Agents are the most valuable skill in AI and product right now. So why not build one? Here's how:
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It defines the goals, logic, and expectations.
Free guides:
β’ GPT-4.1 Prompting Guide: https://lnkd.in/dt8FxriE
β’ Anthropic Prompt Engineering: https://lnkd.in/dc-kucif
β’ Prompt Engineering by Google: https://lnkd.in/dEU2Y_9v
[Extra] 11 AI Agent Prompting Principles: https://lnkd.in/d8nGFFEC
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Unless the framework handles iterating (e.g., n8n), start with a reasoning model (e.g., o1-mini).
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What might your AI agent need to achieve its goals? Consider simple tools, like a calculator, custom functions, integrations, data sources, and MCP servers.
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The agent must track it's progress and learn. Most platforms support:
β’ Short-term memory (variables, last interactions)
β’ Long-term memory (vector, SQL, graph)
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Whether a single agent or multiple agents working together, you must:
β’ Map/code repeatable logic (flow) that doesn't belong to specific agents
β’ Orchestrate communication between AI agents (static or dynamic)
You might also like the AI Agent Architectures With n8n
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If your AI agent is user-facing, you can easily add logic using tools like Lovable, Bolt, or Google Firebase. No coding.
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