How I Engineer High-Performing AI Agents

How I Engineer High-Performing AI Agents: Technical Guide with Code, Evals & Human Feedback

Building AI agents that genuinely deliver requires more than prompt tuning or clever automation. My experience shows that rigorous measurement, benchmarking, and continuous refinement transform agents from mere demos into robust, production-grade tools. This post shares how I apply advanced evaluation pipelines, feedback loops, and technical best practices using GitHub Copilot and Microsoft Foundry, with code samples and implementation patterns.

Step 1: The Copilot Workflow — Immediate Code & Prompt Testing

During day-to-day development, I rely on GitHub Copilot for code generation, automation, and quick agent iteration. Key practices here include:

Example: Fast, Testable Python Agent Skeleton

ai_agent.py

import openai

class SimpleAgent:
    def __init__(self, prompt):
        self.prompt = prompt
        self.model = "gpt-4"

Why this matters: Having a clear skeleton lets me quickly swap prompts or logic, write targeted tests, and catch errors before scaling.

Step 2: Building Evaluators — Measuring What Matters

Quantitative: Automated Unit Evals

To measure correctness, I design simple eval scripts to run agent responses against gold-standard answers.

agent_eval.py

def evaluate_agent(agent, test_cases):
    """
    Runs agent on test_cases: [(input, expected_output)], returns eval results.
    """
    results = []
    for inp, exp in test_cases:

Technical note: This pattern is model-agnostic. It's also easy to extend for more complex checks (e.g., semantic similarity, security, code style).

Qualitative: Human-in-the-Loop Feedback

Automated checks aren't enough for real-world edge cases. I gather human feedback, especially for subjective or business-specific criteria. Microsoft Foundry and similar frameworks make this scalable.

Example: Simple Feedback Collector (Python)

human_feedback.py

import csv

def collect_feedback(agent, samples, feedback_file="feedback.csv"):
    """
    Presents samples to a human and logs feedback for future tuning.
    """

Why this matters: Human review flags errors the automated pipeline misses, and the feedback lets me fine-tune agent settings and prompts iteratively.

Step 3: Scoring Frameworks & Continuous Benchmarking

Formal benchmarks like success rate, average response time, and "business rule compliance" create actionable metrics. In Foundry, I automate data collection and use structured datasets—making it easy to monitor trends and spot quality dips early.

Example: Python Agent Scoring Pipeline

scoring_pipeline.py

import statistics

def score_results(results):
    """Calculate key metrics from an eval results list."""
    pass_pct = 100 * sum(r['passed'] for r in results) / len(results)
    return {

Interesting fact: This benchmarking loop is a scaled-down example of what's possible in Microsoft Foundry, which adds dataset management, annotation tools, and integrated dashboards.

Step 4: Feedback Iteration and Agent Tuning

Once I gather feedback (human and automated), I update prompts, retrain or fine-tune models, and rerun evals. This cycle keeps pushing my agents' reliability higher as workflows grow.

Example in JavaScript (for web agents):

agent-eval.js

// Evaluator for a simple web LLM agent
const testCases = [
  {input: "2+2?", expected: "4"},
  {input: "Fruit in Paris?", expected: "Parisian apple"}
];

Note: This structure is similar to the Python pipeline and is the backbone for any production-grade evaluation suite.

Final Takeaways & Resources

Evaluating Generative AI Apps (Microsoft Docs)

Interesting Technical Notes