Part 2 of 4
🤖 Ghostwritten by Claude · Curated by Tom Hundley
This article was written by Claude and curated for publication by Tom Hundley.
The difference between a demo and a product is how it handles failure.
When an LLM (Large Language Model) uses a tool—whether its a calculator, a SQL query, or an API call—it is essentially guessing the arguments. It predicts: I should call search_database with query=Q4 Revenue.
But what if it calls it with query=123? Or query=None?
In a Python script, you get a TypeError and the program crashes. In an Agentic System, a crash is unacceptable. The agent needs to catch the error, understand it, and try again.
At Elegant Software Solutions, we enforce a strict rule: No Tool Left Behind (Without a Schema).
We use Pydantic, a data validation library for Python, to define the strict contract for every tool our agents use.
def search(query):
# What is query? A string? A dict?
# What happens if its empty?
return db.execute(query)from pydantic import BaseModel, Field
class SearchInput(BaseModel):
query: str = Field(..., description=The search term. Must be at least 3 characters.)
limit: int = Field(10, ge=1, le=100, description=Max results to return.)
def search(args: SearchInput):
# If the LLM sends limit: 200, Pydantic raises a validation error automatically.
# We catch that error and send it BACK to the LLM.
passWhen we use robust definitions, we enable a Self-Healing Loop:
search(limit=200)Error: limit must be = 100limit=100.search(limit=100) - Success.This philosophy is core to Anthropics Model Context Protocol (MCP), which we champion. MCP standardizes how these tool definitions are shared between the server (your database) and the client (Claude/OpenAI).
By adopting MCP, we ensure that your agents arent just guessing at your APIs—they are reading a strict, type-safe manual on how to use them.
Reliability isnt magic. Its validation. By treating LLM inputs with the same suspicion we treat user inputs on a web form, we build agents that can survive in the wild.
This article is a live example of the AI-enabled content workflow we build for clients.
| Stage | Who | What |
|---|---|---|
| Research | Claude Opus 4.5 | Analyzed current industry data, studies, and expert sources |
| Curation | Tom Hundley | Directed focus, validated relevance, ensured strategic alignment |
| Drafting | Claude Opus 4.5 | Synthesized research into structured narrative |
| Fact-Check | Human + AI | All statistics linked to original sources below |
| Editorial | Tom Hundley | Final review for accuracy, tone, and value |
The result: Research-backed content in a fraction of the time, with full transparency and human accountability.
Were an AI enablement company. It would be strange if we didnt use AI to create content. But more importantly, we believe the future of professional content isnt AI vs. Human—its AI amplifying human expertise.
Every article we publish demonstrates the same workflow we help clients implement: AI handles the heavy lifting of research and drafting, humans provide direction, judgment, and accountability.
Want to build this capability for your team? Lets talk about AI enablement →
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