Mauricio Acosta
Test-Driven Workflow Development (TDWD): Orchestrating AI Agents for Business Outcomes
Remember when Test-Driven Development (TDD) felt revolutionary? Write the test first, watch it fail, implement just enough to make it pass, refactor, repeat. It was elegant in its simplicity and transformative in its results.
Fast-forward to today's AI-assisted development era, and we've witnessed the emergence of Test-Driven Generation (TDG). Chanwit Kaewkasi's excellent Medium article captures this beautifully—using tests to guide AI code generation, ensuring the generated code meets our exact specifications.
But here's what I've discovered: there's an abstraction level even above TDG that's emerging in our AI-first development world. I'm calling it Test-Driven Workflow Development (TDWD), and after experimenting with GitHub's new Copilot agents and building custom workflows, I believe this represents the next fundamental shift in how we approach software development.
The Evolution: From Code to Outcomes
Let's trace the evolution:
- Traditional Development: Write code → Hope it works → Debug
- Test-Driven Development: Write test → Write code → Refactor
- Test-Driven Generation: Write test → AI generates code → Validate
- Test-Driven Workflow Development: Define success criteria → Orchestrate AI workflows → Validate outcomes → Iterate
The key insight? TDWD shifts focus from steering AI toward specific code implementations to orchestrating AI agents toward measurable business outcomes.
What is Test-Driven Workflow Development?
TDWD is a systematic approach to building and validating AI-driven workflows that prioritize business effectiveness over code correctness. The methodology follows a simple but powerful cycle:
Define Success → Build Workflow → Test Against Criteria → Iterate
But here's the crucial difference: unlike TDD where we test code behavior, or TDG where we test generated code quality, TDWD tests whether our AI-orchestrated workflow achieves the desired business outcome.
The TDWD Cycle in Practice
1. Define Success Criteria
Before writing a single line of code or configuring any AI agent, you define what "done" looks like:
# Example: Content Generation Workflow
success_criteria:
- Blog post published with proper SEO metadata
- Social media posts generated for 3 platforms
- Email newsletter content created
- Analytics tracking confirmed
- Brand voice consistency score > 85%
2. Build the Workflow
Create the orchestration logic that coordinates multiple AI agents or tools:
# GitHub Actions example
name: Content Creation Workflow
on:
workflow_dispatch:
inputs:
topic:
description: 'Blog post topic'
required: true
jobs:
content-generation:
runs-on: ubuntu-latest
steps:
- name: Generate blog post
uses: ./.github/actions/ai-content-generator
with:
topic: ${{ inputs.topic }}
- name: Validate content quality
uses: ./.github/actions/content-validator
- name: Generate social media content
uses: ./.github/actions/social-media-generator
- name: Test success criteria
run: |
# Run validation scripts
npm run validate-content-workflow
3. Test Against Criteria
Validate that the workflow achieves all success criteria, not just technical correctness:
// Workflow validation tests
describe('Content Creation Workflow', () => {
it('should generate SEO-optimized blog post', async () => {
const result = await validateBlogPost(generatedContent);
expect(result.seoScore).toBeGreaterThan(85);
expect(result.hasMetaDescription).toBe(true);
});
it('should maintain brand voice consistency', async () => {
const brandScore = await analyzeBrandVoice(generatedContent);
expect(brandScore).toBeGreaterThan(85);
});
});
4. Iterate
If criteria aren't met, refine the workflow—not necessarily the code. This might mean adjusting AI prompts, changing the orchestration logic, or redefining success criteria.
The Tony Stark Effect: AI Orchestration
This approach gives you that coveted "Tony Stark" experience. Instead of micromanaging AI to write specific functions, you're sending out JARVIS-like agents with clear missions:
"Hey JARVIS, I need a complete content marketing campaign for our new product launch. Make sure it hits all our brand guidelines and generates at least 50% engagement rate."
The AI agents figure out the how—you focus on the what and why.
The Scientific Method Connection
TDWD naturally aligns with the scientific method:
- Ask a question (Define the business problem)
- Gather information (Research existing solutions)
- Make a hypothesis (Design the workflow)
- Experiment (Run the AI-orchestrated workflow)
- Analyze results (Validate against success criteria)
- Modify hypothesis (Iterate the workflow)
- Present conclusion (Deploy the validated workflow)
- Retest (Continuous monitoring and improvement)
This isn't just development—it's systematic problem-solving with AI as your research assistant, implementation team, and quality assurance department.
Real-World Implementation Across Platforms
The beauty of TDWD is its platform agnostic nature. Here's how it looks across different environments:
GitHub Actions
# .github/workflows/feature-development.yml
name: TDWD Feature Development
on:
issues:
types: [labeled]
jobs:
analyze-and-implement:
if: contains(github.event.label.name, 'tdwd-feature')
steps:
- name: Analyze requirements
uses: ./.github/actions/requirement-analyzer
- name: Generate implementation plan
uses: ./.github/actions/plan-generator
- name: Implement feature
uses: ./.github/actions/ai-implementer
- name: Validate business criteria
run: npm run validate-feature-success
LangChain/LangDock Workflows
from langchain.workflows import TDWDWorkflow
workflow = TDWDWorkflow(
success_criteria={
"code_coverage": 90,
"performance_benchmark": "< 100ms",
"user_acceptance": "automated"
}
)
result = workflow.execute(
task="Implement user authentication system",
agents=["security_specialist", "ux_designer", "backend_developer"]
)
Claude Code Integration
// TDWD prompt pattern for Claude Code
const tdwdPrompt = `
TDWD Task: ${taskDescription}
Success Criteria:
${successCriteria.map(criteria => `- ${criteria}`).join('\n')}
Please orchestrate the necessary steps to achieve these outcomes.
Focus on the destination, not the path.
Validate each criterion before marking complete.
`;
Implementation Patterns: The Iteration Challenge
Now here's where I need to be completely honest with you: the examples I just showed are simplified one-shot workflows. They demonstrate the orchestration thinking, but they're missing the critical piece that makes TDWD actually work—the iteration loop.
Real TDWD requires workflows that continue iterating until success criteria are met. This is where the rubber meets the road, and where different platforms handle things very differently. Let me show you some actual implementation patterns that demonstrate true iterative workflows.
The Problem with Linear Workflows
Those GitHub Actions and LangChain examples? They run once and stop. But TDWD's power comes from the cycle: if your AI-generated content doesn't meet the brand voice requirement, or your generated code fails performance benchmarks, the workflow should automatically iterate with that feedback until it succeeds.
Here's what real iterative TDWD looks like:
Pattern 1: GitHub Actions with Internal Loops
# Real iterative TDWD workflow
name: TDWD Feature Implementation
on:
workflow_dispatch:
inputs:
feature_description:
required: true
jobs:
iterative-development:
runs-on: ubuntu-latest
steps:
- name: TDWD Implementation Loop
run: |
max_iterations=5
iteration=1
while [ $iteration -le $max_iterations ]; do
echo "🤖 TDWD Iteration $iteration"
# AI generates/modifies implementation
./scripts/ai-implement-feature.sh \
"${{ github.event.inputs.feature_description }}" \
./previous-attempts.json
# Run comprehensive validation
if ./scripts/validate-success-criteria.sh; then
echo "✅ All success criteria met!"
./scripts/create-pr.sh
break
fi
# Store failed attempt for next iteration learning
./scripts/store-attempt-feedback.sh $iteration
echo "❌ Criteria not met, iterating..."
iteration=$((iteration+1))
done
if [ $iteration -gt $max_iterations ]; then
echo "🚨 Max iterations reached, escalating to human review"
./scripts/create-review-issue.sh
fi
Pattern 2: Python/LangChain with State Persistence
class TDWDIterativeWorkflow:
def __init__(self, success_criteria, max_iterations=5):
self.success_criteria = success_criteria
self.max_iterations = max_iterations
self.iteration_history = []
self.ai_agents = self._initialize_agents()
def execute_until_success(self, task_description):
"""Execute TDWD loop until all criteria are met"""
for iteration in range(1, self.max_iterations + 1):
print(f"🤖 TDWD Iteration {iteration}")
# Generate solution with context from previous attempts
solution = self.ai_agents.orchestrate_solution(
task=task_description,
success_criteria=self.success_criteria,
previous_attempts=self.iteration_history,
iteration=iteration
)
# Validate against all success criteria
validation_result = self._validate_solution(solution)
if validation_result.all_criteria_met:
print("✅ Success! All criteria satisfied.")
return solution
# Store this attempt for learning in next iteration
self.iteration_history.append({
'iteration': iteration,
'solution': solution,
'validation_failures': validation_result.failures,
'improvement_suggestions': validation_result.suggestions
})
print(f"❌ {len(validation_result.failures)} criteria failed, iterating...")
# Max iterations reached - escalate or fail gracefully
self._handle_max_iterations_reached(task_description)
def _validate_solution(self, solution):
"""Run all success criteria validations"""
failures = []
suggestions = []
for criterion in self.success_criteria:
result = criterion.validate(solution)
if not result.passed:
failures.append(result.failure_reason)
suggestions.extend(result.improvement_suggestions)
return ValidationResult(
all_criteria_met=len(failures) == 0,
failures=failures,
suggestions=suggestions
)
Pattern 3: GitHub Actions with Cross-Run State Persistence
Sometimes you need workflows that can persist state between separate workflow runs—especially useful for long-running AI tasks:
# Workflow that persists state across multiple runs
name: TDWD Persistent Iteration
on:
workflow_dispatch:
inputs:
iteration:
description: 'Current iteration number'
default: '1'
task_id:
description: 'Unique task identifier'
required: true
jobs:
tdwd-iteration:
runs-on: ubuntu-latest
steps:
- name: Load Previous Iteration State
if: github.event.inputs.iteration != '1'
run: |
# Download state from previous iteration
gh run download $(cat .github/tdwd-state/${{ github.event.inputs.task_id }}/latest_run) \
--name "tdwd-state-${{ github.event.inputs.task_id }}"
tar -xzf tdwd-state.tar.gz
- name: Execute AI Workflow Iteration
run: |
./scripts/ai-workflow-step.sh \
--task-id "${{ github.event.inputs.task_id }}" \
--iteration "${{ github.event.inputs.iteration }}" \
--state-file "./state.json"
- name: Validate Success Criteria
id: validate
run: |
success=$(./scripts/validate-criteria.sh)
echo "success=$success" >> $GITHUB_OUTPUT
- name: Handle Success or Iterate
run: |
if [ "${{ steps.validate.outputs.success }}" = "true" ]; then
echo "✅ TDWD Complete - Creating final PR"
./scripts/create-success-pr.sh
else
echo "❌ Criteria not met - Scheduling next iteration"
# Save current state
tar -czf tdwd-state.tar.gz ./state.json ./logs/ ./artifacts/
# Trigger next iteration
next_iteration=$(( ${{ github.event.inputs.iteration }} + 1 ))
if [ $next_iteration -le 5 ]; then
gh workflow run tdwd-persistent.yml \
-f iteration=$next_iteration \
-f task_id="${{ github.event.inputs.task_id }}"
else
echo "🚨 Max iterations reached - Creating review issue"
./scripts/escalate-to-human.sh
fi
fi
- name: Save State Artifacts
uses: actions/upload-artifact@v4
if: always()
with:
name: "tdwd-state-${{ github.event.inputs.task_id }}"
path: tdwd-state.tar.gz
Pattern 4: Event-Driven Iteration with Webhooks
For platforms that support webhooks or API triggers, you can create event-driven iteration:
// Express.js webhook handler for external TDWD iteration
app.post('/tdwd-iterate', async (req, res) => {
const { taskId, iteration, validationResults } = req.body;
if (validationResults.allCriteriaMet) {
// Success! Deploy or finalize
await deployWorkflowResult(taskId);
return res.json({ status: 'completed' });
}
if (iteration >= MAX_ITERATIONS) {
// Escalate to human review
await createHumanReviewTask(taskId, validationResults);
return res.json({ status: 'escalated' });
}
// Trigger next iteration with failure context
const nextIteration = iteration + 1;
await triggerTDWDIteration(taskId, nextIteration, {
previousFailures: validationResults.failures,
improvementSuggestions: validationResults.suggestions
});
res.json({
status: 'iterating',
nextIteration: nextIteration
});
});
The State Management Challenge
The real technical challenge in TDWD isn't the orchestration—it's state management. Each iteration needs to learn from previous attempts:
- What failed and why?
- What partial successes can be built upon?
- What approaches should be avoided?
- How can the AI agents improve their next attempt?
Different platforms handle this differently:
- GitHub Actions: Artifacts, workflow outputs, external storage
- LangChain/Python: In-memory state, databases, vector stores for context
- Cloud Workflows: Managed state services, step functions
- Local Development: File system, local databases, configuration files
Iteration Strategies That Work
Graduated Feedback: Early iterations might be simple pass/fail, but later iterations can provide detailed feedback to help AI agents improve.
Context Accumulation: Each iteration adds more context about what works and what doesn't, making subsequent attempts more informed.
Parallel Exploration: Run multiple approaches simultaneously and compare results, then iterate on the most promising path.
Human Checkpoints: Include optional human review points where complex decisions can be escalated without stopping the entire workflow.
Why This Matters
Without real iteration, TDWD is just fancy workflow orchestration. The iteration loop is what makes it actually test-driven—the continuous validation and refinement until business outcomes are achieved.
The examples I showed earlier demonstrate the thinking patterns, but these iterative implementations show how TDWD actually works in practice. Choose the pattern that fits your platform and organizational needs.
Getting Started with TDWD
1. Start Small
Begin with workflows that have clear, measurable outcomes:
- Automated code review processes
- Content generation pipelines
- Bug triage and resolution workflows
2. Define Clear Success Metrics
Your criteria should be:
- Measurable: Can be programmatically validated
- Business-focused: Tied to actual outcomes, not just technical metrics
- Time-bound: Have clear completion indicators
3. Build Incrementally
Start with simple workflows and add complexity as you learn what works in your environment.
4. Iterate Relentlessly
The power of TDWD comes from iteration. Each cycle teaches you more about effective AI orchestration.
The Future of Development
As LLM models continue improving, the value isn't in writing better code—it's in building better orchestration. TDWD represents a fundamental shift from being AI prompt engineers to becoming AI workflow architects.
We're moving toward a world where:
- Developers focus on outcomes, not implementation details
- AI agents handle the complexity of execution
- Success is measured by business impact, not code quality
- Workflows become the new unit of development
Why This Matters Now
With GitHub's new coding agents, Claude Code's advanced capabilities, and the proliferation of AI development tools, we're at an inflection point. The teams and developers who master TDWD will have a significant competitive advantage.
They'll be the ones who can:
- Scale development efforts without scaling teams
- Maintain quality while moving faster
- Focus human creativity on problems that matter
- Build systems that continuously improve themselves
Practical Next Steps
- Identify a repetitive workflow in your development process
- Define clear success criteria for that workflow
- Build a simple TDWD implementation using your preferred platform
- Measure and iterate based on results
- Scale to more complex workflows as you gain confidence
The Path Forward
Test-Driven Workflow Development isn't just another methodology—it's a recognition that in an AI-first world, our role as developers is evolving. We're becoming orchestrators, architects, and outcome validators rather than just code writers.
The question isn't whether AI will change how we build software—it's how quickly we'll adapt to orchestrating these powerful new capabilities toward meaningful business outcomes.
Start experimenting with TDWD today. Begin small, think big, and focus on the destination rather than the path. Your future self (and your stakeholders) will thank you.
Happy orchestrating! 🤖⚡