Understanding Modern AI Agent Architecture

As AI systems have evolved from simple chat interfaces to sophisticated agents, the underlying architecture has become increasingly complex. Today's most advanced AI agents are built on a component-based architecture that enables safety, planning, integration with external systems, and collaboration capabilities.

The Agent Stack

Modern AI agent architectures typically consist of several key components working together to process requests, ensure safety, plan actions, retrieve knowledge, and interact with external systems.

Let's examine the core components:

Safety Layer

The safety layer acts as the first point of contact between the user and the agent system. This critical component evaluates all incoming requests to ensure they meet security and policy requirements before processing.

As shown in the diagram, the safety flow follows a clear sequence:

The user submits a request to the agent
The Safety Check component analyzes the request and makes an approve/reject decision
If approved, the request is recorded in the Memory Store for future reference and audit

This gatekeeper role is essential for enterprise AI deployment, especially in systems with significant autonomy or access to sensitive tools. The Safety Check component ensures that:

User requests comply with usage policies and ethical guidelines
Potentially harmful requests are flagged or rejected
Input is sanitized and validated before further processing
A record of all requests is maintained for accountability

Planning & Orchestration

AI Agent Planning and Orchestration Layer

Once a request passes the safety checks, it enters the planning and orchestration layer. This sophisticated sequence of operations turns high-level user intentions into actionable steps.

The diagram shows the step-by-step process:

The Safety Check forwards the validated request to the Orchestrator
The Orchestrator sends the request to the Planner to create a structured plan
The Planner may load contextual information from the Memory Store
Additional context may be exchanged between the Planner and Memory
The Planner might request relevant information from Knowledge Retrieval
With all information gathered, the Planner refines the execution plan
Finally, the Planner issues tool calls to execute specific actions

The central components of this layer include:

Orchestrator: Acts as the central coordinator that routes requests to appropriate components and maintains the overall workflow
Planner: Breaks down complex tasks into actionable steps and determines the execution strategy
Memory Store: Persists context and intermediate results between steps, enabling the agent to maintain state

This orchestration layer ensures that each request follows a logical sequence of operations, with the planner converting high-level user intentions into concrete execution steps.

Integration Layer

The integration layer enables the agent to interact with external systems and retrieve information beyond its built-in knowledge. This capability is what transforms a simple language model into a powerful agent that can take actions in the real world.

As illustrated in the diagram, the integration flow involves:

The Planner initiates a tool call with specific parameters
The Tool Interface requests authorization tokens from the Authorization service
With valid authorization, the Tool Interface makes requests to External Services
External Services return data to the Tool Interface
Results are passed back to the Planner for further processing

The key components in this layer are:

Tool Interface: Provides a standardized way to interact with external tools and APIs
Authorization: Manages permissions and authentication for secure tool access
External Service: Represents any third-party system or API that the agent can leverage
Knowledge Retrieval: Accesses relevant information from various sources to support decisions

This integration layer enables agents to overcome the limitations of their base models by retrieving real-time information and taking actions in the world, essentially extending the agent's capabilities far beyond what would be possible with a standalone language model.

Collaboration Layer

The most advanced agent systems include a collaboration layer that enables multiple agents and humans to work together. This capability is essential for solving complex problems that require specialized expertise or human oversight.

The collaboration flow shown in the diagram demonstrates:

The Planner sends plan/task data to be checked for collaboration needs
The system confirms the need for collaboration and distributes subtasks
Tasks are assigned to specialized agents in the Agent Network
The Collaborator accepts and executes the subtask
Results flow back to the original Planner
The Memory Store records outcomes
Finally, a response is delivered to the user

This layer includes:

Agent Network: Enables communication with other specialized agents that can handle specific types of tasks
Collaborator: Represents external entities (human or AI) that can assist with complex tasks

This collaborative architecture allows for complex workflows where multiple agents with different specialties can work together to solve problems beyond the capability of any single agent. It also enables human-in-the-loop operations where human expertise can be seamlessly integrated into the agent workflow.

How A2A & MCP Work Together:

Putting it All Together

When we combine all these layers, we get a complete view of how a user request flows through the system:

User submits a request
Safety check evaluates and approves the request
Request is forwarded to the orchestrator
Orchestrator asks the planner to create a plan
Planner breaks down the task into steps
Knowledge retrieval and tool access occur as needed
Results are processed through the collaboration layer if required
Final response is delivered to the user

This sequential flow allows for a controlled, observable process where each component handles a specific responsibility.

Why This Architecture Matters

This component-based approach offers several advantages:

Modularity: Components can be improved independently
Specialization: Each component focuses on a specific function
Safety: Multiple checkpoints ensure responsible operation
Scalability: New capabilities can be added by connecting additional tools
Observability: The flow can be monitored and debugged at each step

As AI continues to advance, this architectural pattern provides a solid foundation for building systems that are not just powerful but also safe, transparent, and adaptable.

Building Your Own Agent Systems

While the full architecture shown here might seem complex, developers can start with simpler versions and gradually add components as needed. Many open-source projects now provide implementations of these architectural patterns, making it increasingly accessible to build sophisticated agents.

The future of AI development will likely see this architecture continue to evolve, with more specialized components and standardized interfaces emerging as the field matures.

Are you working on agent architectures? What components do you find most challenging to implement? Share your thoughts in the comments