---
title: AI Agent Architectures
url: https://www.emergentmind.com/topics/ai-agent-architectures
type: topic
---

# AI Agent Architectures

Searching arXiv for the cited agent-architecture papers to ground the article.
arXiv Search Query: 2404.11584 AI agent architectures survey
arXiv Search Query: 2503.12687 AI Agents: Evolution, Architecture, and Real-World Applications
AI agent architectures are system designs in which foundation models or large language models operate inside iterative control loops that perceive an environment, retrieve or update memory, reason over goals, plan actions, invoke tools, execute operations, observe outcomes, and continue until completion, termination, or escalation. Recent work consistently treats the agent not as a single monolithic model call, but as a composition of modules, interfaces, and governance mechanisms; recurring decompositions include perception, memory, reasoning, planning, action, tool use, collaboration, and safety or observability layers [2601.01743][2503.12687][2512.09458].

## 1. Historical development and major taxonomies

The modern literature places AI agent architectures within a longer progression from rule-based and reflexive systems to goal-directed, learning, and large-model-based systems. One influential account begins with simple reflex agents that map conditions directly to actions, then traces a progression through model-based reflex agents, goal-based agents, utility-based agents, learning agents, and hierarchical agents. In that view, the decisive shift is from reactive architectures toward deliberative and adaptive ones: contemporary agents maintain internal models, compare alternatives, reason over goals, plan multi-step trajectories, use tools, and incorporate feedback [2503.12687].

A parallel survey tradition divides the space into single-agent and multi-agent architectures. Single-agent systems place reasoning, planning, and tool execution around one language-model-powered entity, typically in a plan–act–evaluate loop. Multi-agent systems contain two or more agents with distinct personas, tool access, or roles, and are often organized along a vertical–horizontal spectrum. Vertical architectures use a lead agent with subordinate agents, whereas horizontal architectures emphasize peer discussion and shared deliberation. Representative patterns include ReAct, RAISE, Reflexion, AutoGPT + P, and LATS for single agents, and organized teams, DyLAN, AgentVerse, and MetaGPT for multi-agent systems [2404.11584].

Another recurrent taxonomy distinguishes standalone or autonomous AI agents from collaborative agentic systems. Standalone agents are described as “autonomous computational entities engineered for specialized task execution within defined operational boundaries,” whereas collaborative agentic systems are “complex architecture[s] where multiple specialized agents collaborate to accomplish objectives that exceed the capabilities of any individual component.” This distinction is architectural rather than merely numerical: it turns on whether the system requires distributed task decomposition, communication, synchronization, shared knowledge, and decision fusion [2506.01438].

A more implementation-oriented synthesis breaks agentic systems into Perception, Brain, Planning, Action, Tool Use, and Collaboration, and formalizes the agent as a modified POMDP-style loop:
$$
\mathcal{A} = \langle \mathcal{S}, \mathcal{O}, \mathcal{M}, \mathcal{T}, \pi \rangle
$$
with perception, memory update, cognitive planning, action selection, and environmental transition functions. This framing makes explicit that an agent is not merely prompted; it is embedded in a recurrent control process with state, memory, tools, and feedback [2601.12560].

## 2. Canonical components and reference architectures

Across surveys and systems papers, a stable component vocabulary has emerged. Contemporary agents typically contain perception or input processing, knowledge representation, memory, reasoning and decision-making, planning, action selection and execution, tool use, environment interaction, feedback loops, self-monitoring, and safety or governance layers. The language model remains central for natural-language understanding, task interpretation, and flexible reasoning, but it is repeatedly described as insufficient by itself because it is bounded by knowledge and reasoning limitations [2503.12687].

One compact formalization represents an agent as:
$$
\mathcal{A}=(\pi_\theta,\mathcal{M},\mathcal{T},\mathcal{V},\mathcal{E})
$$
where $\pi_\theta$ is the transformer policy core, $\mathcal{M}$ the memory subsystem, $\mathcal{T}$ the tool set, $\mathcal{V}$ the verifiers or critics, and $\mathcal{E}$ the environment. The loop is explicit: the agent observes state, retrieves memory, proposes an action, validates it, executes it through tools, and updates memory. This representation underwrites a broad class of single-agent and multi-agent systems and makes verifiers and environment interfaces first-class architectural elements rather than afterthoughts [2601.01743].

A closely related enterprise-oriented reference architecture argues that reliability is “chiefly an architectural property” and converges on a canonical stack of goal manager, planner, tool router, executor or execution gateway, memory, verifiers or critics, safety monitor or supervisor, and telemetry or observability. In this formulation, the model supplies proposals, but the architecture supplies validation, enactment, bounded authority, monitoring, and record-keeping. The paper’s summary phrase, “models propose, architectures dispose,” captures a widely shared design principle: dependable behavior depends more on typed interfaces, least-privilege execution, and assurance loops than on unconstrained model capability alone [2512.09458].

The same broad orientation appears in production-focused studies of industrial practice. Practitioners repeatedly describe agent systems in terms of shared context, registries, graph-based orchestration, policy-driven safeguards, message-based asynchronous execution, multimodal memory, and centralized coordination control planes. This suggests that, in deployed systems, the effective architecture is often defined as much by surrounding infrastructure as by the model itself [2604.00189].

## 3. Planning, action, and tool mediation

Planning is usually separated from generic reasoning as the subsystem that decomposes goals into subgoals, identifies dependencies, selects action sequences, and revises plans when the environment changes. The literature spans linear chain-of-thought-style decomposition, interleaved reasoning-and-acting loops such as ReAct, reflective repair mechanisms such as Reflexion, search-oriented methods such as Tree of Thoughts and LATS, and hierarchical decomposition strategies. The common architectural move is to replace one-shot output generation with explicit or implicit plan formation followed by monitored execution [2404.11584][2601.01743].

Tool use is one of the clearest distinctions between a plain language model and an agent. Tool frameworks expose external APIs, databases, calculators, search systems, business applications, and physical or digital actuators. In production-oriented work, tool use is increasingly treated as a contract problem: the architecture should prefer function schemas and structured outputs, validate types and ranges, enforce preconditions and policy checks, and mediate all external action through typed interfaces. The same work emphasizes idempotency, transactional semantics or compensation, retries with rate limits, and simulate-before-actuate safeguards for side-effectful operations [2512.09458].

A service-oriented variant of this principle appears in CACA Agent, which decomposes the agent into Reception Capability, Workflow Capability, Planning Capability, Methodology Capability, Profile Capability, Tool Capability, Tool Broker, and Tool Service. Planning is formalized as
$$
PlanCap(Task,Methodology) \rightarrow [Proc(ST_1),\ldots,Proc(ST_n)]
$$
and each planned subtask may contain execution, branching, and looping structure:
$$
Proc(ST_i):(Execute(ST_i),Branch(ST_i), Loop(ST_i))
$$
Tool use is late-bound through a registration–discovery–invocation pattern rather than being hard-coded into a monolithic prompt. This architecture reframes extensibility as a systems problem: new process knowledge is inserted through Methodology Capability, and new execution capability is added through tool registration [2403.15137].

The same concern reappears in software-engineering studies of coding agents. One recent analysis argues that prompts act as architectural control surfaces: specifying structured output induces validators and retry logic, whereas declaring tool access induces routers, argument validation, execution control, and state management. Function calling and ReAct-style reasoning are treated as fundamental prompt–architecture couplings because real tool execution always requires routing, checking, failure handling, and stateful orchestration [2604.04990].

## 4. Memory, context, and knowledge placement

Memory is consistently treated as central to autonomy. A common decomposition separates working memory, episodic memory, semantic memory, and procedural memory. Working memory tracks task-relevant state within an ongoing interaction; episodic memory stores past interactions or experiences; semantic memory stores generalized conceptual knowledge; procedural memory stores action sequences or skills. Because context windows are limited, practical systems rely on chunking, chaining, retrieval, forgetting, and consolidation to determine what is surfaced into the current control loop [2503.12687].

Architecture papers increasingly emphasize that memory is not a single undifferentiated context window. One system-level view distinguishes working memory, episodic memory, and semantic or vector stores, and proposes deterministic interfaces such as `retrieve(query, policy)`, `write(record, policy)`, `page_in(keys)`, and `evict(keys, reason)`. It also stresses provenance, freshness, trust tiers, and strict separation between disposable working memory and durable stores, so that speculative reasoning does not silently become long-term knowledge [2512.09458].

CACA Agent offers a functionally distributed memory design. Profile Capability stores persistent system information such as configuration and accounts; Methodology Capability stores durable process knowledge; Tool Capability stores durable metadata about registered tools; Workflow Capability maintains per-request short-term state. This suggests that one important design choice in agent architectures is not simply whether to use memory, but where to place different kinds of persistence and how to align them with planning, execution, and tool discovery [2403.15137].

Industrial deployments add a further constraint: semantic correctness. “The Semantic Training Gap” identifies a mismatch between domain vocabulary learned in training and domain meaning defined through ontological relationships. Its proposed ontology-grounded tool architecture embeds manufacturing ontology into the tool layer through the three-operation contract `resolve`, `contextualize`, and `annotate`, with invariants that no unresolved domain-entity parameter reaches the database and that all tool calls in a session share the same ontology version. In a controlled experiment across six industry configurations and 72 tool invocations using Qwen3-32B, unconstrained tool parameters yielded a 43% hallucination rate for domain identifiers, while ontology-grounded parameters reduced this to 0% [2605.11234].

## 5. Multi-agent coordination, harnesses, and organizational structure

Multi-agent architectures distribute cognition across specialized entities or roles. Surveys distinguish centralized and decentralized coordination, fixed leaders and peer discussion, and communication styles ranging from shared conversations to structured artifact exchange and publish–subscribe filtering. Leadership is treated as a concrete architectural variable rather than a metaphor: one surveyed organized-team system reported that teams with a designated leader completed tasks nearly 10% faster than teams without a leader, while horizontal collaboration can be useful when consultation and feedback from multiple perspectives matter [2404.11584].

Large comparative studies of public agent-system projects suggest that the decisive engineering choices often lie not in the model but in the surrounding “agent harness.” One source-grounded study of 70 projects identifies five recurring dimensions: subagent architecture, context management, tool systems, safety mechanisms, and orchestration. The corpus distribution for subagent architecture includes None/single-agent only 21 projects (30.0%), Orchestrator-Worker 13 (18.6%), Multi-level Recursive 9 (12.9%), Swarm/Collective 4 (5.7%), and Event-driven 5 (7.1%). The same study argues that subagent complexity co-occurs with context sophistication, and that stronger execution environments co-occur with structured governance [2604.18071].

A domain-specific but architecturally revealing example is AIBuildAI, which organizes autonomous machine-learning engineering through a manager agent plus repository-specific designer, coder, and tuner sub-agents, with setup and aggregator agents at the beginning and end. The manager reasons over multiple isolated repositories, invoking specialists rather than directly performing all reasoning itself; the design is justified as a response to “context explosion” in monolithic single-agent approaches. On MLE-Bench, the system ranks first with a medal rate of 63.1%, including 77.27% on 22 low-complexity tasks, 61.40% on 38 medium-complexity tasks, and 46.67% on 15 high-complexity tasks [2604.14455].

Practitioner studies temper the appeal of ever-larger multi-agent systems. Recurrent industrial advice is to begin with the smallest viable number of agents, use explicit workflows and shared context, and add agents only when measurable gains appear, because additional agents introduce coordination failures, overhead, and new governance burdens. This supports a broader architectural lesson: specialization is valuable, but only when the orchestration substrate can preserve state, authority boundaries, and observability [2604.00189].

## 6. Reliability, governance, and evaluation

A major theme in recent work is that robust agent systems require governance to be built into the architecture. Production-oriented designs repeatedly call for explicit constraints on action selection, verification procedures for critical decisions, interpretability features that expose reasoning for human review, permissioning, accountability, privacy controls, oversight protocols, and security protections. Rule-based components remain useful in this setting because they can encode non-negotiable business logic and safety constraints even when the rest of the system is probabilistic [2503.12687].

The most systematic reliability argument treats containment and bounded authority, validation before action, runtime governance, graceful degradation, and observability as coequal design requirements. Typed schemas, least privilege, validators near tool boundaries, budget limits, why-stopped codes, immutable audit trails, deterministic replay, and sandboxing are presented not as optional best practices but as the basis on which failures become bounded, diagnosable, and recoverable [2512.09458].

Evaluation frameworks have consequently broadened. One survey proposes five criteria: “Task completion effectiveness,” “Efficiency and resource utilization,” “Robustness and reliability,” “Safety and alignment,” and “Interaction quality,” and recommends a staged process from component-level evaluation to integrated system evaluation, controlled scenario testing, limited field trials, and full deployment evaluation [2503.12687]. Another formalizes agent evaluation around success, reward, wall-clock time, trajectory length, token use, tool-call counts, tool-selection accuracy, argument accuracy, tool-execution success, recovery after failure, loop rate, robustness under perturbation, variance across seeds, violation rate, and intervention rate, arguing that agent quality is inherently multidimensional [2601.01743].

Open challenges remain persistent across surveys: hallucination in action, infinite loops, prompt injection, brittle tool use, hidden costs from retries and context growth, limited long-term memory, and reproducibility under changing tools and environments. The literature is increasingly explicit that these are architectural failure modes, not merely prompting errors [2601.12560][2602.10479].

## 7. Domain-specific extensions and emerging directions

As agent architectures move into specialized domains, the same core decomposition is being adapted rather than discarded. In finance, a four-layer architecture organizes agents around data perception, reasoning engine, strategy generation, and execution with control. The paper’s key distinction is between the internal decision object
$$
D_{i,t} = g_i(X_t; A_i, H_i, C_i, V_i, S_i)
$$
and the realized action
$$
q_{i,t} = \Lambda_i(D_{i,t}; A_i, S_i),
$$
so that execution remains filtered through approvals, limits, supervisory checks, and controls. This architecture supports a broader systemic analysis of autonomy depth, heterogeneity, execution coupling, infrastructure concentration, and supervisory observability [2603.13942].

At the edge, a modular reference architecture separates Autonomous Gateway Agents, which run quantized Small Language Models and compressed neural networks locally, from Tethered MCU Agents, which perform only lightweight local processing and delegate higher-level reasoning to cloud coordinators. A cross-cutting Governance Layer provides observability, policy enforcement, and safety across distributed fleets. This suggests that the logical notion of an agent can be decoupled from the hardware tier on which its cognition executes [2606.02862].

Two emerging controversies concern where architecture actually resides. One argues that prompts have become architectural control surfaces: wording alone can change framework choice, validator presence, state stores, and tool orchestration, a phenomenon termed “vibe architecting” [2604.04990]. Another, more practitioner-centered, argues that real-world success depends less on abstract autonomy than on control planes, registries, shared context, protocol maturity, and incremental rollout [2604.00189]. Taken together, these views suggest that future agent systems will likely converge toward more explicit workflow graphs, typed contracts, registries, and layered governance rather than toward unconstrained monolithic autonomy.

A recurring misconception is therefore that an AI agent is simply an LLM with tools. The research record instead presents agent architecture as a software-architectural domain in its own right: a space of control loops, memory hierarchies, tool mediation layers, collaboration topologies, and governance structures designed to make perception, planning, action, and adaptation reliable enough for deployment [2604.18071][2512.09458].

Source: https://www.emergentmind.com/topics/ai-agent-architectures