The AI industry is witnessing a significant shift towards agentic systems – AI applications that can reason, plan, and act autonomously. Choosing the right agentic architecture is now crucial for building effective AI solutions. These systems go beyond simple text generation, requiring careful consideration of how they maintain context, make decisions, and execute actions. Whether you're building a personal assistant or a multi-agent system, your architectural choices will fundamentally shape your application's capabilities and reliability. In this guide, we'll explore the key considerations for an agentic architecture that aligns with your specific needs and use cases and how agents communicate with each other through them.

Agentic architecture: The anatomy of an agentic system 

Before we dive into how complex agentic systems operate, we must take a step back and understand their fundamental building block: an agent. An agent operates through prompts and a language model, while the broader agentic system incorporates these agents alongside external tools to create a complete operational architecture. The core components of an agent are:

• Prompts are responsible for guiding agent behavior by using instructions to define how they process information and respond to requests (from the user or other agents).
  
  

• Large language models (LLMs) are the brains behind the operation. They process prompts and generate responses, essentially using the available context to generate text or make decisions about how to use the available tools to achieve the desired objective. 

• External tools extend an agent's functionality beyond language processing by incorporating function calling and APIs, which give access to specific systems like databases and other external resources.

Message interaction between a user and agent

In agentic systems, agents interact with users through a standardized message workflow, ensuring consistent handling and coordinated task execution. An agent has three main types of messages:

1. System prompts define “who” the agent is and how it should operate, acting as the framework for its key behaviors and capabilities. Each agent in a network can have its unique system prompt, which defines its specialized role.

2. User prompts are how users interact with a system to provide specific instructions and requirements for a task. 

3. Assistant response represents the execution layer where the agent responds and acts based on user prompts, creating an ongoing dialogue of alternating messages between user and assistant.

By adhering to a combination of task requirements (user prompts) and system rules (from system prompts), agents are consistent with their predefined behavior and a user's needs while generating (assistant) responses and taking actions. This can be understood through a corporate analogy: the system prompt is like company policies, user prompts are like specific project requirements (the varying tasks), and assistant prompts are like employees executing tasks while following company policies. 

To understand how these message interactions work in a practical setting, let's examine two implementations of agentic systems. While many architectures exist for multi-agent systems, they primarily fall into two categories: centralized and decentralized. The following examples will later be expanded to demonstrate how agents communicate within these systems and how each architecture can be tailored for specific use cases.

In a content moderation and validation system for social media posts, the system prompt defines compliance rules, content standards, and validation criteria for flagging harmful posts and providing response formats. The user prompt submits the content for validation and provides context about the type of data to moderate, which is used by specialized agents to apply appropriate compliance rules and generate reports. 

In an LLM domain specialization system, the system prompts define domain specializations and learning parameters for each agent. User prompts submit training data and specify desired improvements, like increased medical terminology or legal reasoning capabilities. The agents process these examples and adapt their responses while coordinating with other specialized agents to maintain consistent behavior and outputs according to system prompts. 

Due to their stateless nature, agents don't retain memory between interactions, requiring all necessary context to be included in each prompt. This context typically includes the conversation history, but LLMs operate within a fixed context window with limited token capacity. The system employs summarization techniques to preserve key information for longer interactions where the context exceeds these token limits. Critical context can also be embedded directly in system prompts to ensure it remains accessible throughout the entire interaction chain.

Connecting the dots: Building a multi-agent system

Before diving into multi-agent networks, let's start with the fundamental building block: single-agent systems. A single agent operates as an independent node, capable of processing inputs, making decisions, and producing outputs based on defined objectives and capabilities. These systems excel in simplicity, offering faster response times, easier maintenance, and strong performance in specialized tasks like content generation or customer support. Though they lack the collaborative capabilities of multi-agent systems and are limited by their predefined scope, their simplicity and focused nature make them valuable components within larger networks and ideal solutions when dealing with less complex tasks. Multi-agent systems come in two primary architectures, each suited for different use cases.

In a centralized architecture, an orchestrator or main controller agent coordinates all interactions between specialized agents in a predetermined flow. The content moderation system introduced in the previous section is centralized, where a central orchestrator routes content through a sequence of specific agents for toxicity detection, fact-checking, and policy compliance, following strict validation rules. This architecture excels when workflows are well defined and predictable, but it requires comprehensive upfront planning and becomes rigid when scaling beyond its initial design. Hierarchical systems, while also centralized, organize agents in a tree-like structure where higher-level agents manage and delegate to sub-agents in their branches. This hierarchy of orchestrators eases the burden on a single controller agent. For instance, the content moderation system can be modified to have a top-level agent oversee separate branches for text and image moderation, each with sub-agents for specific checks. 

In contrast, a decentralized architecture allows agents to interact freely based on their capabilities and the task. Take the LLM fine-tuning system where different expert agents like domain specialists and evaluation agents collaborate dynamically as needed. New agents, like performance monitoring specialists, can join the system without disrupting existing workflows. While this flexibility is powerful for evolving systems, the non-deterministic nature of agent interactions can make outcomes less predictable and harder to debug as the agent network increases.

The key distinction lies in their adaptability: centralized systems offer precise control and reliability for specific use cases, while decentralized systems provide the flexibility needed for dynamic, evolving environments where requirements may change unexpectedly.

How agents communicate in a network 

Agent communication patterns vary significantly between centralized and decentralized architectures. In centralized systems, all communication flows through an orchestrator following a structured sequence. The orchestrator is the central intelligence that aligns goals, oversees the overall workflow, and ensures the effective collaboration of all agents. Taking our content moderation system as an example, when a user submits content, the orchestrator first determines the moderation intent (for example, text analysis, image screening, or both). Based on this, it develops a moderation plan, breaking it down into specific checks like toxicity detection, policy compliance, and fact verification. The orchestrator then coordinates these tasks by making function calls to appropriate specialized agents — routing text to the language analysis agent, images to the visual content agent, and verified facts to the fact-checking agent. Each agent executes its assigned task and reports back to the orchestrator, which maintains control over the entire workflow.

How multiple agents communicate in a centralized architecture 

Decentralized systems follow a more collaborative approach. Consider our LLM fine-tuning system: when a fine-tuning request enters the system, an entry point agent (typically specializing in task classification) analyzes the intent and broadcasts it to all available agents in the network, eliminating the need for a central orchestrator. For instance, if the request involves fine-tuning a medical language model, the data preparation agent, domain expertise agent, and evaluation agents all receive this broadcast. Instead of a central authority making assignments, agents participate in a voting process based on their capabilities and current workload to determine who is best suited for each aspect of the fine-tuning task. An evaluation agent might vote higher for testing phases, while a data scientist agent might claim the hyperparameter optimization task. This dynamic self-organization enables the system to naturally adapt to changing requirements while allowing new agents to join seamlessly.

How multiple agents communicate in a decentralized architecture 

How agent frameworks can help 

Agent frameworks streamline AI system development by providing developers with ready-to-use components and structures for creating multi-agent systems. This approach prevents developers from getting caught up in implementation details while significantly reducing the complexity of creating agent-based architectures. Let's examine three popular frameworks and their distinct approaches. 

1. CrewAI enables developers to create role-based AI agents that operate as a coordinated team, making it naturally suitable for centralized architectures. Through role-based orchestration, it facilitates structured planning and task management across workflows like collaborative research projects, where each agent fulfills a predetermined role. Despite being easy to implement, it faces scalability challenges and is not suited for situations where agents need to operate independently. 

2. The Vercel AI SDK excels in front end-centric, single-agent systems for real-time AI interactions and streaming responses. It integrates seamlessly with modern web frameworks, providing an excellent developer experience, while also being configurable for multi-agent or decentralized systems that require heavy back-end processing

3. Autogen shines in creating decentralized agent networks. It's particularly powerful for scenarios requiring autonomous agent-to-agent communication and complex problem-solving in changing environments, especially coding tasks where multiple specialized agents collaborate and new agents can be added on the fly. While Autogen offers great flexibility and powerful agent communication capabilities, it comes with a steeper learning curve and requires more setup time compared to simpler frameworks.

Your choice of framework may also depend on programming language expertise. While CrewAI and Autogen are built for Python developers, the Vercel AI SDK caters to TypeScript environments. As a developer, you can also build custom agent architectures without frameworks through direct API integration, custom routing logic, and function calling. By wrapping LLM interactions and tool functions into reusable components, you can create highly customized systems. This approach offers maximum flexibility and precise control over agent behaviors, though it requires deeper technical expertise.

The importance of agentic architecture 

Imagine an AI that goes beyond just answering questions and actively solves problems and makes decisions on its own. This is the promise of agentic architecture, where AI transforms from a passive tool into an active partner in decision-making. By combining language models with external tools, these AI agents are autonomous experts available 24/7, capable of tackling complex challenges that traditionally require human intervention. This shift enables businesses to confidently delegate operational demands to AI agents, transforming how organizations work. Rather than just providing information, these agents actively help solve real-world problems.

Similar to how the design of a building determines its strength and functionality, the architecture you choose for your AI agents shapes several important capabilities: 

• Decision-making speed: This is determined by how quickly agents can process information, access tools, and execute actions. Some architectures might offer faster single-task processing, while others enable parallel decision-making across multiple agents.

• Scalability: The scalability of your chosen architecture measures how easily the system can grow. Modular, decentralized designs allow new agents and capabilities to be added seamlessly, whereas hierarchical structures often create bottlenecks as agent numbers grow.

• Cost efficiency: The complexity of an architecture directly impacts operational costs through resource utilization, processing overhead, and maintenance requirements. 

• Maintenance and setup: Architectures vary significantly between initial deployment and ongoing maintenance complexity. Some designs may prioritize simplicity and quick setup, while others trade ease of deployment for enhanced capabilities.

• Reliability: Different architectures offer varying levels of reliability and fault tolerance. More distributed systems can maintain operation even if some components fail, while centralized systems might offer stronger consistency but present single points of failure.

• Adaptability: Perhaps most crucially, your architecture determines how effectively agents can combine the reasoning capabilities of LLMs with the precision of predefined rules. The right balance allows agents to adapt to new scenarios while maintaining reliable performance in core functions.

Okareo can evaluate your agents, no matter which agentic architecture you choose 

Modern agentic architectures offer diverse approaches to complex tasks, from simple single-agent systems to sophisticated multi-agent networks. Whether you choose centralized or decentralized control, or frameworks like CrewAI and Autogen, your architectural decisions will define what your system can achieve. 

Your architectural choices impact every aspect of your agent's performance, so evaluation must be integrated from day one of development. Through Okareo's platform, you can rigorously evaluate different agentic architectures by testing how agents perform across simulated scenarios. 

Okareo's TypeScript and Python SDKs allow you to seamlessly integrate your evaluations into your CI pipeline, either whenever your agent changes or on a scheduled basis. This is coupled with a user-friendly web application that provides intuitive checks and metrics, making it easy to validate that your agents function as intended, whether you're building a single agent or a complex multi-agent system. Start evaluating your agents today by signing up for Okareo.