Back

9 Steps to Build AI Agents from Scratch. I've given a simple step by step explanation. 𝗦𝘁𝗲𝗽 1: 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗠𝗶𝘀𝘀𝗶𝗼𝗻 & 𝗥𝗼𝗹𝗲 • Decide what problem the agent will solve. • Figure out who will use it. • Plan how users will interact with it (chat, voice, API, etc.). 𝗦𝘁𝗲𝗽 2: 𝗗𝗲𝘀𝗶𝗴𝗻 𝗜𝗻𝗽𝘂𝘁 & 𝗢𝘂𝘁𝗽𝘂𝘁 𝗙𝗹𝗼𝘄 • Define what kind of input the agent accepts (text, voice, data). • Decide how results are returned (text, charts, reports). • Use standard formats (like JSON, APIs) so it’s reliable. 𝗦𝘁𝗲𝗽 3: 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝗿𝗼𝗺𝗽𝘁𝘀 • Write clear instructions for the AI. • Add tone, style, and domain knowledge. • Improve with fine-tuning or prebuilt libraries. 𝗦𝘁𝗲𝗽 4: 𝗘𝗻𝗮𝗯𝗹𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 & 𝗧𝗼𝗼𝗹𝘀 • Connect the AI to tools (databases, calculators, APIs). • Use logic frameworks so it can think + act. • Make it capable of solving complex problems step by step. 𝗦𝘁𝗲𝗽 5: 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗲 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • Use multiple agents with different roles (planner, executor, checker). • Let them work together in a controlled workflow. • Agents communicate for better efficiency. 𝗦𝘁𝗲𝗽 6: 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 • Decide if the agent remembers only short-term (per session) or long-term. • Store chat history and summaries. • Use memory embeddings so the agent can recall past context. 𝗦𝘁𝗲𝗽 7: 𝗔𝗱𝗱 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 (𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹) • Allow speech-to-text and text-to-speech. • Support images, videos, or vision-based tasks. • This makes the agent more interactive and richer in experience. 𝗦𝘁𝗲𝗽 8: 𝗙𝗼𝗿𝗺𝗮𝘁 & 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 • Present results as dashboards, reports, or APIs. • Make outputs both human-friendly and machine-readable. • Allow export as charts, PDFs, or JSON. 𝗦𝘁𝗲𝗽 9: 𝗗𝗲𝗽𝗹𝗼𝘆 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 • Launch the agent with an API or UI. • Integrate with apps, Slack, or CRMs. • Monitor its performance with tracking tools. A simple yet powerful framework for anyone who wants to get started with building and deploying AI agents. ✅ Repost for others in your network who can benefit from this.