The usual advice for AI integrations is to feed the model the prompt and let it work out the logic. That advice costs you performance and trust. What you end up shipping looks like a precision tool and behaves like a recommendation engine. What nobody measures is AI Agent Prompt Fidelity: how much of a user’s intent your agent backed with hard data, and how much of it the model inferred.
I thought I had seen every way a database query could be mangled, and then I started auditing agentic systems. Most developers treat an AI agent as a smarter SQL WHERE clause. The trouble starts when a prompt asks for more narrowing than your verified schema can supply, because the LLM does not throw an error. It guesses, quietly. That is where hallucinations come from, and it is why I check prompt fidelity on anything headed for production.
The math behind prompt fidelity
Fidelity comes out of information theory, specifically bits of selectivity. Every constraint in a prompt narrows the search space: “Rock songs,” “under 4 minutes,” “songs with a punchy bass melodic element.” The information a constraint carries is -log2(p) bits, where p is the fraction of the dataset that survives it.
A fidelity score near 1.0 means the agent’s answer rests on your verified tool calls. A low score means it filled the gaps from its own training data, which may be three years stale. So if you are building a WordPress MCP Adapter for an agent, spell out which fields are filterable and which ones are inferred.
<?php
/**
* Simple AI Agent Prompt Fidelity Calculator
*
* @param array $constraints {
* @type float $p The selectivity fraction (0.0 to 1.0)
* @type bool $is_verified Whether the field exists in the database schema
* }
* @return float Fidelity score from 0.0 to 1.0
*/
function bbioon_calculate_prompt_fidelity(array $constraints): float {
$total_bits = 0;
$verified_bits = 0;
foreach ($constraints as $constraint) {
// bits = -log2(p)
$bits = -log10($constraint['p']) / log10(2);
$total_bits += $bits;
if ($constraint['is_verified']) {
$verified_bits += $bits;
}
}
if ($total_bits === 0) return 1.0;
// Fidelity = verified_bits / total_bits
return round($verified_bits / $total_bits, 2);
}
// Example usage: Rock (0.2p, Verified) + Bass-led (0.05p, Inferred)
$prompt_data = [
['p' => 0.2, 'is_verified' => true],
['p' => 0.05, 'is_verified' => false]
];
$score = bbioon_calculate_prompt_fidelity($prompt_data);
// Output will be ~0.35 - A low fidelity "recommendation" prompt.
?>Why agents get away with guessing
Agents don’t report their compression ratio. When Spotify’s AI Playlist agent builds a list of “minor key songs,” it is usually pulling that from the model’s memory, because the key/mode field is never exposed to the agentic layer. That is a design problem rather than a model problem. A better built system would just say so: “I verified the genre and duration, but I am guessing on the musical key.”
The prompts that feel most personal are usually the ones that push past what the verified layer can answer. If you work on enterprise WordPress sites, audit your tool schema. The official WordPress REST API documentation is a good place to check that your agent is not trying to aggregate across O(n²) operations, which ends in either a timeout or a confident invention.
If auditing prompt fidelity is eating your dev hours, hand it to me. I have been wrestling with WordPress since the 4.x days, and I know where the bottlenecks hide.
Audit it before your users do
Every agent has a point past which it stops looking things up and starts substituting. Simple lookups stay accurate; cross-referenced queries are where the silent swaps happen. The fix is deterministic capability discovery: hand the model a fixed list of the fields it can filter on instead of letting it guess. That step is skippable in a prototype and not in anything people trust with their data.
