AI Agent Architectures

• AI Agent Architectures are structured designs that embed LLM-based reasoning in modular pipelines for planning, execution, and self-reflection.
• Multi-agent systems, with vertical leader-follower and horizontal peer coordination, achieve up to 10% faster task completion by efficiently allocating roles and managing communications.
• Practical guidelines emphasize modularity, clear role definitions, robust feedback loops, and phased execution to enhance reliability and mitigate errors.

AI agent architectures are structured systems that embed a language-model-based “brain” within a modular pipeline for reasoning, planning, acting on environments, and reflecting on outcomes. Modern AI agents are realized as closed-loop, goal-driven, tool-using decision makers capable of decomposing complex objectives, coordinating multi-step execution, and adapting through memory and feedback. Architectures vary from single-agent, monolithic deployments to robust multi-agent systems with explicit leadership, distributed roles, and advanced communication protocols, all designed to maximize reasoning capability, reliability, parallelism, and extensibility while controlling error and emergent behavior.

1. Taxonomy and Fundamental Design Patterns

AI agent architectures are principally classified along the axes of agent count, coordination strategy, and role specialization.

• Single-Agent Systems: Centered on a single LLM or foundation model, responsible for end-to-end reasoning, planning, and tool execution. These systems are architecturally simpler and best suited for tasks with well-defined specifications, limited toolsets, and modest parallelism demands. No agent-level feedback loops exist, although human-in-the-loop correction may be included (Masterman et al., 2024).
• Multi-Agent Systems: Two or more LLM-powered agents, each with distinct personas, roles, and tool access, act either in organized (hierarchical/vertical) fashions—designated leader delegating and aggregating subtasks—or peer-to-peer (horizontal), sharing context and tasks in open forums. Multi-agent setups unlock benefits such as parallel task execution, viewpoint diversification, and greater fault tolerance. Architectures may rotate leadership dynamically based on performance metrics (Masterman et al., 2024, Bansod, 2 Jun 2025).

| Architecture Pattern | Coordination | Best Use Case | |-----------------------------------|---------------------|--------------------------------------| | Single-Agent | Centralized | Simple, fixed tasks; low parallelism | | Vertical Multi-Agent (Leader-Follower)| Hierarchical | Isolated subtasks, clear division | | Horizontal Multi-Agent (Peer) | Peer-to-peer | Brainstorming, consensus, debate |

Key empirical result: Leader-driven teams complete tasks ~10% faster than unstructured peer teams, and dynamic leadership with criticize–reflect loops further lowers completion times (Masterman et al., 2024).

2. Principal Architectural Components

Every AI agent comprises three conceptual layers: brain (LLM core), perception (memory, scratchpad, and state), and action (tool-calling interface) (Masterman et al., 2024):

• Reasoning Modules: Chain-of-thought and ReAct-style prompting, memory-augmented reasoning (e.g., RAISE), self-reflection (e.g., Reflexion), and graph-search (e.g., LATS Monte Carlo planning).
• Planning Phases: Typically organized into task decomposition, multi-plan generation and selection, external planner invocation (e.g., PDDL), reflection and refinement, and memory-augmented planning.
  • Graph-based formalism: A directed acyclic graph $G=(V,E)$ with nodes as subtasks and edges as dependency relations. Independent subgraphs enable parallel execution (Masterman et al., 2024).
• Tool Invocation: Includes function-calling APIs where agents emit well-typed JSON, manual tool schema (AutoGPT+P), or publish–subscribe bus models (MetaGPT).
• Interconnection Flow:

1. User prompt → Planning Module
2. Plan → Reasoning Module
3. Reasoning → Action (tool invocation)
4. Observation → Memory Update
5. Loop via reflection until termination condition (Masterman et al., 2024).

3. Multi-Agent Role Allocation, Leadership, and Coordination

Multi-agent deployments rely on careful definition of agent roles (including persona templates: tool inventories, goals, conversational “dos/don’ts”) (Masterman et al., 2024, Sapkota et al., 15 May 2025).

• Vertical Coordination: In leader–follower patterns, the lead agent manages global state, delegates tasks, and aggregates sub-agent results. Communication tends to be matrixed (subagent → lead → subagent). Leadership may rotate based on performance metrics.
• Horizontal Coordination: All agents interact on a shared channel; task ownership is often by explicit volunteering. Risks include “social chatter” and cross-talk, mitigated using message filters, scoped channels, and explicit task partitioning (Masterman et al., 2024).

Protocols for multi-agent coordination include the Contract Net Protocol (CNP), Agent-to-Agent (A2A), Agent Network Protocol (ANP), and higher-layer negotiation/consensus (Agora layer), each specifying schemas for task broadcast, bidding, result integration, and resolution of disputes (Derouiche et al., 13 Aug 2025).

4. Communication Protocols and Interoperability

Agent communication modes are dictated by architecture—broadcast (all messages visible) for horizontal/peer models, direct point-to-point for vertical/hierarchical. Publish–subscribe patterns, notably in MetaGPT and more scalable protocols like gRPC, QUIC, KQML, or CloudEvents, are employed for robust and scalable inter-agent and agent–tool interactions (Masterman et al., 2024, Du et al., 2 Sep 2025, Derouiche et al., 13 Aug 2025).

Critical requirements from Internet-inspired systems include:

• Scalability: O(N²) full-mesh communications are avoided via hierarchical clustering and capability discovery services (CDS, “DNS for capabilities”).
• Security/Identity: DID documents and OAuth-like delegated authorization ensure that tool calls are traceable and authenticated.
• High Performance/Latency: UDP, gRPC, and class-based QoS guarantee timely control, state sync, and bulk data exchange.
• Manageability: Observability planes (metrics, tracing, semantic logs) and policy-as-code configurations support operational governance.
• Protocol Examples: KQML/FIPA-ACL for semantic negotiation, OAuth for secure actions, AgentCard and ANP for agent identity/interaction contracts (Du et al., 2 Sep 2025, Derouiche et al., 13 Aug 2025).

5. The Agentic Control Cycle: Planning, Execution, Reflection

All advanced agent architectures converge on a tripartite execution cycle (Masterman et al., 2024, Nowaczyk, 10 Dec 2025):

• Planning Phase: Agents synthesize plans—via decomposition or multi-plan selection—potentially invoking classical planners, and instantiate explicit dependencies between tasks. Data flows from user goal to LLM prompt, to plan, to dispatch.
• Execution Phase: Agents (or parallel subagents) execute plan steps, making tool calls either synchronously or asynchronously. Observations are written back to agent memory.
• Reflection Phase: Self-evaluation via critic roles or LLM passes, evaluating state (goal-achievement, errors, inconsistencies), prompting plan revision, and enabling robust error recovery. Reflection triggers re-planning or fall-back reasoning as needed.

This plan–act–reflect loop provides robustness against unexpected outputs, unreliable tool responses, and misaligned objectives (Masterman et al., 2024).

6. Empirical Insights: Capabilities, Trade-Offs, and Limitations

Single-agent findings:

• ReAct reduces hallucination rates (6% vs 14% for CoT), but can enter repetitive loops absent external feedback.
• Memory-augmented approaches (e.g., RAISE) improve context retention and role fidelity, but still falter on complex logic and can hallucinate roles.
• Reflexion-style self-reflection achieves lower hallucinations and higher success vs. ReAct/Chain-of-Thought, but is limited by windowed memory (Masterman et al., 2024).

Multi-agent findings:

• Organized vertical teams (with a leader) achieve ~10% faster task completion and lower costs.
• Horizontal teams (e.g., DyLAN, AgentVerse) excel at brainstorming and flexible reasoning, while vertical teams remain preferred for sequential, tool- or data-isolated tasks.
• Publish–subscribe strategies (MetaGPT) outperform single-agent baselines in code-generation and logic benchmarks by reducing communication noise and enabling relevance-filtered messaging (Masterman et al., 2024).

General principles:

• The choice between single- and multi-agent depends on task complexity, the need for diverse feedback, and parallelism requirements.
• Human oversight and feedback injection consistently improve reliability.
• Communication, role, and phase clarity are key to robust orchestration.

Limitations remain in benchmarking (scarcity of realistic, uncontaminated tasks), domain shift generalization, and mitigation of alignment and safety failures (e.g., agent collusion, propagation of bias) (Masterman et al., 2024).

7. Practical Guidelines for Agent Architecture Design

Based on the architectural survey and concrete case studies (Xu et al., 2024, Masterman et al., 2024):

• Separate Reasoning from Orchestration: Planning, workflow, and tool discovery should be modularized, isolating LLM components to only those tasks that require high-flexibility inference.
• Service-Oriented Extensibility: Employ service-computing patterns (“Registration–Discovery–Invocation”) for dynamic tool addition/removal, minimizing redeployment risk.
• Capability Module Definition: Treat each functional module as a microservice with clear input/output/process boundaries.
• Role and Persona Clarity: Specify agent capabilities, roles, conversational guidelines, and permissible toolsets explicitly; enforce them via prompt templates and persona schemas.
• Phased Structure Enforcement: Maintain clear separation and auditability across planning, execution, and reflection phases.
• Guard Against Social Noise: In multi-agent settings, implement message filtering, persona constraints, and strict workflow scoping.
• Combine Reflection and Critique Loops: Reflexion-style passes, dynamic leadership, and explicit self-critique increase robustness.
• Log and Monitor All Phases: Collect latency, action, and memory utilization logs to tune utility–cost trade-offs and uncover bottlenecks or coordination failures.

Future development directions include the creation of standardized, cross-domain benchmarks, domain-adaptive agentic strategies, and stronger, composable guardrails for autonomous, transparent, and scalable agent operation (Masterman et al., 2024).