AI Agents: Why exactly do you need them?

2026-02-28 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

This article explores the necessity and differentiating capabilities of AI agents compared to standalone Large Language Models (LLMs), using the Google ADK agentic framework and the "gemini-3-flash-preview" LLM. The author demonstrates an agent's superior performance in five key areas: contextual reasoning, handling multi-turn conversations, managing multiple concurrent tasks, memory retention/recall without explicit configuration, and implementing guardrails against inappropriate input. For instance, the agent successfully interpreted incomplete queries, maintained context across turns to book a reservation, executed three distinct functions (booking, menu listing, online order) simultaneously, summarized past discussions, and calmly responded to profane language without explicit prompting. These capabilities highlight how agentic frameworks overcome the limitations of plain LLMs in complex, interactive scenarios.

Key takeaway

For AI Engineers developing conversational applications, relying solely on a plain LLM will lead to significant programmatic overhead for features like entity recognition, tool routing, and conversational intelligence. You should integrate agentic frameworks like Google ADK to simplify the development of human-like assistants, ensuring robust handling of multi-turn conversations, complex tasks, and implicit guardrails without extensive custom coding.

Key insights

AI agents provide crucial conversational intelligence and task management capabilities beyond standalone LLMs.

Principles

• Agents excel at contextual reasoning.
• Agentic frameworks simplify complex LLM interactions.

Method

The author compared an LLM within an agentic framework against a plain LLM across five conversational capabilities: contextual reasoning, multi-turn handling, multi-tasking, memory, and guardrails.

In practice

• Use agentic frameworks for complex user interactions.
• Implement agents for robust conversational AI.
• Leverage agents for implicit memory and guardrails.

Topics

• AI Agents
• Agentic Frameworks
• Large Language Models
• Conversational AI
• Tool Use

Code references

• MetaAditya/GoogleADK-agent

Best for: AI Engineer, Machine Learning Engineer, AI Chatbot Developer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Naturallanguageprocessing on Medium.