4 Things Everyone Must Know About LLMs and Agentic AI

2026-03-04 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

An Agentic AI Engineer shares four critical insights for working with Large Language Models (LLMs) and agentic AI, emphasizing that advanced prompting alone is insufficient for resolving most LLM issues. The article highlights the importance of researching specific LLM models to match use cases, advocating for single-task agents rather than multi-tasking ones to prevent performance degradation. It also addresses the challenge of managing the context window, proposing the use of Domain-Specific Languages (DSLs) to reduce token count significantly, citing an example where a DSL dropped API call tokens from ~20,000 to ~1,600 while improving output quality. Finally, the author recommends one/few-shot prompting for ensuring structural adherence in LLM outputs.

Key takeaway

For AI Engineers designing agentic systems, prioritize architectural decisions over prompt engineering. Ensure your agents are highly specialized for single tasks, as this significantly improves reliability. Implement Domain-Specific Languages (DSLs) to efficiently manage context windows and reduce token usage, which can drastically improve output quality and consistency. Additionally, leverage one/few-shot prompting to enforce desired output structures, saving iteration time.

Key insights

Effective LLM and agentic AI development prioritizes model selection, task simplification, context management, and structural prompting over complex prompts.

Principles

• Prompting mitigates, but doesn't solve, core LLM problems.
• Agents excel at single, clearly defined tasks.
• LLMs follow examples well for structural adherence.

Method

To optimize LLM performance, research model capabilities, simplify agent tasks into single responsibilities, manage context with DSLs, and use one/few-shot prompting for output structure.

In practice

• Research specific LLM documentation for use case suitability.
• Split complex agent duties into multiple specialized agents.
• Implement a simple DSL to manage LLM context windows.

Topics

• Agentic AI
• Large Language Models
• Context Window Management
• Domain-Specific Language
• Few-Shot Prompting

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.