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.

• Characteristics:
  • No “intelligence”: these agents will follow the rules without adapting, learning, or any memory
  • Predictable behavior: agents will perform tasks as programmed, without deviation.
  • Limited scope: agents are helpful for repetitive, well-defined tasks but fail when encountering unexpected scenarios.
• Examples:
• Robotic Process Automation (RPA) for invoice processing or data entry
• Email autoresponders with rigid templates (yes, even your “out of office” replies or canned responses)
• Scripting tools that execute basic workflows think bash scripts or PowerShell scripts
• When to Use: These agents help when it comes to automating the cmd+c, cmd+v operation at scale and can be helpful for:
  • Routine Tasks: repetitive, predictable, and error-intolerant
  • Structured Data: anytime you’re copying or working with structured data from one system to another.
  • Zero need for adaptability: When changes or edge cases are rare or nonexistent.
• Workflow

Fixed Automation Agent Workflow

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.

• Characteristics:
  • Context-Aware: With the added perk of LLMs, these agents can process ambiguous inputs with contextual reasoning.
  • Rule-Constrained: Decisions are validated by predefined, explicit rules or thresholds.
  • Stateless: No long-term memory; each task is processed independently.
• Examples:
  • Email filters: Classify emails as spam, promotions, or primary with human-like nuance.
  • AI-enhanced content moderation: Thanks, automod! These agents are commonly found on social platforms where they flag offensive or harmful content within a specific context.
  • Customer Support Classification: Categorizes or routes tickets to the correct team based on vague or incomplete descriptions.
• When to Use: These agents thrive on “close enough is good enough” tasks.
  • Tasks requiring flexibility: with ambiguous inputs and traditional rule-based systems falling short.
  • High-Volume, Low-Stakes: Anytime you get a high throughput of ideas where occasional errors are tolerable.
  • Cost-Sentisitve Scenarios: With a lower level of complexity, computational and operational overhead is reduced.
• Workflow

LLM-Enhanced Agent Workflow

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.

• Characteristics:
  • Reasoning and Action: These agents mimic human problem-solving by thinking through a problem and executing the next step.
  • Multi-step workflows: Agents can handle multi-stage queries by breaking them into smaller, more actionable parts.
  • Dynamic Planning: With multi-steps comes a short memory, where strategy can be adjusted mid-task based on new data or feedback.
  • Basic Problem-Solving: Can assist with basic open-ended problem-solving with no direct solution path.
• Examples:
  • Language agents solving multi-step queries: What’s the best way to travel from Brooklyn to Midtown, optimizing for time or cost?
  • AI Dungeon Masters: Generate or adapt narrative-driven games or scenarios for tabletop games.
  • Project Planning Tools: Break down a goal into tasks and a schedule or milestone.
• When to Use: For tasks requiring strategic thinking, like planning a project or solving queries requiring the consideration of multiple factors.
  • Strategic Planning: Planning a route, event, or travel plan with a goal.
  • Multi-Stage Queries: when answers require multiple subtasks or reasoning chains.
  • Dynamic Adjustments: Tasks where conditions can change mid-task requiring flexible re-strategizing and evaluation.
• Workflow

Workflow for ReAct Agent — Reasoning meets Action

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.

• Characteristics:
  • Includes a RAG workflow: Has external knowledge provided through external database, API, or documentation with the addition of LLM-enhanced context.
  • Grounded in current knowledge: Pulls in real-time or domain-specific knowledge.
  • Reasoning and Action: Uses ReAct-style reasoning to break down tasks and dynamically retrieve information as needed.
  • Low Hallucinations: Designed for scenarios where correctness and relevance are non-negotiable.
• Examples:
  • Legal research tools accessing case law, legal databases, or other precedents.
  • Medical assistants reference clinical studies to provide guidance, advice, or additional diagnostics.
  • Technical Troubleshooting Agents assist tech support or software companies in resolving issues.
• When to Use: Anytime domain-specific accuracy is a must — consider using a ReAct + RAG agent.
  • High-Stakes Decision-Making: Use for tasks where factual precision and accountability are essential.
  • Domain-Specific Application: These agents draw from specialized knowledge sources or applications.
  • Dynamic Knowledge Needs: Anytime real-time updates or references are crucial (ie, stock market or tech policies) leverage these agents to maximize the impact.
• Workflow:

ReAct Reasoning plus action agent

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.

• Characteristics:
  • Multi-Tool Integration: Agents leverage APIs, databases, and software tools to perform tasks.
  • Multitaskers: These tools can handle and address workflows that require multiple steps and tools working together.
  • Dynamic Task Execution: Agents can switch tools dynamically between task requirements and resources.
  • High Automation Potential: Agents can reduce human involvement in repetitive or multi-stage processes.
• Examples:
  • Code Generation Tools (GitHub CoPilot, Sourcegraph’s Cody, Warp Terminal): Generate, review, and implement code using IDE Integrations and version control tools.
• Data Analysis Bots: Combining multiple APIs
• When to Use: For jobs requiring diverse tools and APIs in tandem.
• Workflow:

Tool Enhanced Agent Workflow

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.

• Characteristics:
  • Meta-Cognition: Agents will evaluate their thought processes and decision outcomes.
  • Explainability: The Agent’s output will provide transparent reasoning behind actions, often stating, “I chose X because it aligns with Y based on data Z.”
  • Self-Improvment: Agents learn from mistakes through feedback or self-assessment to improve performance over time. This allows the agents to recognize when they are uncertain or wrong and adjust accordingly.
• Examples:
  • AI that explains its reasoning, self-evaluating learning systems, or quality assurance (QA) agents.
• When to Use:
  • Tasks requiring accountability and improvement, like quality assurance or sensitive decision-making.
• Workflow

Self-Reflecting Agent Workflow

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.

• Characteristics:
  • Long-Term Memory: Agents store and recall historical interactions, preferences, and task progress.
  • Context-Aware Personalization: Decisions and actions are informed by user-specific data and history.
  • Adaptive Learning: Over time, these agents become more intelligent and more efficient as they refine their understanding of a user or process.
  • Consistency across interactions: Agents ensure continuity and coherence, even in long, multi-session workflows.
• Examples:
  • Project management AI with task history
  • Customer service bots tracking interactions
  • Personalized shopping assistants
• When to Use:
  • This is for tasks requiring individualized experiences and tailored recommendations or when tasks may span multiple interactions or sessions over an extended period.
• Workflow:

Memory-Enhanced Agents Workflow

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.

• Characteristics:
  • Active Environment Control: Agents can make decisions and execute actions that directly alter their surroundings.
  • Autonomous Operation: Agents operate independently within predefined limits, requiring minimal human intervention.
  • Feedback-Driven Adaptation: Agents continuously monitor the environment, adjust their actions, and learn from the results.
  • Complex System Integration: Agents interact with multiple systems simultaneously to manage complex tasks.
• Examples:
  • AutoGPT-like autonomous agents
  • Adaptive robotics
  • Smart Cities and Homes
  • Industrial Automation
• When to Use: When control over external systems or environments is required, such as in robotics, IoT systems, or interconnected systems may come into play, these Agents allow for large-scale digital tasks autonomously.
• Workflow:

Workflow of Environment Controlling Agents

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.

• Characteristics:
  • Autonomous Learning: Agents can refine their models or processes based on feedback, data, or environmental changes without requiring manual updates.
  • Adaptive and Scalable: Agents are able to adjust to changing conditions or new tasks seamlessly.
  • Evolutionary Behavior: Agents use techniques like reinforcement learning, genetic algorithms, or self-optimization to enhance performance over time.
  • High Risk / High Reward: These agents operate independently, requiring intervention only for high-level guidance or ethical oversight. This shows extreme revolutionary promise for research and innovation but also demands strict observability and monitoring.
• Examples:
  • Neural networks with evolutionary capabilities
  • Swarm AI systems
  • Autonomous Robotics
  • Financial prediction Models
• When to Use: Cutting-edge research and autonomous learning systems.
• Workflow:

Self-Learning Agent Workflow

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?

• Build your first agent: Check out this tutorial by Pratik Bhavsar on how to evaluate a LangGraph Agent for Finance Research.
• Learn more about LLM Observability: Read Conor Brondson’s take on the difference between LLM Monitoring and Observability.
• Stay curious: Join me on an adventure exploring this new world of AI Observability and Evaluations on the Galileo Blog, LinkedIn, or the Chain of Thought Podcast.

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 [email protected].