𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 (𝗣𝗮𝗿𝘁 𝟭)

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 (𝗣𝗮𝗿𝘁 𝟭)

You use chatbots like ChatGPT. They have a limit. They answer once.

Ask a chatbot to find flights and book them. It often fails. It does not fix errors. It does not do complex tasks.

AI Agents are different.

• Chatbots: Reactive. Text only.
• Agents: Proactive. Use tools. Plan steps.

The formula is simple: Agent = LLM + Memory + Planning + Tool Use.

The LLM is the brain. Memory keeps track of you. Planning breaks down goals. Tool use acts on the world.

Agents use a loop. They repeat five steps:

• Perceive: Get input.
• Reason: Think about the next move.
• Plan: Split the goal into parts.
• Act: Use a tool or run code.
• Observe: Check the result.

Andrew Ng shares four ways to design agents:

• Reflection: The agent fixes its own mistakes.
• Tool Use: The agent connects to APIs.
• Planning: The agent splits hard tasks.
• Collaboration: Multiple agents work as a team.

Gartner says 40% of enterprise apps will use agents by 2026.

Avoid all-purpose agents. They fail in production.

Follow these rules for stability:

• Keep the scope narrow.
• Use structured data.
• Output machine-readable commands.
• Use the agent as a Copilot.

Human confirmation is a feature. It makes the system stable.

Next time we build a ReAct agent with pure Python. No frameworks.

Source: https://dev.to/alvinzhang/building-ai-agents-from-scratch-part-1-core-architecture-and-underlying-principles-explained-46fk Optional learning community: https://t.me/GyaanSetuAi