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.
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