Building AI Agent Systems: A Deep Dive into Architecture and Intuitions

Author(s): Prashant Kalepu

Originally published on Towards AI.

2024 has truly been marked as the year of AI agents, and it’s not hard to understand why. But what exactly are AI agents, and why do they represent the next leap forward in the world of generative AI? In this blog, I’ll be diving deep into the architecture of Agentic AI systems, decoding how they function and why they matter.

While there’s no one-size-fits-all blueprint for AI agent architecture, let’s embark on a journey to brainstorm a generalized design — guided by our intuition and the shifts shaping the AI landscape. So, grab your coffee and get comfortable as we explore this fascinating topic. As you read, I encourage you to visualize the architecture as a dynamic, evolving structure — almost like a diagram unfolding in your mind.

To fully grasp this concept, we first need to take a step back and examine the seismic shifts in the AI world, particularly the transition from monolithic models to more modular, compound AI systems. This shift lays the foundation for AI agents, and understanding it will give us the clarity to approach the design of their architecture.

Shift: From Monolithic Models to Compound AI Systems

Traditional AI models, while powerful, often face limitations stemming from the data they’ve been trained on. These models are confined by their training data, which restricts the scope of what they know and the types of tasks they can handle. Additionally, adapting these models to new tasks or domains is not a simple process. Fine-tuning may help, but it demands substantial resources, time, and fresh data — making them far from flexible in the dynamic real world.

Problem Statement

Let’s put this into perspective with a real-world example. Imagine you’re following a fitness plan and want to know how many workout sessions you have left in the month. If you rely on a standalone AI model, the answer you get is likely to be inaccurate, because the model has no access to the specifics of your fitness routine, like your workout schedule or progress. It simply doesn’t know enough about your situation.

But what if we built a more sophisticated system around the model? Suppose we integrated the model with a live fitness tracking database. Now, the AI can query the database to fetch the correct, up-to-date information. The result could be something like: “You have 8 workout sessions left this month.” This would be a far more accurate and context-aware answer, because it’s based on real-time data from your personalized fitness plan.

This is the essence of a compound AI system — a system where the AI model doesn’t operate in isolation, but instead collaborates with external systems, databases, or APIs to deliver precise, contextually relevant responses. Unlike monolithic models, which work with predefined data, compound AI systems dynamically interact with the world, providing answers that are not only accurate but personalized.

What Makes Compound AI Systems Effective?

Unlike traditional AI models, which operate in isolation and provide a fast response in milliseconds, compound AI systems take a modular approach, leveraging a variety of tools such as databases, external programs, and APIs. These components work together to think, provide context, plan and fetch real-time information, to enhance the model’s capabilities.

This modular design offers a significant advantage over the conventional approach of relying solely on a single model to generate answers. Instead of being confined to the limits of one model’s training data, compound AI systems integrate diverse resources, enabling them to adapt and respond to a broader range of tasks.

The Triad of AI Agent

AI agents are designed to efficiently complete tasks, but their true effectiveness stems from their ability to navigate through 3 critical pillars:

1. Reasoning
   At the heart of every AI agent lies its ability to reason — the process of breaking down complex problems into smaller, more manageable tasks and finding solutions step-by-step. Reasoning involves understanding the problem and recognizing patterns. It’s the cognitive ability that enables the agent to strategize and decide on the best course of action to solve a given task.
2. Acting
   Once the AI agent has reasoned through the problem, the next step is to act. This is where the agent transitions from planning to execution. The agent can use a variety of external tools — such as APIs, databases, or even additional models — to carry out its decisions. For example, the agent might query a database to fetch relevant information, use an API to send a request, or invoke another model to perform a specialized task, like translating text or solving a complex mathematical equation. Acting allows the agent to turn its reasoning into tangible outcomes.
3. Memory
   The third pillar is memory. Memory allows the agent to retain information over time, storing internal logs of its reasoning process and keeping track of past interactions. This gives the agent the ability to adapt and personalize its behavior, drawing from previous experiences to improve future performance. With memory, the agent doesn’t start from scratch with each new task — it can recall past decisions, learn from prior mistakes, and fine-tune its actions based on historical data, ultimately leading to more efficient and accurate problem-solving.

Together, these three pillars — reasoning, acting, and memory — work in harmony to give AI agents the capacity to handle tasks intelligently and autonomously. They enable the agent not only to solve immediate problems but also to learn, adapt, and optimize its performance over time, making them an indispensable part of advanced AI systems.

The Core Architecture Breakdown

Before we get into the nitty-gritty details, take a look at the diagram below that illustrates the architecture. This will help visualize the design and make the components easier to understand.

So, for better understanding, let’s use the fitness example we discussed in the starting of the blog: “How many workout sessions do I have left in the month?”

Reasoning Block

The reasoning block is based on the first principle of AI agents: Reasoning. This block is made up of two layers (Decomposition Layer and Planning Layer) + LLM (core of the agent).

Large Language Model (LLM)

At the heart of the Reasoning block, Large Language Models (LLMs) play a pivotal role. These models, trained on vast datasets, bring unparalleled capabilities for context understanding, logical deduction, and complex problem-solving. They serve as the foundation for the Decomposition and Planning layers within the reasoning block.

Why LLMs Are the Core of Reasoning?

1. Contextual Understanding: LLMs excel in grasping the context behind complex tasks, making them indispensable for accurate task decomposition and planning.
2. Adaptability: They are dynamic and capable of adapting to diverse inputs, enabling seamless integration with different tools.
3. Cognitive Processing: LLMs mimic human cognition by reasoning through tasks step-by-step, ensuring that even ambiguous or open-ended queries are handled effectively.

Decomposition Layer

Given a complex task, the Decomposition Layer is responsible for breaking it down into smaller, more manageable subtasks. Think of it as a problem-solving approach where large problems are chunked into bite-sized pieces, making them easier to tackle one step at a time.

If the task is, “How many workout sessions do I have left in the month?”, the decomposition layer splits this into smaller questions:

• What is the total number of workouts scheduled for the month?
• How many workouts have been completed so far?
• How many workouts are left?

At this point, we have a clear breakdown of the task into three smaller subtasks, which makes it easier to handle.

Planning Layer

Once the subtasks are identified, the Planning Layer steps in. It’s the agent’s strategy room — where decisions are made about which tools or resources best fit each subtask. This planning is done by reasoning about the task at hand, the context, and the available tools, essentially creating a blueprint for the next steps.

For instance:

• What is the total number of workouts scheduled for the month? — (fitness API query tool)
• How many workouts have been completed so far? — (Database query)
• How many workouts are left? — (Calculation tool)

Execution Block

The Execution Block embodies the second core principle of AI agents: Acting. This block bridges the gap between reasoning and tangible outcomes by leveraging a set of tools that execute the tasks defined in the planning layer.

Task Execution Layer

The Task Execution Layer is the engine that turns plans into action. It consists of a set of programs or functions that can dynamically invoke the right tool or service based on the specific task and plan created in the planning layer.

For instance, if the agent’s plan requires querying a database, here’s how it would work:

1. API/ORM Call: The agent uses a pre-defined function or API endpoint to query the database.
2. Data Fetching: Once the request is made, the relevant data is fetched.
3. Post-Processing: The data is processed and passed back for further use, such as generating a response for the user.

This layer is designed to be flexible and modular, enabling the agent to interact dynamically with a variety of tools depending on the task at hand.

In the execution block, you’ll typically find a range of tools that cater to different needs. Some common tools include:

• Databases: For retrieving structured data (e.g., SQL, NoSQL).
• APIs: For fetching data or interacting with third-party services (e.g., RESTful APIs, GraphQL).
• File Systems: For reading/writing data from storage (e.g., cloud or local file systems).
• Machine Learning Models: For performing predictions or specialized tasks (e.g., NLP models, computer vision models).
• Web Scraping Tools: For extracting data from websites (e.g., BeautifulSoup, Selenium).

These tools are essential for enabling the agent to perform complex tasks that require external data or processing capabilities.

The Feedback Loop

Once the agent executes a task, it doesn’t just stop there. It enters a feedback loop that evaluates the outcome of its action.

1. Outcome Evaluation: The agent compares the results of its action (e.g., the data fetched from an API) with predefined expectations or success criteria.
2. Error Detection: If the results are inaccurate or incomplete, the agent flags it and revisits its reasoning and actions.
3. Re-planning: Based on this evaluation, the agent re-plans the subtasks and executes a refined strategy to improve the outcome.

This feedback mechanism is essential for iterative improvement. By constantly refining its approach based on real-time data and outcomes, the agent ensures that its actions evolve to be more accurate and efficient over time.

The key challenge is how to implement this feedback mechanism in a live system without disrupting the user experience. Here’s the solution:

1. Validation Criteria: The agent can use predefined success indicators or validation criteria. For example, when querying a database, it can check whether the fetched data meets certain expectations — such as completeness, or relevance.
2. Dynamic Error Checking: The agent can employ error-checking mechanisms that constantly monitor real-time performance and outcomes.
3. Adaptive Learning: When the agent identifies a discrepancy or suboptimal outcome, it adjusts its approach — whether by querying different tools, re-adjusting parameters, or seeking additional information.

This process happens dynamically, without manual intervention, ensuring that the agent’s decision-making process gets continuously refined, making it more intelligent and effective over time.

Memory Block

The Memory Block is the final cornerstone of an AI agent’s architecture. It allows the agent to retain past experiences, improving its ability to make informed decisions and adapt over time. Memory, in this context, refers to a repository where the agent stores information about previous interactions, reasoning processes, and even task outcomes.

Memory Storage and Representation

Memory in an AI agent isn’t just a simple log; it’s structured, allowing for efficient retrieval and use. Typically, the memory is stored in a structured format such as a knowledge base, where each piece of information is stored in a way that makes it easy to access and analyze.

• Vector Embeddings: These are commonly used to represent learned experiences or actions. By converting data into vector representations, the system can quickly find patterns, relationships, and insights from past actions.
• Dynamic Memory: The memory isn’t static; it updates based on new inputs, actions, or experiences. This allows the agent to adapt to new situations without needing to start from scratch each time.

Types of Memory: Short-Term vs. Long-Term

The design of the agent’s memory depends on the use case and task requirements. There are generally two types of memory:

1. Short-Term Memory: This is like a temporary cache, storing only the most recent interactions and data relevant to the ongoing task. The retention period is usually short, focusing on current activities.
2. Long-Term Memory: This type stores information that the agent can reference across sessions. It helps the agent retain valuable insights or lessons learned from past interactions, even after the task is completed or the session ends.

The duration of each memory type can be controlled by relevance or importance to the current task. For example, if a task requires recalling previous decisions or interactions, the agent can access its long-term memory to inform its current actions.

Personalization and Adaptation

One of the primary benefits of the memory block is the personalization it provides. By remembering past experiences, the agent can adapt over time, tailoring its responses or actions based on what it has learned.

• Task-Specific Memory: The agent can remember previous tasks, solutions, and mistakes. This helps the agent make better decisions in future tasks by leveraging past knowledge.
• User-Specific Memory: If the agent is interacting with different users, it can store user preferences or past interactions, ensuring that future responses are more tailored and contextually aware.

As the agent gathers more experiences, its decision-making ability improves, making it more effective and efficient in solving complex tasks. Memory is, therefore, what allows the AI agent to move from simply “reacting” to situations to actively learning and adapting over time.

Real-Time Memory Updates

In some cases, an agent’s memory may need to be updated or refined in real-time, especially when the agent encounters new data or evolving scenarios. For instance, if the agent learns a new fact during a conversation, it can immediately update its memory, enabling it to apply this new knowledge in future interactions.

Future Improvements

While the current architecture lays a solid foundation, there is immense potential for further refinement and enhancement:

1. Contextual Understanding: Improving the agent’s ability to better understand context and adapt its memory retention based on task relevance or user behavior could make agents even more personalized.
2. Enhanced Feedback Loops: Feedback mechanisms could be further improved with machine learning-driven insights, helping agents adjust not just on errors but also to proactively anticipate issues based on previous interactions.
3. Self-Learning Capabilities: Introducing more advanced reinforcement learning techniques could enable agents to evolve their decision-making strategies autonomously, requiring minimal human input for continuous improvements.
4. Real-Time Memory Expansion: Making memory updates more dynamic and real-time could allow agents to better handle fast-changing information, such as in rapidly evolving fields like finance, healthcare, or customer service.
5. Cross-Agent Collaboration: Future systems could enable agents to communicate with each other, sharing insights and strategies across different domains, enhancing their collective intelligence.

Conclusion: Unlocking the Power of AI Agents

In this article, we’ve explored the fundamental architecture behind AI agents, breaking down their core principles — Reasoning, Acting, and Memory — that drive their intelligent decision-making processes. From decomposing complex tasks into manageable subtasks, to dynamically selecting and utilizing tools based on a well-structured plan, the agent’s ability to act is powered by a robust execution block. The feedback loop mechanism ensures continuous improvement by evaluating the outcomes of actions and making adjustments for more accurate results. Finally, the memory block allows the agent to personalize its responses and adapt to new situations, ensuring long-term efficiency and effectiveness.

The system we’ve outlined demonstrates how combining multiple layers — reasoning, acting, and memory — creates a highly flexible and adaptive AI agent. This architectural approach moves beyond simple monolithic models and creates a compound, modular AI system capable of solving real-world problems more intelligently and dynamically. By leveraging tools, databases, and external systems, AI agents can offer more accurate, personalized, and context-aware responses.

I’ve tried my best to describe the architecture of the agentic AI systems. I appreciate you taking the time to read this.

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Published via Towards AI