TDD for AI Agents

Patterns intermediate 15 min

Sources verified Dec 23

Test-Driven Development adapted for AI coding agents, using tests as anchors to prevent 'vibe coding' and ensure verifiable behavior.

Test-Driven Development (TDD) becomes even more valuable when working with AI agents. Kent Beck, creator of TDD, calls it a 'superpower' for agentic development. Tests provide the stable anchor that prevents 'vibe coding' - without them, agents iterate endlessly based on vibes rather than verifiable criteria.

The Red-Green-Generate workflow adapts TDD for AI: (1) Write a failing test that specifies the desired behavior, (2) Let the agent generate implementation, (3) Verify the test passes. This creates a clear exit condition - the agent iterates until tests pass, not until it 'feels done'.

The Tautology Trap: Never let the same agent write BOTH the test AND the implementation. If it writes both, it will write weak tests that its own buggy code can pass. Tests must be established BEFORE code generation, validated by a human, or written by a separate agent.

Traditional TDD assumes determinism - run a test, get the same result. AI agents are non-deterministic, so tests need flexibility. Use scoring rubrics (0-100) instead of binary pass/fail. Evaluate behaviors and reasoning, not just outputs. Run tests multiple times to catch flaky agent behavior.

Spec-First Prompting extends TDD to conversations with AI. Before asking for implementation, ask for the test: 'Create a Jest test that fails for [Feature X] covering edge cases A and B. Do not implement yet.' If the agent understands the test, it understands the requirement.

There are two automation loops: Inner loop (Claude Code hooks) - fast feedback during generation. Configure PostToolUse hooks to auto-run type checking or linting when files change. The agent 'feels' compiler errors immediately and self-corrects. Outer loop (Git hooks) - quality gate before commit. Pre-commit hooks block commits if tests fail, catching issues missed by the agent.

TDD for agents shifts review earlier. Instead of reviewing generated code (low leverage), review the test specification (high leverage). If the tests are right, the implementation will be too. This aligns with the 'shift left' principle - catch problems early when they're cheap to fix.

Key Takeaways

• TDD is a 'superpower' for AI agents - tests anchor behavior instead of relying on vibes
• Red-Green-Generate: write failing test first, let agent implement, verify test passes
• The Tautology Trap: never let the same agent write both test and implementation
• Use scoring rubrics (0-100) instead of binary pass/fail for non-deterministic systems
• Spec-First Prompting: ask agent to write tests before implementation to validate understanding
• Inner loop (Claude Code hooks): fast feedback during generation via auto-linting
• Outer loop (Git hooks): quality gate that blocks commits with failing tests
• Review test specifications (high leverage) instead of generated code (low leverage)

In This Platform

This platform uses TDD principles at build time: Zod schemas define expected structure (the 'test'), build.js validates all content against schemas (the 'verification'), and the build fails if validation fails. Every concept, exercise, and source must pass schema validation before deployment.

Relevant Files:

• build.js
• frontend/src/schemas/survey.ts
• test/unit/schemas.test.ts

Related Concepts

GuardrailsAgentic LoopsStructured Outputs & JSON SchemaAI System Monitoring

Prerequisites

Agentic Workflows

Sources

✓ Pragmatic Engineer: Kent Beck, creator of TDD, calls it a 'superpower' for AI agents - tests anchor behavior instead of vibes ⓘ

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TDD, AI Agents and Coding with Kent Beck

Pragmatic Engineer

Kent Beck, creator of TDD, explains how test-driven development becomes even more valuable with AI agents. Tests provide the stable anchor that prevents 'vibe coding' - agents iterate until tests pass, creating a verifiable exit condition. The Red-Green-Generate workflow adapts TDD for AI: write failing test, let agent generate implementation, verify behavior. Key insight: TDD shifts from 'slow overhead' to 'massive accelerator' because AI handles the tedious test-writing boilerplate.

Key Findings:

• TDD is a 'superpower' when working with AI agents
• Tests anchor agents to verifiable behavior, not vibes
• Red-Green-Generate: write failing test, let agent generate code, verify

Credibility: high

View Source ↗

✓ Latent Space / Vellum: The Tautology Trap: agents write weak tests to pass their own buggy code; separation of concerns is critical ⓘ

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AI Agents, Meet Test Driven Development

Latent Space / Vellum

Practical deep-dive on applying TDD to AI agents from Vellum's production experience. Key insight: traditional TDD assumes determinism, but agents require flexible evaluation - scores and ratings instead of binary pass/fail. The 'Tautology Trap' warns against letting the same agent write both test and implementation. Separation of concerns is critical: tests must anchor behavior before generation begins.

Key Findings:

• Deploying AI agents is tough - bugs, hallucinations, unexpected behavior derail workflows
• TDD provides verifiable exit criteria instead of relying on agent's whims
• Testing agents requires flexibility - evaluate behaviors and reasoning, not just outputs

Credibility: high

View Source ↗

✓ Anthropic: Writer/Reviewer and TDD splits - one agent writes tests, another implements - improve output quality ⓘ

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Multi-Agent Collaboration Best Practices

Anthropic

Official guidance on multi-agent patterns including debate (multiple models reviewing each other), TDD splits (one writes tests, another implements), and Architect/Implementer separation. Research shows diverse model debate (Claude + Gemini + GPT) achieves 91% on GSM-8K vs 82% for identical models.

Key Findings:

• Separate Claude instances can communicate via shared scratchpads
• Multi-agent debate with diverse models outperforms single-model approaches
• Writer/Reviewer and TDD splits improve output quality

Credibility: high

View Source ↗

✓ HumanLayer: Review plans (or tests) for maximum leverage, not code - 'shift left' principle ⓘ

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Advanced Context Engineering for AI Agents

HumanLayer

Introduces the Research → Plan → Implement workflow for AI-assisted development. Key insight: reviewing plans is higher leverage than reviewing code. The workflow explicitly clears context between phases to maintain focus and quality.

Key Findings:

• Research → Plan → Implement workflow produces better results than direct coding
• Review the plan before implementation for maximum leverage
• Keep context utilization at 40-60% - clear between phases

Credibility: high

View Source ↗