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· Agentic AI

Agentic AI and tool use, explained

MCP is widely credited with bringing "agentic AI" into the mainstream. It's worth separating the trend from the hype — and seeing why a focused, human-adjudicated review tool is a deliberate choice, not a limitation.

From chatting to doing

"Tool use" (or function calling) is what lets a model take an action instead of only producing text. MCP standardized how a model reaches those tools, and that standardization is a big part of why agentic systems moved from demos to production through 2025. In AEC specifically, vendors now name "AI agents" as a defining 2026 trend, alongside predictive design and data-centric engineering.

The autonomy spectrum

Not all "agents" are the same. At one end sits a broad, self-directed agent with many tools and long-running autonomy. At the other sits a focused tool: one defined task, bounded inputs, a structured result. More autonomy buys more capability — and, as the security discussion of recent months made clear, more risk. Where you sit on that spectrum should match the stakes of the work.

Why focused + human-in-loop fits review work

For licensed, liability-bearing work, an autonomous agent that issues decisions on its own is a non-starter. A focused tool that drafts a finding and hands it to a responsible professional is the right shape: you get leverage on the repetitive parts of review without surrendering judgment or accountability.

Where AECdesign.ai sits

AECdesign.ai is deliberately not an open autonomous agent. It exposes focused review tools, returns drafts, and leaves adjudication to the engineer of record. The agentic trend is real and useful; the responsible application of it in AEC is bounded by design.

Sources

Draft, not determination. Every substantive AECdesign.ai result is a draft. A licensed engineer of record must review, revise, accept, or reject each finding before it is issued. AECdesign.ai never provides a sealed determination.

Leverage on review, judgment intact.

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