7 Layered Agentic AI Reference Architecture

(This is one section of chapter 2 of my upcoming Springer’s Agentic AI book which will be published in the middle of 2025. Please have a sneak preview and let me know what you think)

This layered approach decomposes the complex AI agent ecosystem into distinct functional layers: from Foundation Models that provide core AI capabilities, through Data Operations and Agent Frameworks that manage information and development tools, to Deployment Infrastructure and Security layers that ensure reliable and safe operations, culminating in the Agent Ecosystem where business applications deliver value to end-users. Each layer serves a specific purpose while abstracting complexity from the layers above it, enabling modular development, clear separation of concerns, and systematic implementation of AI agent systems across organizations.

The seven layers of the proposed agent architecture are interconnected, with each layer building on the functionality of the one beneath it. Starting from Layer 1, Foundation Models provide the core AI capabilities, which are utilized by Layer 2, Data Operations, to manage and preprocess data effectively. Layer 3, Agent Frameworks, leverages both data and foundational AI capabilities to enable the creation and execution of intelligent agents. Layer 4, Development Tools, supports the frameworks by providing programming environments, debugging tools, and integration solutions, streamlining the agent-building process. Layer 5, Deployment Infrastructure, ensures that agents created through the frameworks and tools can be deployed at scale with robust performance. Layer 6, Security, reinforces the system by safeguarding data, models, and operations across all previous layers including layer 7, so we can think of layer 6 as a vertical layer with implication to each layer. Finally, Layer 7, the Agent Ecosystem, integrates these capabilities to deliver cohesive and functional AI applications for end-users, abstracting underlying complexities to provide seamless and scalable solutions.

In the next few subsections, I will describe it from top down, that is from Layer 7 to Layer 1.

Figure 1: The Seven-Layer AI Agent Architecture

Layer 7. Agent Ecosystem

The ecosystem layer represents the vibrant marketplace where AI agents interface with real-world applications and users. This encompasses a diverse range of business applications, from intelligent customer service platforms to sophisticated enterprise automation solutions. Business apps in this layer include virtual assistants handling customer inquiries, automated content generation systems, intelligent document processing solutions, and AI-powered decision support systems. Tool providers create specialized interfaces that make AI capabilities accessible to specific industries, such as legal document analysis tools, medical diagnosis assistants, or financial trading algorithms.

The ecosystem also includes integration platforms that connect AI agents with existing business systems like CRM, ERP, and workflow management tools. This layer supports both vertical solutions (industry-specific) and horizontal solutions (function-specific) that can be deployed across different sectors. Development tools and SDKs enable businesses to customize and extend agent capabilities for their specific needs. In Chapter 4, we will highlight Agent use in improving business workflows or inventing new ones.

The marketplace aspect facilitates the discovery and deployment of pre-built agents and components, allowing organizations to find and implement AI solutions quickly. This includes agent directories, capability registries, and reputation systems that help users evaluate and select appropriate solutions. The ecosystem also encompasses communities of developers, businesses, and users who contribute to the evolution of AI agent applications, sharing best practices, use cases, and innovations.

Important considerations at this level include user experience design, integration capabilities, scalability of solutions, and the balance between customization and standardization. The ecosystem layer is where the theoretical capabilities of AI agents are transformed into practical, value-generating applications that solve real business problems and enhance human capabilities.

Layer 6. Security and Compliance

The security and compliance layer forms a crucial protective framework ensuring AI agents operate safely, securely, and within regulatory boundaries. While seemingly positioned as a distinct layer, it’s vital to understand that security and compliance are not afterthoughts, but rather foundational principles that must be embedded within each layer of the AI Agent stack. This layer’s placement at Layer 6 reflects its overarching role in safeguarding the entire system, but its principles permeate every level, from the foundational models to the agent ecosystem. We will discuss more about these challenges in Chapter 10 of this book.

Why Layer 6, yet Integrated into Every Layer?

1. Comprehensive Oversight: Placing security and compliance as a distinct layer emphasizes the need for a holistic approach. Layer 6 acts as a central point for defining security policies, compliance requirements, and risk management strategies that apply across the entire architecture.
2. Specialized Focus: This dedicated layer allows for the development of specialized expertise and tools focused specifically on security and compliance. This includes threat modeling, vulnerability assessment, security audits, and compliance monitoring, which require dedicated skills and resources.
3. Regulatory Adherence: As AI agents increasingly handle sensitive data and operate in regulated industries, a dedicated layer helps ensure adherence to evolving legal and regulatory frameworks (e.g., EU AI Act, GDPR, HIPAA). This layer is responsible for implementing necessary controls and processes to meet these requirements.
4. Risk Management Framework: Layer 6 facilitates the implementation of a comprehensive risk management framework. We recommend using a structured approach to assess and mitigate potential security and compliance risks, including vendor risk assessment for third-party AI services. Regular security assessments, penetration testing, and compliance audits ensure the ongoing integrity of the security framework.
5. Incident Response and Business Continuity: This layer is crucial for developing and maintaining AI Agent-related incident response plans and disaster recovery procedures. These plans must be regularly tested to ensure business continuity in the face of security breaches or system failures.

Security and Compliance Across All Layers:
It’s paramount to understand that Layer 6 does not operate in isolation. Effective security and compliance require a “defense in depth” strategy, where security measures are integrated into each layer of the architecture:

• Layer 1 (Foundation Models): Secure model development practices, including data sanitization, model robustness testing, and secure training environments, are essential.
• Layer 2 (Data Operations): Data security, privacy protection, access controls, and encryption are critical for managing the data used by AI agents.
• Layer 3 (Agent Frameworks): Secure coding practices, input validation, and secure API design within the agent framework are necessary to prevent vulnerabilities.
• Layer 4 (Development Tools): Secure development environments, code signing, and dependency management tools are important for building secure AI agent systems.
• Layer 5 (Evaluation and Observability): Monitoring for anomalous behavior, security logging, and auditing capabilities are crucial for detecting and responding to threats.
• Layer 7 (Agent Ecosystem): Secure deployment practices, access controls, and ongoing monitoring of the agent’s interactions within the ecosystem are essential.

In essence, while Layer 6 provides the overarching framework and specialized expertise, security and compliance must be a shared responsibility across all layers and throughout the entire lifecycle of an AI agent. This integrated approach is essential to build trustworthy, robust, and ethically sound AI systems that can operate safely and responsibly in the real world.

Layer 5. Evaluation and Observability

Recent developments in AI Agent evaluation have focused on creating comprehensive and standardized approaches to assess both the safety and performance of autonomous AI systems. One significant initiative is led by the AI Safety Institute (AISI) of the UK Government, which emphasizes the importance of evaluating AI agents capable of making long-term plans and operating semi-autonomously. This framework aims to test decision-making processes and action selection in complex environments, ensuring that agents can operate safely and effectively. Recently, AISI launched a bounty program for novel evaluations and agent scaffolding to encourage the development of innovative evaluation techniques for assessing the capabilities and potential risks of advanced Agent systems(UK AISI, 2024).

The bounty program is primarily seeking innovative techniques in two main technical areas: autonomous capability evaluations and agent scaffolding. For autonomous capability evaluations, AISI is looking for methods to assess an AI agent’s ability to operate independently, make decisions, and carry out complex tasks without human intervention. Agent scaffolding, the second focus area, involves developing frameworks or tools that can support and guide AI agents in their operations. Technically, successful applications are expected to demonstrate novel approaches to evaluating AI agents. This could involve developing new metrics, creating sophisticated simulation environments, or designing complex multi-step tasks that challenge an agent’s capabilities across different domains.

Another notable contribution is the Mosaic AI Agent Framework introduced by Databricks. This framework includes an evaluation component specifically designed for AI agents, featuring pre-built metrics that assess answer correctness, groundness, and relevance. It also incorporates safety evaluation metrics tailored for autonomous agents, facilitating a streamlined development and evaluation workflow through integration with MLflow. This combination allows developers to efficiently evaluate their AI agents across multiple dimensions (Wendell & Rao, 2024).

In addition to these frameworks, Agent Protocol, released in late 2023(https://agentprotocol.ai/), provides a standardized way to interact with AI agents. While not strictly an evaluation framework, it has significant implications for benchmarking agent performance. By establishing consistent protocols for agent interactions, this initiative allows for standardized testing of capabilities and safety features across different agent implementations, making it easier to compare performance on similar tasks.

Benchmarks for assessing how well agents perform in multi-agent environments by evaluating their communication, coordination, and conflict resolution capabilities can be valuable.

Recent frameworks have placed a strong emphasis on safety benchmarks. Key aspects include containment, which assesses an agent’s ability to operate within defined boundaries; alignment, which evaluates how well an agent’s actions align with intended goals and ethical guidelines; robustness, which tests an agent’s performance under various stress conditions or adversarial inputs; and interpretability, which measures how easily an agent’s decision-making process can be understood and audited.

Performance evaluation has also evolved significantly. It now encompasses task completion metrics that measure an agent’s ability to achieve specific objectives, efficiency assessments that evaluate resource usage — including time and computational power — adaptability evaluations that assess how well an agent performs in novel or changing environments, and scalability tests that examine an agent’s performance as task complexity increases.

A growing trend in AI agent evaluation is the incorporation of cost metrics. This involves assessing the economic viability of deploying agents while measuring the trade-off between performance improvements and increased costs. Evaluating an agent’s ability to optimize resource usage autonomously is becoming increasingly important as organizations seek to balance effectiveness with economic considerations.

Despite these advancements, several challenges remain in the field of AI agent evaluation. One major challenge is achieving standardization across different benchmarks, which would enable better comparisons among various implementations. Additionally, developing methodologies to evaluate the potential long-term consequences of AI agent actions is an area that requires further research.

Another significant challenge lies in creating evaluation frameworks that can assess agent performance in rapidly changing or unpredictable environments. As AI systems become more integrated into dynamic real-world applications, this adaptability will be crucial for ensuring their reliability and safety.

Finally, incorporating ethical considerations into AI agent benchmarks is becoming increasingly important, especially for agents deployed in sensitive domains such as healthcare or finance. As the field continues to evolve rapidly, these evaluation frameworks are likely to adapt further, with a growing emphasis on comprehensive assessments that are standardized and ethically aware.

Observability is another hot topic for Agentic AI. We see some emerging tools in this space. The following are some examples:

• LangSmith provides advanced observability features for monitoring large language model (LLM) applications. Key capabilities include detailed tracing of function executions and system events using an `@traceable` decorator, centralized dashboards for session grouping, and integration with LangChain and other frameworks. Its platform supports both real-time and historical evaluations, custom metrics, and automated alerts for anomaly detection. Additionally, LangSmith offers features for dataset management, human feedback integration, and centralized prompt management for efficient optimization of AI applications. The enterprise plan includes enhanced deployment support and training.
• Langfuse specializes in self-hosted observability solutions for LLMs, offering extensive support for tracing, including multimodal tracing, and the ability to monitor system performance with minimal overhead. It allows users to manage datasets and evaluate models using both automated and human-assisted methods. Langfuse emphasizes flexibility, with an open-source version that caters to developers needing customizable solutions. Its enterprise plan adds features such as proactive monitoring, automated event triggers, and comprehensive analytics dashboards. This makes it suitable for teams requiring deeper control and insight into their LLM systems.
• Arize AI focuses on monitoring and troubleshooting machine learning models in production. Its platform provides model performance analytics, bias detection, drift monitoring, and root cause analysis. It is designed to identify anomalies in real-time, offering visualizations and tools to trace issues back to the underlying datasets. Arize supports the integration of multiple AI models and provides features for managing dataset integrity, ensuring transparency in predictions.
• Weave offers observability tailored for interactive AI systems. Its platform is designed to track user interactions with AI agents, providing analytics to measure user satisfaction, detect anomalies, and optimize conversational flows. This makes it particularly suited for applications involving chatbots or virtual assistants. Weave also provides detailed insights into how user queries are processed by AI, enabling developers to iteratively improve system responses.
• AgentOps.ai focuses on operationalizing AI agents, providing tools to monitor their real-time performance, track usage patterns, and detect errors. It emphasizes agent lifecycle management by integrating monitoring with deployment workflows. Its observability tools include the ability to analyze agent interactions and ensure compliance with operational requirements, which is critical for ensuring AI agents perform reliably in dynamic environments.
• Braintrust emphasizes analytics and decision-making tools for AI-driven systems. Its observability features include automated reporting, real-time metric tracking, and support for visualizing system behavior. Braintrust enables developers to identify bottlenecks and inefficiencies in AI workflows, facilitating optimization of model performance and ensuring the robustness of AI deployments in critical applications.

Each of these platforms addresses unique needs within the Agentic AI observability space, from general performance monitoring to specialized use cases such as agent lifecycle management or conversational flow optimization.

Layer 4. Deployment and Infrastructure

The deployment and infrastructure layer provides the robust technical foundation necessary for running AI agents at scale. Cloud platforms (AWS, Azure, GCP) offer essential services including compute resources (GPU/TPU acceleration), storage solutions (object storage, block storage), and networking capabilities (load balancers, CDNs). Container orchestration systems like Kubernetes manage agent deployment, scaling, and failover, ensuring high availability and reliability.

Infrastructure-as-Code (IaC) tools enable automated deployment and configuration management, using technologies like Terraform, CloudFormation, or Pulumi. This ensures consistent and repeatable deployments across different environments. CI/CD pipelines automate the testing and deployment process, enabling rapid iteration and updates to agent systems.

Resource management systems optimize infrastructure utilization through dynamic scaling, load balancing, and resource allocation. This includes sophisticated scheduling algorithms that match workloads to appropriate compute resources based on cost and performance requirements. Edge computing capabilities enable AI agents to operate closer to data sources, reducing latency and bandwidth usage.

Development environments like Replit provide integrated tools for coding, testing, and deploying AI agents. These environments support collaborative development, version control, and easy access to necessary dependencies and libraries. Infrastructure monitoring systems provide real-time visibility into system health, resource utilization, and performance metrics.

In addition to Amazon Bedrock, Microsoft Azure OpenAI service, and Replit, the following are emerging hosting service providers

• Letta: Letta provides a cloud-based infrastructure designed to host stateful AI agents, with persistent memory and task management capabilities. It uses containerized deployments (e.g., Docker) for scalability and supports REST API endpoints and Python SDKs for integration. Its hosting environment provides low latency and reliability for real-time conversational applications.
• Agents API: Built for versatile hosting, Agents API emphasizes modularity in deploying AI agents across a wide range of environments. Its infrastructure supports both stateless and stateful operations with cloud-native scalability. It facilitates custom hosting configurations, enabling seamless interaction with external systems and third-party tools.
• LiveKit Agents: LiveKit focuses on hosting agents optimized for real-time interaction, leveraging its WebRTC-based infrastructure for low-latency communication. The platform ensures high availability with distributed hosting and dynamic load balancing, designed for voice, video, and text integration in collaborative and interactive applications.

Finally, disaster recovery and business continuity features are needed for system reliability through automated backups, multi-region deployment, and failover mechanisms. Cost management tools track resource usage and optimize infrastructure spending through techniques like spot instance usage and automatic resource cleanup which are proven technology in traditional CPU based cloud environments and can be retrofitted with some innovation for GPU cloud. The infrastructure layer also includes tools for managing model versioning, deployment strategies (blue-green, canary), and feature flagging.

Layer 3. Agent Frameworks

The agent frameworks layer provides sophisticated software frameworks and tools that simplify the development and management of AI agents. LangChain offers a comprehensive development framework with features like prompt management, chain-of-thought reasoning, and sophisticated memory management. It includes tools for building complex workflows, implementing retrieval-augmented generation (RAG), and managing agent state. In Section 2.2 we will compare some of top Agent frameworks.

These frameworks include tools for debugging, testing, and monitoring agent behavior. They provide abstractions for common tasks like API integration, data processing, and error handling. Development tools support both low-code and programmatic approaches to agent development, catering to different skill levels and use cases.

The frameworks layer also includes specialized tools for specific domains or tasks, such as frameworks for building conversational agents, document processing systems, or automated reasoning systems. Integration capabilities enable seamless connection with various data sources, APIs, and external services.

Among the latest developments in 2024, a special kind of Agent called “Computer Use Agent” is gaining a lot of traction. For example, AI agents like Anthropic’s Claude Computer Use Agent, Google’s Project Jarvis, and OpenAI’s upcoming “Operator” mark a significant evolution in AI capabilities, enabling direct interaction with computer interfaces by manipulating cursors, clicking buttons, and typing text. This advancement transforms how tasks are executed, automating processes such as form-filling, multi-site searches, and online transactions. By taking over routine and time-intensive tasks, AI agents enhance productivity, allowing humans to focus on creative and strategic work, while their 24/7 availability increases efficiency across industries.

Layer 2. Data Operations

The data operations layer manages the complex data infrastructure required for AI agent operations. Vector databases (Pinecone, Weaviate, Milvus) provide specialized storage and retrieval systems for high-dimensional vector embeddings, enabling efficient similarity search and semantic matching. These databases support sophisticated indexing techniques like HNSW (Hierarchical Navigable Small World) for fast approximate nearest neighbor search.

Data loaders provide versatile interfaces for ingesting and processing diverse data types, including structured databases, document stores, and unstructured content. ETL pipelines handle data cleaning, transformation, and enrichment, ensuring data quality and consistency. This includes capabilities for handling streaming data, batch processing, and real-time updates.

Advanced data processing features include automatic schema detection, data validation, and format conversion. Data versioning systems track changes and maintain data lineage, enabling reproducibility and audit capabilities. Caching mechanisms optimize data access patterns, reducing latency and computational overhead.

Data operations tools support sophisticated querying capabilities, including hybrid search combining vector similarity with traditional filtering. Data synchronization mechanisms ensure consistency across distributed systems and handle conflict resolution in multi-writer scenarios.

The layer includes tools for data governance, including data quality monitoring, access control, and compliance tracking. Data pipeline orchestration tools manage complex data workflows, handling dependencies and ensuring reliable data processing. Performance optimization tools help tune database configurations and query patterns for optimal efficiency.

Monitoring and observability tools provide insights into data operation performance, including metrics on throughput, latency, and resource utilization. The layer also supports data backup and recovery operations, ensuring data durability and availability.

In the data operation layer, one prominent component is RAG (Retrieval-Augmented Generation), a framework that combines retrieval models with generative AI to enhance the accuracy and relevance of generated outputs. RAG operates by retrieving relevant information from a database or external source based on a query, which is then used to guide the generative model in producing responses. It excels in tasks such as question-answering, summarization, and enriching generative models with up-to-date knowledge.

Building on this foundation, Agentic RAG introduces autonomous decision-making capabilities, utilizing agents to orchestrate retrieval, generation, and iterative refinement of results. Unlike the passive, query-driven retrieval in standard RAG, Agentic RAG employs active strategies and multi-step reasoning to handle complex problem-solving and multi-turn dialogue systems effectively. This makes it particularly suited for dynamic workflows where adaptability and iterative improvement are essential.

Comparatively, while both RAG and Agentic RAG leverage retrieval and generative AI to improve output quality, RAG is simpler and focused on static, query-specific tasks. In contrast, Agentic RAG’s agents enable it to manage more intricate, autonomous processes, making it a more versatile and advanced framework for handling complex scenarios(Figure 2.2).

Figure 2: RAG vs. Agentic RAG

Layer 1. Foundation Models

The foundation model layer represents the core AI engines that power agent capabilities. Leading models from OpenAI (GPT-4), Anthropic (Claude), Google (Gemini), and Cohere provide sophisticated natural language processing and reasoning capabilities. Notably, they are in the process of being trained with advanced functionalities such as agentic planning, chain-of-thought reasoning, and other agentic capabilities, which will enable more robust and dynamic interactions.

These models support various interaction modes, including completion, chat, function calling interfaces, and multi-modality — processing different types of inputs such as text, images, and structured data. They integrate safety measures and content filtering capabilities to ensure appropriate outputs, offering features like content moderation, toxicity detection, and bias mitigation. API interfaces provide programmatic access to these capabilities, with features like request batching, streaming responses, and rate limiting. Different model versions allow applications to choose appropriate trade-offs between performance, cost, and specialization.

Architectural innovations like mixture-of-experts, constitutional AI, and specialized training techniques enhance these models, supporting multiple languages and handling diverse inputs. Performance optimizations include response caching, prompt compression, and efficient token usage, enabling capabilities for semantic search, text classification, and structured output generation. Regular model updates incorporate new functionalities and improvements while maintaining backward compatibility.

Multi-modality models, which can process and integrate various types of data such as text, images, audio, and structured data, are becoming increasingly essential for AI agents. These models enable AI agents to understand and generate complex responses based on multiple input types, enhancing their contextual understanding and interaction capabilities. For instance, Claude’s Computer Use Agent can autonomously navigate web pages, click buttons, and type text, mimicking human computer use(Anthropic, 2024).