A demo is easy. Getting the same agent to behave in production is not, and the gap between the two is usually a missing LLM Agent Evaluation step. That is why the “smart” assistant hallucinates, or quietly burns compute at 2 AM while nobody is watching. After 14 years of WordPress and messy backend logic, the pattern is familiar: we build something sophisticated and never prove it works before we ship it.
Normal software testing assumes determinism. Input X, assert output Y. An LLM will hand you three different phrasings of the same correct answer. Put a router in front of several specialist agents and the number of things that can go wrong multiplies instead of adding up. So if you are shipping something like a plugin directory AI agent, a quick “vibe check” on a few prompts tells you almost nothing. You need a framework.
Three failure modes offline LLM agent evaluation catches
Offline evaluation runs before deployment, against a curated dataset, and it is your quality gate. Three failure modes are worth measuring separately.
1. Routing accuracy and over-routing
The router decides which agent ever sees the query, so it fails in two directions. Under-routing sends a complex question to a lightweight agent and you get a shallow answer back. Over-routing is the expensive one: “What is the stock price?” wakes up a heavy research agent that retrieves ten documents nobody asked for. I watched one project pay 500% more than it needed to because the router was miscalibrated. Track the over-routing rate. It shows up on the invoice before it shows up in the output.
2. Using a stronger model as the judge
Nobody is going to read 1,000 test cases by hand every time a prompt changes. So you hand the job to a more capable model, Claude 3.5 Sonnet or GPT-4o, and give it a rubric to score against: is the answer factually right, is the reasoning sound, is anything missing. Ask that judge for structured JSON and the whole pipeline drops into CI/CD without a human in the loop.
3. RAG metrics and faithfulness
An agent that retrieves its own context can still hallucinate on top of documents it actually read, which is the confusing kind of wrong. Frameworks like RAGAS score the retrieval pipeline, and the number I watch is faithfulness: did the answer come from the retrieved context, or did the model fill the gap on its own? Below 85% I would not put it in front of users.
Building the evaluation dataset
The ground truth dataset comes before any agent logic. One sample in an LLM Agent Evaluation suite looks about like this:
{
"id": "eval_001",
"query": "Compare WooCommerce vs Shopify for high-volume headless setups",
"category": "comparison",
"expected_agent": "research_specialist",
"ground_truth_facts": [
"WooCommerce allows full ownership of data",
"Shopify Plus starts at $2,000/month"
],
"metrics_threshold": {
"accuracy": 0.9,
"faithfulness": 0.85
}
}On WordPress I usually wrap these checks in a WP-CLI command or a background task, so a deployment can be blocked when the scores come in under threshold. The gate itself is not complicated:
<?php
/**
* Simple quality gate for LLM deployments
*/
function bbioon_verify_agent_quality( $results ) {
$min_accuracy = 0.90;
foreach ( $results as $test_case ) {
if ( $test_case->accuracy < $min_accuracy ) {
// Log the bottleneck and fail the build
error_log( "LLM Evaluation Failed on ID: " . $test_case->id );
return false;
}
}
return true;
}Tools like Langfuse store the traces, so you can look back and see how better feature logic moved the numbers over time.
If this LLM Agent Evaluation work is eating your dev hours, I can take it off your hands. I have been building on WordPress since the 4.x days, and most of that time has gone into closing the distance between what a demo promises and what production actually does.
Beyond the vibe check
“The demo went well” is not evidence. A systematic offline suite gives you an audit trail for stakeholders and catches regressions before your users do. Fifty samples is enough to start with. Write the judge prompt with a real rubric, automate the run, and let the numbers do the arguing. All the best and happy building.
