Who is AI Agent Testing & Evaluation Guide for?

This guide is for builders and teams evaluating ai agent testing & evaluation guide in practical, production-focused workflows.

What should I do after reading AI Agent Testing & Evaluation Guide?

Use the checklist and linked resources to pick one next action, implement it, and measure outcomes before expanding scope.

Amir Brooks

testingevaluationagentsqualitymonitoring

AI Agent Testing & Evaluation Guide

A practical framework for testing AI agents from unit tests to production monitoring, with evaluation patterns that scale.

February 6, 20266 min read

Testing AI agents is different from testing traditional code, but not mysterious. You still need unit tests, integration tests, and regression checks - you just have to handle probabilistic outputs.

This guide covers:

Unit tests for tools
Integration tests for workflows
Evaluation frameworks
Benchmarking approaches
A/B testing behavior
Regression testing
Production monitoring

1) Unit Testing Agent Tools

Start with the deterministic parts. Every agent tool (search, fetch, file writes) should have a standard test suite.

describe("tool:parseInvoice", () => {
  it("extracts totals", async () => {
    const result = await parseInvoice(sampleDoc);
    expect(result.total).toBe(129.90);
  });
});

These tests catch 80% of issues before the LLM is involved.

Testing Tool Error Handling

Don't just test the happy path. Agents need to recover gracefully when tools fail:

describe("tool:parseInvoice - error cases", () => {
  it("returns a structured error for malformed PDFs", async () => {
    const result = await parseInvoice(corruptedDoc);
    expect(result.error).toBe("PARSE_FAILED");
    expect(result.total).toBeUndefined();
  });

  it("handles timeout gracefully", async () => {
    jest.useFakeTimers();
    const promise = parseInvoice(slowDoc);
    jest.advanceTimersByTime(30_000);
    await expect(promise).rejects.toThrow("TIMEOUT");
  });
});

Schema Validation Tests

If your tools accept structured input, test the schema boundaries:

describe("tool:searchPeople - input validation", () => {
  it("rejects empty queries", async () => {
    await expect(searchPeople({ query: "" })).rejects.toThrow();
  });

  it("truncates queries over 500 chars", async () => {
    const result = await searchPeople({ query: "a".repeat(600) });
    expect(result.queryUsed.length).toBeLessThanOrEqual(500);
  });
});

2) Integration Testing Agent Workflows

Integration tests simulate a full run. Use recorded tool responses so tests are repeatable.

const mockTools = {
  search: async () => mockSearchResult,
  fetch: async () => mockPage
};

const result = await runAgentWorkflow(input, mockTools);
expect(result.status).toBe("success");

Focus on workflow correctness, not exact wording. If your agent system uses multi-agent orchestration, test each stage independently before testing the full pipeline.

Multi-Step Workflow Tests

Real agents chain multiple tools. Test the full sequence:

describe("workflow: research-and-summarize", () => {
  it("completes a 3-step research workflow", async () => {
    const mockTools = {
      search: async () => ({ results: [{ url: "https://example.com", title: "Test" }] }),
      fetch: async () => ({ content: "Article about AI agents in production..." }),
      summarize: async (text: string) => ({ summary: "AI agents need testing." }),
    };

    const result = await runAgentWorkflow(
      { goal: "Research AI agent testing" },
      mockTools
    );

    expect(result.status).toBe("success");
    expect(result.steps).toHaveLength(3);
    expect(result.output.summary).toBeDefined();
  });

  it("handles mid-workflow tool failures", async () => {
    const mockTools = {
      search: async () => ({ results: [{ url: "https://example.com" }] }),
      fetch: async () => { throw new Error("Network timeout"); },
      summarize: async () => ({ summary: "" }),
    };

    const result = await runAgentWorkflow(
      { goal: "Research AI agent testing" },
      mockTools
    );

    expect(result.status).toBe("partial");
    expect(result.failedSteps).toContain("fetch");
  });
});

3) Evaluation Frameworks

Use structured evaluations to compare changes. This is where agent testing diverges most from traditional software - you need to measure quality, not just correctness.

Common approaches:

Rubric scoring (quality, relevance, safety)
Pairwise comparisons (A vs B outputs)
Task completion rate
LLM-as-judge (use a separate model to grade outputs)

Avoid fake benchmarks. Use real samples from your domain.

Building a Rubric-Based Evaluator

Here's a practical rubric scorer you can adapt:

type RubricDimension = {
  name: string;
  weight: number;
  scorer: (output: string, expected: string) => number; // 0-1
};

const rubric: RubricDimension[] = [
  {
    name: "relevance",
    weight: 0.4,
    scorer: (output, expected) => {
      // Simple keyword overlap - replace with embedding similarity in production
      const keywords = expected.toLowerCase().split(" ");
      const matches = keywords.filter(k => output.toLowerCase().includes(k));
      return matches.length / keywords.length;
    },
  },
  {
    name: "completeness",
    weight: 0.3,
    scorer: (output) => {
      return output.length > 200 ? 1 : output.length / 200;
    },
  },
  {
    name: "safety",
    weight: 0.3,
    scorer: (output) => {
      const blocklist = ["password", "secret", "token"];
      return blocklist.some(w => output.includes(w)) ? 0 : 1;
    },
  },
];

function evaluateOutput(output: string, expected: string): number {
  return rubric.reduce((score, dim) => {
    return score + dim.weight * dim.scorer(output, expected);
  }, 0);
}

LLM-as-Judge Pattern

For nuanced evaluation, use a second model (like Claude) to grade the output:

async function llmJudge(output: string, criteria: string): Promise<number> {
  const response = await anthropic.messages.create({
    model: "claude-sonnet-4-20250514",
    messages: [{
      role: "user",
      content: `Rate this output 1-10 on: ${criteria}\n\nOutput: ${output}\n\nScore (number only):`
    }],
  });
  return parseInt(response.content[0].text, 10);
}

Data dashboard for monitoring agent evaluation metrics

4) Benchmarking Approaches

Benchmarks should answer one question: is this change better for users?

Good benchmarks:

Real user tasks
Fixed input sets
Clear pass/fail criteria

Bad benchmarks:

Overly synthetic prompts
Single "hero" examples
Metrics that don't map to user value

For ranking agent quality over time, an ELO-based rating system gives you a more nuanced signal than pass/fail benchmarks alone.

5) A/B Testing Agent Behavior

In production, use A/B tests to compare:

Prompt variants
Model versions (Claude Opus vs GPT-5 Codex)
Tool ordering

Example: 10% of traffic uses a new prompt, measure task success and cost.

A/B Test Implementation

Here's a minimal A/B test router for agent behavior:

function assignVariant(userId: string, experimentId: string): "control" | "treatment" {
  const hash = crypto.createHash("md5").update(`${userId}:${experimentId}`).digest("hex");
  return parseInt(hash.slice(0, 2), 16) < 26 ? "treatment" : "control"; // ~10%
}

async function runWithExperiment(userId: string, task: AgentTask) {
  const variant = assignVariant(userId, "prompt-v2-test");

  const prompt = variant === "treatment" ? newPromptV2 : currentPrompt;
  const result = await runAgent({ ...task, prompt });

  await logExperiment({
    userId,
    experiment: "prompt-v2-test",
    variant,
    success: result.status === "success",
    cost: result.tokenCost,
    latencyMs: result.durationMs,
  });

  return result;
}

6) Regression Testing

Every prompt or workflow change can break something. Keep a regression suite.

const regressionSet = loadRegressionCases();
for (const testCase of regressionSet) {
  const output = await runAgent(testCase.input);
  expect(score(output)).toBeGreaterThan(testCase.threshold);
}

Store regression cases in version control. Don't rely on memory.

7) Monitoring in Production

Testing doesn't stop after deployment. Monitor:

Failure rates
Latency
Cost per run
User feedback signals
Output quality scores (sampled)

If metrics drift, roll back or adjust quickly.

Production Monitoring Setup

For Convex‑backed agent apps, I store run metrics as structured events:

// convex/metrics.ts
export const logRunMetrics = mutation({
  args: {
    runId: v.string(),
    agentId: v.string(),
    status: v.string(),
    latencyMs: v.number(),
    tokenCount: v.number(),
    estimatedCost: v.number(),
    qualityScore: v.optional(v.number()),
  },
  handler: async (ctx, args) => {
    await ctx.db.insert("runMetrics", {
      ...args,
      createdAt: Date.now(),
    });
  },
});

Alerting on Drift

Set thresholds and alert when metrics deviate:

async function checkDrift(agentId: string) {
  const recent = await getRecentMetrics(agentId, { hours: 1 });
  const baseline = await getBaselineMetrics(agentId);

  if (recent.avgLatency > baseline.avgLatency * 1.5) {
    await alert(`Agent ${agentId} latency spiked 50%+`);
  }
  if (recent.failureRate > 0.1) {
    await alert(`Agent ${agentId} failure rate above 10%`);
  }
}

Practical Testing Pipeline

Unit Tests -> Integration Tests -> Eval Suite -> Regression -> Production Monitoring

Treat evaluation as a pipeline, not a one-off event.

Final Advice

Agent testing is about confidence, not perfection. Build a system where changes are safe to ship. That means deterministic tool tests, realistic evaluation sets, and continuous monitoring.

Don't neglect security testing — a broken auth check in your agent pipeline is worse than a bad prompt. If you're building your first agent, the complete builder's guide covers the full architecture. And when you're ready to take agents from prototype to production, the concept‑to‑production guide covers reliability patterns and monitoring in depth.

The AI Agent Masterclass walks through this entire testing pipeline with real examples, including evaluation patterns I use across production agent systems.

If you do this well, you can move fast and sleep at night.

Ready to ship an AI product?

We build revenue-moving AI tools in focused agentic development cycles. 3 production apps shipped in a single day.

Book a 20-min Fit Call See how agentic development works

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securityagents

AI Agent Authentication & Security: A Practical Guide

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Feb 6, 20267 min read

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testingevaluationagentsqualitymonitoring

AI Agent Testing & Evaluation Guide

A practical framework for testing AI agents from unit tests to production monitoring, with evaluation patterns that scale.

February 6, 20266 min read

This guide covers:

Unit tests for tools
Integration tests for workflows
Evaluation frameworks
Benchmarking approaches
A/B testing behavior
Regression testing
Production monitoring

1) Unit Testing Agent Tools

Start with the deterministic parts. Every agent tool (search, fetch, file writes) should have a standard test suite.

describe("tool:parseInvoice", () => {
  it("extracts totals", async () => {
    const result = await parseInvoice(sampleDoc);
    expect(result.total).toBe(129.90);
  });
});

These tests catch 80% of issues before the LLM is involved.

Testing Tool Error Handling

Don't just test the happy path. Agents need to recover gracefully when tools fail:

describe("tool:parseInvoice - error cases", () => {
  it("returns a structured error for malformed PDFs", async () => {
    const result = await parseInvoice(corruptedDoc);
    expect(result.error).toBe("PARSE_FAILED");
    expect(result.total).toBeUndefined();
  });

  it("handles timeout gracefully", async () => {
    jest.useFakeTimers();
    const promise = parseInvoice(slowDoc);
    jest.advanceTimersByTime(30_000);
    await expect(promise).rejects.toThrow("TIMEOUT");
  });
});

Schema Validation Tests

If your tools accept structured input, test the schema boundaries:

describe("tool:searchPeople - input validation", () => {
  it("rejects empty queries", async () => {
    await expect(searchPeople({ query: "" })).rejects.toThrow();
  });

  it("truncates queries over 500 chars", async () => {
    const result = await searchPeople({ query: "a".repeat(600) });
    expect(result.queryUsed.length).toBeLessThanOrEqual(500);
  });
});

2) Integration Testing Agent Workflows

Integration tests simulate a full run. Use recorded tool responses so tests are repeatable.

const mockTools = {
  search: async () => mockSearchResult,
  fetch: async () => mockPage
};

const result = await runAgentWorkflow(input, mockTools);
expect(result.status).toBe("success");

Focus on workflow correctness, not exact wording. If your agent system uses multi-agent orchestration, test each stage independently before testing the full pipeline.

Multi-Step Workflow Tests

Real agents chain multiple tools. Test the full sequence:

describe("workflow: research-and-summarize", () => {
  it("completes a 3-step research workflow", async () => {
    const mockTools = {
      search: async () => ({ results: [{ url: "https://example.com", title: "Test" }] }),
      fetch: async () => ({ content: "Article about AI agents in production..." }),
      summarize: async (text: string) => ({ summary: "AI agents need testing." }),
    };

    const result = await runAgentWorkflow(
      { goal: "Research AI agent testing" },
      mockTools
    );

    expect(result.status).toBe("success");
    expect(result.steps).toHaveLength(3);
    expect(result.output.summary).toBeDefined();
  });

  it("handles mid-workflow tool failures", async () => {
    const mockTools = {
      search: async () => ({ results: [{ url: "https://example.com" }] }),
      fetch: async () => { throw new Error("Network timeout"); },
      summarize: async () => ({ summary: "" }),
    };

    const result = await runAgentWorkflow(
      { goal: "Research AI agent testing" },
      mockTools
    );

    expect(result.status).toBe("partial");
    expect(result.failedSteps).toContain("fetch");
  });
});

3) Evaluation Frameworks

Use structured evaluations to compare changes. This is where agent testing diverges most from traditional software - you need to measure quality, not just correctness.

Common approaches:

Rubric scoring (quality, relevance, safety)
Pairwise comparisons (A vs B outputs)
Task completion rate
LLM-as-judge (use a separate model to grade outputs)

Avoid fake benchmarks. Use real samples from your domain.

Building a Rubric-Based Evaluator

Here's a practical rubric scorer you can adapt:

type RubricDimension = {
  name: string;
  weight: number;
  scorer: (output: string, expected: string) => number; // 0-1
};

const rubric: RubricDimension[] = [
  {
    name: "relevance",
    weight: 0.4,
    scorer: (output, expected) => {
      // Simple keyword overlap - replace with embedding similarity in production
      const keywords = expected.toLowerCase().split(" ");
      const matches = keywords.filter(k => output.toLowerCase().includes(k));
      return matches.length / keywords.length;
    },
  },
  {
    name: "completeness",
    weight: 0.3,
    scorer: (output) => {
      return output.length > 200 ? 1 : output.length / 200;
    },
  },
  {
    name: "safety",
    weight: 0.3,
    scorer: (output) => {
      const blocklist = ["password", "secret", "token"];
      return blocklist.some(w => output.includes(w)) ? 0 : 1;
    },
  },
];

function evaluateOutput(output: string, expected: string): number {
  return rubric.reduce((score, dim) => {
    return score + dim.weight * dim.scorer(output, expected);
  }, 0);
}

LLM-as-Judge Pattern

For nuanced evaluation, use a second model (like Claude) to grade the output:

async function llmJudge(output: string, criteria: string): Promise<number> {
  const response = await anthropic.messages.create({
    model: "claude-sonnet-4-20250514",
    messages: [{
      role: "user",
      content: `Rate this output 1-10 on: ${criteria}\n\nOutput: ${output}\n\nScore (number only):`
    }],
  });
  return parseInt(response.content[0].text, 10);
}

Data dashboard for monitoring agent evaluation metrics

4) Benchmarking Approaches

Benchmarks should answer one question: is this change better for users?

Good benchmarks:

Real user tasks
Fixed input sets
Clear pass/fail criteria

Bad benchmarks:

Overly synthetic prompts
Single "hero" examples
Metrics that don't map to user value

For ranking agent quality over time, an ELO-based rating system gives you a more nuanced signal than pass/fail benchmarks alone.

5) A/B Testing Agent Behavior

In production, use A/B tests to compare:

Prompt variants
Model versions (Claude Opus vs GPT-5 Codex)
Tool ordering

Example: 10% of traffic uses a new prompt, measure task success and cost.

A/B Test Implementation

Here's a minimal A/B test router for agent behavior:

function assignVariant(userId: string, experimentId: string): "control" | "treatment" {
  const hash = crypto.createHash("md5").update(`${userId}:${experimentId}`).digest("hex");
  return parseInt(hash.slice(0, 2), 16) < 26 ? "treatment" : "control"; // ~10%
}

async function runWithExperiment(userId: string, task: AgentTask) {
  const variant = assignVariant(userId, "prompt-v2-test");

  const prompt = variant === "treatment" ? newPromptV2 : currentPrompt;
  const result = await runAgent({ ...task, prompt });

  await logExperiment({
    userId,
    experiment: "prompt-v2-test",
    variant,
    success: result.status === "success",
    cost: result.tokenCost,
    latencyMs: result.durationMs,
  });

  return result;
}

6) Regression Testing

Every prompt or workflow change can break something. Keep a regression suite.

const regressionSet = loadRegressionCases();
for (const testCase of regressionSet) {
  const output = await runAgent(testCase.input);
  expect(score(output)).toBeGreaterThan(testCase.threshold);
}

Store regression cases in version control. Don't rely on memory.

7) Monitoring in Production

Testing doesn't stop after deployment. Monitor:

Failure rates
Latency
Cost per run
User feedback signals
Output quality scores (sampled)

If metrics drift, roll back or adjust quickly.

Production Monitoring Setup

For Convex‑backed agent apps, I store run metrics as structured events:

// convex/metrics.ts
export const logRunMetrics = mutation({
  args: {
    runId: v.string(),
    agentId: v.string(),
    status: v.string(),
    latencyMs: v.number(),
    tokenCount: v.number(),
    estimatedCost: v.number(),
    qualityScore: v.optional(v.number()),
  },
  handler: async (ctx, args) => {
    await ctx.db.insert("runMetrics", {
      ...args,
      createdAt: Date.now(),
    });
  },
});

Alerting on Drift

Set thresholds and alert when metrics deviate:

async function checkDrift(agentId: string) {
  const recent = await getRecentMetrics(agentId, { hours: 1 });
  const baseline = await getBaselineMetrics(agentId);

  if (recent.avgLatency > baseline.avgLatency * 1.5) {
    await alert(`Agent ${agentId} latency spiked 50%+`);
  }
  if (recent.failureRate > 0.1) {
    await alert(`Agent ${agentId} failure rate above 10%`);
  }
}

Practical Testing Pipeline

Unit Tests -> Integration Tests -> Eval Suite -> Regression -> Production Monitoring

Treat evaluation as a pipeline, not a one-off event.

Final Advice

Agent testing is about confidence, not perfection. Build a system where changes are safe to ship. That means deterministic tool tests, realistic evaluation sets, and continuous monitoring.

The AI Agent Masterclass walks through this entire testing pipeline with real examples, including evaluation patterns I use across production agent systems.

If you do this well, you can move fast and sleep at night.

Ready to ship an AI product?

We build revenue-moving AI tools in focused agentic development cycles. 3 production apps shipped in a single day.

Book a 20-min Fit Call See how agentic development works

Related Guides

securityagents

AI Agent Authentication & Security: A Practical Guide

A pragmatic security playbook for agent-to-agent and agent-to-API communication, including verification flows, rate limiting, and token rotation patterns.

Feb 6, 20267 min read

agentsproduction

Autonomous AI Agents: From Concept to Production

A practical guide to taking AI agents from prototype to production, with reliability, cost control, and monitoring patterns learned from 24/7 operations.

Feb 6, 20266 min read

mcpai tools

MCP Explained: The Model Context Protocol for AI Builders

A builder-friendly guide to MCP (Model Context Protocol): what it is, why it matters, and how to build servers and integrations.

Feb 6, 20264 min read

1) Unit Testing Agent Tools

Testing Tool Error Handling

Schema Validation Tests

2) Integration Testing Agent Workflows

Multi-Step Workflow Tests

3) Evaluation Frameworks

Building a Rubric-Based Evaluator

LLM-as-Judge Pattern

4) Benchmarking Approaches

Good benchmarks:

Bad benchmarks:

5) A/B Testing Agent Behavior

A/B Test Implementation

6) Regression Testing

7) Monitoring in Production

Production Monitoring Setup

Alerting on Drift

Practical Testing Pipeline

Final Advice

Related reading

The Builder's Guide to AI Agents

AI Agent Fundamentals Course

AI Audit Template

Enjoyed this guide?

Ready to ship an AI product?

Related Guides

AI Agent Authentication & Security: A Practical Guide

Autonomous AI Agents: From Concept to Production

MCP Explained: The Model Context Protocol for AI Builders

1) Unit Testing Agent Tools

Testing Tool Error Handling

Schema Validation Tests

2) Integration Testing Agent Workflows

Multi-Step Workflow Tests

3) Evaluation Frameworks

Building a Rubric-Based Evaluator

LLM-as-Judge Pattern

4) Benchmarking Approaches

Good benchmarks:

Bad benchmarks:

5) A/B Testing Agent Behavior

A/B Test Implementation

6) Regression Testing

7) Monitoring in Production

Production Monitoring Setup

Alerting on Drift

Practical Testing Pipeline

Final Advice

Related reading

The Builder's Guide to AI Agents

AI Agent Fundamentals Course

AI Audit Template

Enjoyed this guide?

Ready to ship an AI product?

Related Guides

AI Agent Authentication & Security: A Practical Guide

Autonomous AI Agents: From Concept to Production

MCP Explained: The Model Context Protocol for AI Builders