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: Orchestrating Multiple LLMs for Enhanced Capabilities
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AI memory shifts from retrieval to compilation with LLM Wiki
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RAG is already becoming the “old way” The future of AI memory is not retrieval.
It’s compilation. Here’s the shift in one sentence: From searching information To structuring knowledge The new model? LLM Wiki Instead of: Chunking documents Running similarity -
AI tool usage at scale: ChatGPT leads with 4.7B monthly visits
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AI tool usage at scale is here. ChatGPT: 4.7B monthly visits (Jan 2025)
Canva: 887M
Google Translate: 595M
DeepSeek: 268M (massive surge) http://
Character.AI: 226M
Perplexity: 133M
Gemini: 118M
Claude: 105M AI isn’t a trend anymore — it’s infrastructure. What’s your #1 -
Better prompts lead to better AI outputs: structure, analysis, conversation, planning.
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Better prompts = better AI outputs • Structured → precision
• Analytical → research
• Conversational → ideas
• Planning → execution Great prompts are frameworks, not guesses. Via Giuliano Liguori (
@ingliguori
) #AI #Prompts #GenAI -
AI Agent Development Roadmap: From LLMs to Orchestration
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The roadmap to building AI agents is becoming clearer Learn LLMs & prompting Add tools & APIs Implement memory Build workflows Orchestrate multi-agent systems Deploy, monitor, improve Great agents are not just intelligent.
They are connected, stateful, -
Claude Features Enable AI Integration into Real Workflows
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Claude is more than a chatbot • Projects → persistent context
• Artifacts → live outputs
• MCP → external tools
• Connectors → enterprise knowledge
• Rules → operational consistency AI becomes powerful when connected to real workflows. Via Giuliano Liguori -
AI Agents for Small Business Automation with n8n
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AI agents for small business • Automate leads
• Automate email
• Automate support
• Connect workflows
• Reduce manual work AI + n8n = scalable automation without complex coding. Via Giuliano Liguori (
@ingliguori
) #AI #Automation #n8n -
AI Agents Beyond LLMs: Orchestration and Architecture
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AI agents are more than LLMs • Skills → expertise
• MCP → external connections
• Subagents → delegated tasks
• Hooks → automation
• Memory → persistent context Real AI systems are orchestration layers. Via Giuliano Liguori (
@ingliguori
) #AI #AIAgents #MCP -
τ Scaling: Beyond Transistor Shrinking for AI Systems
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The proposed “τ Scaling” approach moves beyond traditional transistor shrinking and focuses more on reducing latency and improving efficiency across chips, interconnects, and AI systems. Concepts like LogicFolding, hybrid bonding, Unified Bus, and advanced system-level
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Huawei τ Scaling: Beyond Transistor Shrinking for AI
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Interesting direction from Huawei at ISCAS 2026. The proposed “τ Scaling” approach moves beyond traditional transistor shrinking and focuses more on reducing latency and improving efficiency across chips, interconnects, and AI systems. Concepts like LogicFolding, hybrid