Project 18: Agent Evaluation Forge (Benchmarks, Regression, Red Team)

Build a full evaluation and benchmarking system for assistants with deterministic replays, adversarial testing, and release gates.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 20-35 hours
Main Programming Language Python
Alternative Programming Languages TypeScript, Go
Coolness Level Level 4: Hardcore Tech Flex
Business Potential 3. The “Service & Support” Model
Prerequisites testing frameworks, metrics basics, data pipelines
Key Topics success metrics, synthetic datasets, regression harness, red-team testing

1. Learning Objectives

  1. Define robust KPIs for assistant performance and reliability.
  2. Build automated task harnesses for repeatable evaluations.
  3. Generate synthetic/adversarial workloads.
  4. Run regression tests across model/router/prompt versions.
  5. Gate deployments based on measurable thresholds.

2. Theoretical Foundation

2.1 Measuring Non-Deterministic Systems

Assistant behavior varies across runs, providers, and tool latencies. Evaluation therefore needs distributions, confidence intervals, and scenario coverage instead of single-run pass/fail thinking. Good metrics combine quality, reliability, cost, latency, and safety.

2.2 Regression and Risk Management

When models or prompts change, behavior shifts. Regression testing detects harmful drift before users do. Red-team suites specifically test known failure classes and adversarial prompts.

3. Project Specification

3.1 What You Will Build

An evaluation platform with:

  • metric registry
  • benchmark runner
  • synthetic task generator
  • adversarial suite runner
  • regression comparator
  • release gate CLI

3.2 Functional Requirements

  1. Evaluate tasks on success, latency, cost, and reliability.
  2. Detect hallucination/factuality failures with citation checks.
  3. Compare candidate build against baseline.
  4. Produce trend reports and failure clusters.
  5. Return clear gate decision (pass/block).

3.3 Non-Functional Requirements

  • Reproducibility: fixed seeds and deterministic fixtures.
  • Scalability: batch evaluation support.
  • Actionability: failures include trace links.

3.4 Real World Outcome

$ evalforge run --suite production_v7 --candidate router_v3
[Suite] tasks=420 adversarial=70 replay=170 synthetic=180
[Metrics] success=0.84 p95=2.8s cost=$0.031
[Safety] injection_defense=0.91 hallucination_incidents=14
[Regression] factuality -6.2% vs baseline
[Gate] BLOCK deployment

4. Solution Architecture

4.1 High-Level Design

Task Suites -> Runner -> Metrics Aggregator -> Regression Comparator -> Gate Decision
                                  |
                                  v
                             Trace Store

4.2 Key Components

Component Responsibility Key Decisions
Runner execute scenarios deterministic fixture mode
Metrics aggregator compute KPIs weighted vs hard-gate metrics
Regression comparator baseline diff threshold policy
Gate release decision fail-fast on critical regressions

5. Implementation Guide

5.1 The Core Question You’re Answering

“How do I know this assistant release is objectively better and safer than the previous one?”

5.2 Concepts You Must Understand First

  1. Eval metric taxonomy
  2. Synthetic data generation constraints
  3. Distribution-aware comparisons
  4. Adversarial testing methods

5.3 Questions to Guide Your Design

  1. Which metrics should block release?
  2. How many runs are needed for stability?
  3. How do you isolate model drift from orchestration drift?

5.4 Thinking Exercise

Create a weighted reliability score from success, factuality, latency, cost, and safety incident rate.

5.5 The Interview Questions They’ll Ask

  1. How do you evaluate tool-using agents fairly?
  2. What is a useful hallucination metric?
  3. Why are offline evals not enough?
  4. How do you set regression thresholds?
  5. How do you keep adversarial suites realistic?

5.6 Hints in Layers

Hint 1: Build a 20-task gold benchmark first.

Hint 2: Capture trace IDs for all failures.

Hint 3: Add adversarial tasks only after baseline is stable.

Hint 4: Version every suite and threshold policy.

5.7 Books That Will Help

Topic Book Chapter
LLM evaluation “The LLM Engineering Handbook” eval chapters
Iterative measurement “AI Engineering” improvement loops
Quality process “Code Complete” testing chapters

5.8 Common Pitfalls and Debugging

Problem 1: eval-suite mismatch with production

  • Why: poor workload sampling.
  • Fix: include anonymized production replay slices.
  • Quick test: suite distribution mirrors production categories.

Problem 2: high metric variance

  • Why: uncontrolled randomness.
  • Fix: deterministic stubs and repeated run confidence intervals.
  • Quick test: repeated identical runs stay within tolerance.

5.9 Definition of Done

  • Automated benchmark suite covers quality, reliability, latency, cost, safety
  • Regression comparison against baseline is stable
  • Adversarial tests run in CI
  • Release gate decisions are policy-driven and reproducible