🧠 AI Agents vs Agentic AI — and Why OrKa Exists

🧭 The Landscape Is Changing

In the growing chaos of “agent everything” hype, a much-needed paper dropped recently:

👉 AI Agents vs Agentic AI: A Conceptual Taxonomy, Applications, and Challenges by Sapkota et al. (MAY 2025)

This is not just another buzzword-salad. It’s the first serious attempt at defining what we actually mean by “agents” in the age of LLMs—and why most “agent frameworks” today are stuck in the wrong paradigm.

This post breaks it down, unpacks the key insights, and shows how they map directly to why I built OrKa: a cognitive orchestration framework designed for actual agentic reasoning, not just task scripting.

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🧠 TL;DR — The Paper in One Sentence

Most current AI “agents” are really just tools. True agentic AI requires goal-driven, self-directed, memory-integrated, and introspectable systems. The gap is massive—and structural.

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🧩 1. The Key Distinction: Agent ≠ Agentic

Park et al. define the AI Agent as a software entity that takes action in an environment—this includes everything from a chess bot to an AutoGPT prompt chain. Most of what the AI world calls “agents” fits here.

But Agentic AI is a different beast:

“An agentic system is not just reactive—it selects, plans, evaluates, and adapts to reach a goal.”

Agentic AI systems must:

• Exhibit goal-driven behavior over time
• Possess internal representations (not just pass-through logic)
• Use memory, context, and reasoning
• Evaluate intermediate outcomes and revise plans

So what the paper is doing is creating a sharp taxonomy to separate LLM-based prompt puppets from true cognitive systems.

It’s a call for systems that think, not just ones that act.

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📐 2. The Taxonomy: Three Axes of Agentic Depth

The authors define three dimensions that any AI system can be evaluated along:

1. Task Type

• Reactive tasks: input → output (e.g., “Summarize this PDF”)
• Iterative tasks: multiple steps, still mostly linear
• Exploratory tasks: multi-objective, open-ended, evolving

2. Cognitive Sophistication

• Scripted: fixed instructions
• Adaptable: uses observations to update plans
• Reflective: revises self-models, learns over time

3. Degree of Autonomy

• Tool-like: only acts when invoked
• Goal-seeking: autonomously acts toward objectives
• Self-improving: evaluates and revises its own strategies

This framework isn’t just theoretical—it lets you diagnose the limitations of today’s LLM-based agent stacks, most of which sit in:

• Reactive 🟡
• Scripted 🟡
• Tool-like 🟡

Even LangGraph, AutoGen, or CrewAI barely move the needle into adaptive territory.

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🚨 3. The Agent Fallacy

The paper makes a brutal but necessary point:

“Labeling a scripted LLM wrapper as an ‘agent’ confuses interface with cognition.”

Boom.

Just because a system can invoke tools or parse a multi-step prompt doesn’t make it agentic. There’s no planning, no internal modeling, no reasoning. Just a glorified function call.

The paper argues we’re stuck in syntactic pipelines, mistaking workflow graphs for intelligence.

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🔁 4. Why This Matters: The Illusion of Progress

Most current frameworks bolt LLMs onto task trees and call it “agency.” But they:

• Lack runtime introspection
• Can’t evaluate outcomes
• Don’t remember or adapt

This gives the illusion of agentic AI, while still operating at the level of brittle scripts.

Park et al. warn that this will stall progress unless we address:

• Representation: how agents understand goals
• Memory integration: episodic and semantic recall
• Adaptivity: planning + self-correction

Without these, “agents” are just fancy tools with RAG.

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⚙️ 5. Where OrKa Fits In

This paper vindicates the OrKa architecture and design philosophy.

OrKa is not an agent framework like AutoGen. It’s a cognitive orchestration system, designed to:

• Define composable mental flows in YAML
• Execute agents based on prior outputs, not hardcoded order
• Include RouterAgents, MemoryNodes, and fallback chains
• Enable full introspection and trace replay
• Model decision-tree logic and dynamic branching

It treats reasoning like a modular graph, not a call stack. And it makes every part of that graph visible and version-controlled.

In short:

OrKa doesn’t just build “agents.” It builds agentic cognition flows.

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🔬 6. Toward Agentic Infrastructure

What the paper calls for—agentic AI with memory, planning, autonomy—is what OrKa is architected to support:

OrKa is still early. But it’s built on the right scaffolding for where agentic AI needs to go.

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🔮 Final Thought

If you’re serious about building actual agentic systems—not just calling OpenAI from a task list—read this paper. Then think deeply about your stack.

The real challenge isn’t just making agents do more.

It’s making them understand what they’re doing—and why.

And if that’s your goal, OrKa’s not a framework.

It’s a lens.

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📎 Links

• Paper: https://www.researchgate.net/publication/391776617
• OrKa GitHub: https://github.com/marcosomma/orka-reasoning
• Orkacore: https://orkacore.com