The Secret Life of Azure: The Judge and the Jury
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The Secret Life of Azure: The Judge and the Jury
Building reliable systems with automated evaluation
#Azure #AIAgents #Evaluation #LLMAsAJudge
🎧 Audio Edition: Prefer to listen? Check out the expanded AI podcast version of this deep dive on YouTube.
📺 Video Edition: Prefer to watch? Check out the 7-minute visual explainer on YouTube.
Evaluation & Quality
The whiteboard was filled with the flowcharts from our last session, but Timothy was staring at a set of logs with a frustrated expression.
"Margaret," Timothy said, "the system is running, but the quality is inconsistent. Sometimes the Extraction Agent misses a field, and then the Inventory Agent tries to log null data. It's a chain reaction of errors. I can’t sit here and manually check every single execution trace."
Margaret picked up a green marker and drew a new box that sat outside the main workflow, connected to the output of every agent.
"That's because you're treating the output as a 'black box,' Timothy. In a production system, you don't just hope the agents are correct. You build an Evaluation Loop. We need to move from manual spot-checks to Automated Evaluation."
The Evaluator: LLM-as-a-Judge
"How do we automate judgment?" Timothy asked. "An assertion in code can't tell if a summary is 'accurate' or if a tone is 'professional'."
"You use a stronger agent to grade a weaker one," Margaret explained. "We deploy an Evaluator Agent with a specific set of rubrics. It doesn't participate in the workflow or modify the data; it sits above it as a watcher. After each step—or at the very end—the Evaluator compares the output against the source and gives it a score or a simple pass/fail grade based on 'Faithfulness' and 'Relevancy'."
The Feedback Loop: Self-Correction
"So the Evaluator just tells me it failed?" Timothy asked.
Margaret drew an arrow from the Evaluator back to the orchestrator.
"No, it tells the orchestrator. If the Evaluator detects an error—like a missing field or a hallucination where the agent made up data—it sends a 'Correction Prompt' back to the specialized agent. It’s a closed loop. The agent sees exactly what it missed and retries the task until the Evaluator gives it a passing score."
The Test Suite: Ground Truth
"But how do I know the Evaluator is right?" Timothy questioned.
Margaret wrote the words Ground Truth at the top of the board.
"We create a 'golden dataset'—a set of inputs where we already know the perfect outputs. We run our agents against this set and let the Evaluator calculate the accuracy. It gives us a 'System Grade.' If we update the model or change a prompt, we re-run the suite to make sure our score doesn't drop. This is how you prevent regressions."
The Result
Timothy looked at the logs again, but this time they were clean. The Evaluator was catching the small errors in the background, and the agents were correcting themselves before the user ever saw a mistake.
"It’s not just a team anymore," Timothy said. "It’s a self-correcting system."
Margaret capped her marker and smiled. "Exactly. When you stop guessing and start measuring, the architecture finally becomes production-ready."
The Core Concepts
- LLM-as-a-Judge: Using a high-reasoning model to evaluate the performance and logic of other agents.
- Evaluation Rubrics: Structured criteria (categorical pass/fail or numeric scores) used to grade agent output.
- Self-Correction: A workflow where an agent receives feedback from an evaluator and retries the task to fix errors.
- Ground Truth: A curated dataset of "perfect" answers used to benchmark and version the system.
- Faithfulness: A metric measuring how much of the agent's response is actually supported by the source data.
Aaron Rose is a software engineer and technology writer at tech-reader.blog. For explainer videos and podcasts, check out Tech-Reader YouTube channel.
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