AI agents vs chatbots and how to debug multi‑model AI APIs

AI agents are autonomous systems that analyse data, understand context and make real‑time decisions, enabling proactive tasks such as booking flights or handling complex customer workflows. Chatbots, by contrast, are task‑specific software that follow predefined scripts or limited AI to answer simple queries like order tracking, escalating to human operators when needed. Key differences include autonomy, learning capability, personalization, task complexity, integration depth and cost.

When AI products combine several large‑language models—e.g., GPT, Claude, Gemini, or region‑specific models—debugging becomes an infrastructure challenge. Failures can range from authentication errors and high latency to invalid JSON outputs, tool‑call instability, or silent cost increases. Effective debugging requires a taxonomy of failure types, comprehensive request‑level logging (including workflow, selected model, provider, token usage, latency, retries, fall‑backs and validation results) and a focus on the workflow rather than just the model. Monitoring fallback behavior is also essential to detect hidden issues even when a backup model returns an answer.