Continuous Integration (CI/CD)

Continuous Integration (CI/CD) for AI agents applies software engineering best practices to agent development. Agent CI brings automated CI/CD pipelines to agentic applications, treating agents as software rather than machine learning models.

What is CI/CD for AI Agents?

CI/CD for AI agents automates the testing, validation, and deployment of agent applications through Git-based workflows. Instead of manual testing and ad-hoc deployments, CI/CD pipelines ensure every code change is automatically evaluated before reaching production.

Traditional CI/CD focuses on unit tests, integration tests, and build processes. Agent CI/CD extends this with specialized evaluation types that validate agent behavior, performance, safety, and consistency.

How Agent CI/CD Works

GitHub Integration and Pull Request Automation

Agent CI integrates directly with GitHub through a GitHub App installation that monitors your repository for pull requests. When you open or update a PR, Agent CI automatically:

  1. Detects the pull request through GitHub webhooks
  2. Runs configured evals from your .agentci/evals/ directory
  3. Posts results as PR comments showing pass/fail status for each eval
  4. Blocks merging (optional) if critical evals fail, similar to required status checks

This automated CI/CD workflow ensures no prompt changes reach production without validation.

Git-Based Environment Strategy

Your Git branch structure defines your CI/CD deployment pipeline:

  • main branch → Production environment
  • staging branch → Staging environment
  • Feature branches → Development environments

No configuration files or environment variables needed. Your existing Git workflow becomes your agent CI/CD strategy.

Branch-Based Deployments

Every branch can run as a live agent environment, enabling:

  • Isolated testing of changes before merging
  • Stakeholder feedback on branch-specific agent instances
  • Cross-environment comparison using Git commit relationships
  • Progressive deployment through development → staging → production

CI/CD Pipeline Components

Automated Evaluation Runs (CI Runs)

CI runs execute automatically on pull requests, providing immediate feedback within your development workflow:

  • Trigger on PR events rather than every commit
  • Run eval suites configured in .agentci/evals/ directory
  • Detect regressions by comparing against baseline performance
  • Post PR comments with detailed pass/fail results
  • Support branch protection through GitHub status checks

Runtime Monitoring (Production CI/CD)

Beyond CI runs, Agent CI monitors production deployments through runtime evaluation:

  • Capture live interactions via OpenTelemetry instrumentation
  • Apply same evals to production data as development
  • Percentage-based sampling to control evaluation costs
  • Post-deployment validation of agent behavior

This continuous deployment monitoring ensures production agents maintain quality baselines over time.

Developer CLI for Local CI/CD

The Agent CI CLI enables local CI/CD workflows during development, allowing you to run evals locally before opening a pull request.

Local CI/CD testing provides:

  • Immediate feedback on agent changes
  • Pre-commit validation to catch issues early
  • Pytest-style output with pass/fail summary
  • Dashboard deeplinks for detailed analysis

Version Control as Source of Truth

Agent CI's CI/CD approach treats Git as the single source of truth for agent configuration:

Git-Native Prompt Versioning

Prompt changes are tracked through normal Git commits:

  • No separate management interface for prompt versioning
  • Standard Git diff shows prompt changes and performance impact
  • Git commit hashes serve as evaluation identifiers
  • Rollback with Git using standard commands

Evaluation Configuration in Git

All eval configurations live in your repository:

  • TOML files in .agentci/evals/ directory define test cases
  • Version controlled alongside application code
  • Branch-specific eval configurations for different environments
  • Pull request reviews include eval configuration changes

CI/CD Best Practices for Agents

1. Run Evals on Every PR

Configure GitHub branch protection to require Agent CI status checks before merging. This CI/CD gate prevents regressions from reaching production.

2. Use Feature Branches for Experimentation

Create feature branches to test prompt changes in isolated environments. The CI/CD pipeline runs evals automatically, providing confidence before merging.

3. Progressive Deployment Strategy

Deploy through environments sequentially:

  1. Development branch - Rapid iteration with full eval suite
  2. Staging branch - Pre-production validation with production-like data
  3. Main branch - Production deployment with runtime monitoring

4. Monitor Production with Runtime Evals

Enable runtime evaluation sampling to continuously validate production agent behavior. This CI/CD feedback loop catches issues that only appear with real user interactions.

CI/CD vs Traditional ML Workflows

Agent CI's CI/CD approach differs fundamentally from machine learning workflows:

Traditional ML Agent CI/CD
Model registries Git version control
Training pipelines Pull request workflows
Experiment tracking Git commit history
A/B testing infrastructure Branch-based deployments
Notebook deployments Standard software deployment

By treating agents as software, Agent CI/CD leverages mature software engineering practices rather than requiring specialized ML infrastructure.

Continuous Integration for Multi-Agent Systems

Complex agents with multiple prompts benefit from comprehensive CI/CD:

  • Router prompt changes trigger eval runs for routing logic
  • Tool execution prompts validate parameter generation
  • Planning prompts ensure correct task decomposition
  • Response prompts verify output formatting

The CI/CD pipeline evaluates the complete agent behavior, not isolated prompt performance.

CI/CD Deployment Patterns

Pull Request Workflow

Standard CI/CD workflow for agent development:

  1. Create feature branch for prompt changes
  2. Make changes to prompts or agent logic
  3. Open pull request to trigger CI evals
  4. Review eval results in PR comments
  5. Merge when evals pass to deploy changes

Continuous Deployment

Agent CI automatically monitors your repository through the GitHub App integration. When pull requests are merged to your configured production branch, runtime evals validate deployment success and monitor production behavior.

Environment Promotion

Promote code through environments using Git merges:

# Promote staging to production
git checkout main
git merge staging
git push origin main

Agent CI's CI/CD tracks performance across environments using Git commit relationships.

Common CI/CD Questions

What is CI/CD for AI agents? CI/CD for AI agents automates testing, validation, and deployment of agent applications using Git-based workflows and specialized evals.

How does Agent CI/CD differ from traditional CI/CD? Agent CI/CD includes AI-specific evaluation types (accuracy evals, LLM evals, safety evals) alongside traditional software testing practices.

Do I need separate CI/CD infrastructure? No - Agent CI integrates with your existing Git workflow and GitHub repositories without requiring separate infrastructure.

Can I use Agent CI/CD with my existing pipeline? Yes - Agent CI works through GitHub App integration and monitors your repository automatically, complementing any existing development workflows.

Getting Started with Agent CI/CD

  1. Install the GitHub App from agent-ci.com
  2. Create .agentci/evals/ directory in your repository
  3. Add TOML eval configurations defining test cases
  4. Open a pull request to trigger automated CI/CD evaluation
  5. Review results and merge when evals pass

Next Steps