challenges. Agents will work together like a synchronized orchestra (or at least a decent garage band). Swarms of specialized AI agents, connected through multi-agent workflows, will tackle complex, interconnected challenges that no single system could handle alone. Imagine marketing automations, seamlessly passing insights to data analysis bots, nudging project management agents to adjust timelines—all happening autonomously and efficiently. These systems will demand robust communication protocols, adaptive task management, and a clear focus on transparency and security.

What’s Next?

As always, think about how you can use these tools to augment human creativity and intelligence—not replace it. (And if it can remind you to drink more water, even better.)

Happy agent-building, engineers! Now go forth and build like the superhero you are. 🚀

Want to geek out more about agents or how we’ll build a better future with a little extra help from our agentic friends? Feel free to reach out to me on LinkedIn, Twitter, BlueSky, or drop me a good ol’ fashioned email at erin@rungalileo.io.