Designing Reasoning Boundaries in Agentic Systems
In my previous article - https://hackernoon.com/your-ai-agent-should-not-be-talking-to-all-mcp-tools, I talked about exposing repeatable sequences as domain tools. The main LLM should not have to choose among APIs, queries, and Model Context Protocol (MCP) capabilities.
This is a continuation of the article where I will explain internal orchestration and go inside the tool.
A high-level tool may call APIs, query databases, apply rules, and invoke another LLM. Which steps belong in software, and which require model reasoning?
Hiding five low-level calls behind one function does not help if it makes five model calls instead. The decisions are harder to see, but the latency, cost, and inconsistent behavior remain.
Rule of thumb: use software where the result can be computed, and an LLM where it must be interpreted. The hard part is deciding where to draw that line.
Three Execution Layers
Three layers need to be implemented - Ordinary software handles calculations, validation,...
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