The Problem
Modern deployment pipelines involve multiple steps — linting, testing, building, staging, deploying — each with different failure modes. When something breaks, debugging the pipeline is often harder than debugging the code itself.
The Solution
Use Flezi AgentBox's Agent Pipeline system to create an intelligent CI/CD workflow. Chain multiple agents together in a DAG (Directed Acyclic Graph), where each agent handles one step and passes context to the next.
Pipeline Architecture
Lint Agent — Runs ESLint, Prettier, and type-checking on the codebase. Fails fast on syntax errors.
Test Agent — Executes unit tests and integration tests in the Docker sandbox. Collects coverage reports.
Build Agent — Compiles the application, optimizes assets, and creates deployment artifacts.
Deploy Agent — Pushes to staging first, runs smoke tests, then promotes to production with canary deployment.
Building the Pipeline
In the Builder Lab, use the visual DAG editor to connect agents:
[Lint] → [Test] → [Build] → [Deploy:Staging] → [Smoke Test] → [Deploy:Production]
↓ (on failure)
[Rollback Agent]
Each node in the pipeline is a standalone agent with its own configuration, LLM provider, and error handling logic.
Inter-Agent Communication
Agents pass data using Flezi AgentBox's transformation rules:
{
"from": "test-agent",
"to": "build-agent",
"transform": {
"coverage_report": "$.results.coverage",
"test_count": "$.results.total_tests",
"should_build": "$.results.all_passed"
}
}Error Handling
The pipeline supports three error strategies:
- Fail Fast — Stop the entire pipeline on first failure
- Continue on Warning — Skip non-critical failures and continue
- Auto-Rollback — Trigger the rollback agent when deployment fails
Always configure a rollback agent for production deployments. The canary deployment pattern catches issues before they affect all users.
Monitoring
Track pipeline runs in the analytics dashboard:
- Execution time per step and total
- Success rate over time
- Common failure points with root cause analysis
- Cost attribution per pipeline run