Agentic AI Unveiled: Dual Paradigms, Architecture Battles, and Future Directions

Introduction

The article "Agentic AI: a comprehensive survey of architectures, applications, and future directions" (Springer Nature) provides a systematic review of AI agents, distinguishing two fundamentally different technology trees: symbolic/classical and neural/generative.

TL;DR (Key Takeaways)

LLM agents should not be forced into the old perception‑planning‑action‑reflection loop; they belong to a distinct tech tree.

The authors propose a dual‑paradigm framework:

A PRISMA‑based review of 90 papers (2018‑2025) reveals domain‑specific preferences and a strong bias toward neural approaches in recent years.

The future lies in hybrid architectures that combine reliability with flexibility.

Why “Concept Retrofitting” Is Misleading

Traditional surveys often map LLM agents onto symbolic frameworks such as BDI or PPAR, which the authors argue is akin to translating an electric‑car manual into steam‑engine terminology—seemingly coherent but fundamentally inaccurate. A re‑classification based on underlying mechanisms is required.

Dual‑Paradigm Overview

The two technology trees are visualized as follows:

Methodology: PRISMA Screening Process

The authors applied the PRISMA systematic‑review protocol:

Initial records: 157

After deduplication: 120

Screened abstracts: 78

Added 12 classic symbolic papers

Final corpus: 90 papers

Keyword groups used for coding:

Symbolic : BDI agent, SOAR, POMDP, …

Neural : LLM agent, prompt chaining, AutoGen, …

Two independent coders achieved Cohen’s κ = 0.82, indicating high reliability.

Results Panorama: Paradigm × Domain × Time

Three‑dimensional visualizations show:

2018‑2021 dominated by symbolic (blue points); 2022‑2025 a surge of neural (orange points).

High‑risk sectors (medical, legal) favor symbolic or mixed approaches.

Data‑rich sectors (finance, education) are overwhelmingly neural.

Architecture Showdown: Symbolic vs. Neural

Core Mechanism : Symbolic uses MDP/POMDP + rule engine; Neural relies on prompt chaining + tool calls.

State Management : Explicit belief base vs. implicit context window.

Verifiability : High (formalizable) vs. Low (requires posterior explanation).

Typical Frameworks : SOAR, JADE vs. AutoGen, CrewAI, LangChain.

Multi‑Agent Collaboration Models

Coordination : Contract‑net/blackboard (symbolic) vs. structured dialogue/role division (neural).

Decision Making : Deterministic auction vs. LLM‑driven random routing.

Traceability : Full audit log vs. black‑box with only prompt dumps.

Evaluation Metrics Beyond Accuracy

Symbolic side : goal‑completion fidelity, plan optimality, formal verification.

Neural side : long‑term task success rate, tool‑call robustness, token cost, latency.

Future benchmarks must be “paradigm‑aware” to avoid mismatched evaluation.

Hybrid Architecture: Highest Demand, Lacking Standards

The survey identifies nine major gaps where hybrid solutions are needed, specifying what each paradigm must contribute and how they can interoperate.

Ethical Governance Across Paradigms

Transparency : Symbolic systems are inherently white‑box; neural systems require SHAP/LIME and decision logs.

Safety Attacks : Symbolic vulnerable to logical bombs; neural vulnerable to prompt injection.

Responsibility : Symbolic traceable to rule engineers; neural attribution is a black hole, potentially prompting strict‑liability legislation.

Reference

https://link.springer.com/article/10.1007/s10462-025-11422-4
Agentic AI: a comprehensive survey of architectures, applications, and future directions