How to build an AI Agent

 

Agents are the most valuable skill in AI and product right now. So why not build one? Here's how:

𝐒𝐭𝐞𝐩 𝟏: 𝐃𝐞𝐟𝐢𝐧𝐞 𝐚 𝐬𝐲𝐬𝐭𝐞𝐦 𝐩𝐫𝐨𝐦𝐩𝐭

It defines the goals, logic, and expectations.

Free guides:

• GPT-4.1 Prompting Guide: https://lnkd.in/dt8FxriE

• Anthropic Prompt Engineering: https://lnkd.in/dc-kucif

• Prompt Engineering by Google: https://lnkd.in/dEU2Y_9v

[Extra] 11 AI Agent Prompting Principles: https://lnkd.in/d8nGFFEC

𝐒𝐭𝐞𝐩 𝟐: 𝐒𝐞𝐥𝐞𝐜𝐭 𝐚𝐧 𝐋𝐋𝐌

Unless the framework handles iterating (e.g., n8n), start with a reasoning model (e.g., o1-mini).

𝐒𝐭𝐞𝐩 𝟑: 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐭𝐨𝐨𝐥𝐬

What might your AI agent need to achieve its goals? Consider simple tools, like a calculator, custom functions, integrations, data sources, and MCP servers.

𝐒𝐭𝐞𝐩 𝟒: 𝐏𝐫𝐨𝐯𝐢𝐝𝐞 𝐦𝐞𝐦𝐨𝐫𝐲

The agent must track it's progress and learn. Most platforms support:

• Short-term memory (variables, last interactions)

• Long-term memory (vector, SQL, graph)

𝐒𝐭𝐞𝐩 𝟓: 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐞 𝐭𝐡𝐞 𝐥𝐨𝐠𝐢𝐜

Whether a single agent or multiple agents working together, you must:

• Map/code repeatable logic (flow) that doesn't belong to specific agents

• Orchestrate communication between AI agents (static or dynamic)

You might also like the AI Agent Architectures With n8n 

𝐒𝐭𝐞𝐩 𝟔: 𝐀𝐝𝐝 𝐔𝐬𝐞𝐫 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞

If your AI agent is user-facing, you can easily add logic using tools like Lovable, Bolt, or Google Firebase. No coding.