Reactive: user sends message, model generates response, exchange is complete. No actions in the world — only text output. Predictable and isolated failures.
Right for: Q&A, summarization, drafting, translation, explanation.
Proactive loop: think → act → observe → repeat until done. Can interact with external systems. Less predictable — errors compound across steps.
Right for: multi-step tasks, external tool use, tasks where the sequence is not known in advance.
The chatbot-agent distinction is a spectrum. A chatbot with access to one web search tool is slightly agentic. A fully autonomous coding agent that writes, runs, reads errors, fixes, and iterates is deeply agentic. Most production systems sit somewhere in between.
A customer support assistant that answers questions from a knowledge base is a chatbot. The same assistant that can also look up order status via API, issue a refund, and create a follow-up ticket is an agent — it takes actions with real-world consequences.
Use a chatbot when the task requires one response, no external systems are involved, user control over each step matters, or latency is critical.
Use an agent when the task has multiple dependent steps, the right sequence is not predetermined, external tools are needed, or the task involves trial-and-error iteration.
Chatbot errors are isolated — one bad response doesn't affect the next. Agent errors compound — an early mistake can derail the entire task. Agents require explicit stopping conditions, error handling, and human-in-the-loop checkpoints for high-stakes actions.
Ask the AI assistant about agents vs chatbots, when to use each architecture, or how to handle the reliability tradeoff in agents.