讓工具使用型智能體真正完成任務
Tool-use agents that actually finish the job
工具使用型智能體要可靠完成任務，關鍵在於工具合約、權限邊界和停止條件，而非單靠模型能力。建議為每項工具設定嚴格的 JSON Schema、分層權限、明確停止條件及可追蹤記錄，並在人手批准下處理金錢、刪除和對外發送。評估亦應涵蓋工具故障時的任務完成能力，而不只測試理想情況。
The pattern: Agent demos look magical for three tool calls, then stall — wrong tool, empty result, infinite retry, or a confident answer that never checked the system of record. Shipping agents is less about picking a framework and more about making tool use finishable.
Why it matters: In 2026 the bottleneck is not model IQ. It is tool contracts, permission boundaries, and stop conditions. Teams that treat tools like APIs with schemas and SLAs ship; teams that treat tools like magic strings keep demoing.
What actually works
- Strict schemas — JSON Schema (or equivalent) on every tool. Reject malformed args before the side effect.
- Permission layers — read vs write vs irreversible. Human approval for money, delete, and external send.
- Explicit stop conditions — max steps, budget, and a "done when" checklist the agent must satisfy.
- Observable traces — every tool call logged with args, latency, and outcome. If you cannot replay it, you cannot debug it.
MCP and the tool market
Model Context Protocol and similar tool buses help standardize discovery, but they do not invent good tools. A poorly designed MCP server is still a poorly designed API. Prefer fewer, well-tested tools over a kitchen-sink catalog the model will misuse.
What we'd watch next
Agent evals that score task completion under tool failure, not just golden-path benchmarks. Production reliability shows up when the database is slow, the ticket is missing, or the user changes their mind mid-loop.
Originally published on ayraix.com, practical AI for enterprise builders.
來源：DEV Community · dev.to