AI Agents ≠ single LLM They’re systems of models • General LLMs → reasoning
• Domain LLMs → expertise
• RAG → real-time data
• Tools → actions & automation
• Open-source → control & privacy Real power = orchestration of multiple LLMs Via Giuliano Liguori
@ingliguori
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AI Agents Are Multi-Model Systems Powered by Orchestration
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Better Prompting Styles Unlock Different AI Model Outputs
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You don’t need a better AI model. You need a better prompting style. Structured → precision Analytical → accuracy Conversational → speed Planning → execution Same model. Different outputs. The model doesn’t change
Your prompting style does Follow -
Main LLM Types Powering AI Agents Explained
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AI Agents = LLMs + orchestration Here are the main types of LLMs powering them General-purpose (GPT, Claude) Domain-specific (Legal, Finance, etc.) RAG-based (real-time knowledge) Tool-augmented (API actions) Open-source (LLaMA, Mistral) The game is no
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Decoding the AI 2027 Scenario From Agent-1 to Superintelligence
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From Agent-1 to Superintelligence: Decoding the AI 2027 Scenario and Its Profound Implications Check out my article: https://
linkedin.com/pulse/from-age
nt-1-superintelligence-decoding-ai-2027-its-giuliano-liguori–vb1vf
… Via @ingliguori #AI2027 #FutureTech -
Scaling AI Evolution: From Symbolic Logic to Autonomous Agents
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Scaling Ascent Peak: The Seven Summits of Artificial Intelligence In my latest article, I chart the evolution of AI—from the early days of symbolic logic to today’s autonomous agents, and what lies beyond. This journey is more than a history lesson: it’s a strategic framework
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MCP, RAG, and AI Agents: A Complementary Stack Explained
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MCP vs RAG vs AI Agents MCP → connects tools
RAG → connects knowledge
Agents → execute tasks Not competitors.
A stack. The advantage?
How you combine them. Where are you investing first? #AI #Agents #RAG #Tech -
AI Agents Levels From Rule-Based to Autonomous Execution
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AI agents have levels 1. Rule-based (if-then automation) 2. Tool-using assistants 3. Strategic multi-step agents 4. Context-aware autonomous agents 5. Superintelligent digital personas (theoretical AGI) We’re moving from “AI that responds” → to “AI that executes
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9 Steps to Build Functional AI Agents from Scratch
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How to build AI agents from scratch (9 steps): 1. Purpose & scope 2. I/O schemas 3. System instructions 4. Reasoning + tools 5. Multi-agent orchestration 6. Memory & context 7. Multimodal 8. Structured outputs 9. UI / API Ship agents that do work, not just talk.
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AI Tools Comparison: Right Tool for Right Job
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Cheat sheet: ChatGPT = create/build (writing, coding, workflows)
Grok = live trends + punchy takes
Gemini = Google Workspace-native collaboration
Claude = deep reading + long-doc reasoning
Perplexity = research w/ citations Right tool for the right job.
#AI #LLM #Productivity -
20 Must-Try AI Tools for 2025: Complete Stack
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20 must-try AI tools for 2025. This stack covers everything: chat & reasoning image gen video gen voice/music AI search automation AI agents My top 3 right now: ChatGPT, Perplexity, Midjourney. What are yours? #AI #AITools #GenerativeAI #Automation