7 tips for building better AI chat agents

Building a reliable developer-facing chat agent requires attention to responsiveness, durability, and operational feedback beyond model quality alone. The authors recommend delivering an immediate streamed model response while a longer-running agent initializes, accurately measuring conversation length using the latest step’s input tokens to avoid premature context compaction, and limiting repeated failed tool calls while allowing internal retries for transient errors. They emphasize durable, resumable event streams and ordered message inboxes so chats recover from disconnects and can handle queued or steering user messages, with visible tool activity tailored to the audience. Prompt versions should be stored, associated with models, and switchable without deployment to support controlled iteration and evaluation. Agent tools should clearly communicate limits, incorporate user context, support custom UI rendering, feedback, subagents, and sandboxed execution where useful. Finally, sampled completed turns should be evaluated asynchronously and paired with detailed observability, allowing teams to identify failures such as hallucinations, missing tools, or documentation gaps and connect results to specific prompt versions.

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