Building AI Agents: From Chatbots to Autonomous Workflows

Chatbot vs Agent

A chatbot responds to messages. An agent takes actions:

• Calls APIs
• Queries databases
• Writes files
• Orchestrates multi-step workflows

Agent Architecture

User Goal
    ↓
Planner (LLM decides steps)
    ↓
Tool Executor (APIs, code, search)
    ↓
Memory (conversation + state)
    ↓
Response / Action

Core Patterns

ReAct (Reason + Act)

The model alternates between thinking and using tools.

Plan-and-Execute

First create a plan, then execute each step.

Multi-Agent

Specialized agents collaborate (researcher, coder, reviewer).

Production Considerations

• Guardrails: Validate tool inputs/outputs
• Human-in-the-loop: Approve sensitive actions
• Observability: Log every tool call and LLM response
• Cost caps: Set max tokens and API calls per session

Getting Started

1. Build a single-tool agent (web search or calculator)
2. Add memory across turns
3. Introduce a second tool
4. Add error handling and retries

Agents are powerful but complex. Master RAG and prompting first.