What is ADLC?
ADLC is a comprehensive lifecycle management framework that guides you through four sequential phases, with continuous monitoring and governance applied throughout:Prepare
APIs and Data preparation
Build
Agent development and implementation
Evaluate
Testing and performance assessment
Run
Deployment and operations
Continuous Monitoring and Governance
AI guardrails, observability, security scanning, and compliance checks applied throughout all phases
ADLC Phases Overview
| Phase | Key Activities | Tools & Technologies |
|---|---|---|
| Prepare | Data ingestion, ML feature preparation, API unification | Data pipelines, MCP servers, API management |
| Build | Framework selection, agent implementation, tool integration, RAG setup | Agno, LangChain, LangGraph, MCP, Vector DBs |
| Evaluate | Unit tests, LLM judges, human annotations, tracing analysis | Langfuse, LangWatch, Promptfoo, Red teaming |
| Run | CI/CD deployment, API exposure, monitoring, scaling | Argo CD, APIM, Kubernetes, Observability stack |
| Continuous Monitoring and Governance | Guardrails, security scans, compliance checks, performance monitoring | AI Gateway, Nemo Guardrails, Observability tools, Security tooling |
Benefits
Structured Approach
Clear phases reduce complexity and ensure nothing is missed.
Quality Assurance
Evaluation, monitoring, and governance at every step.
Production Ready
End-to-end tooling ensures agents are ready for production.
Getting Started
1
Understand the Phases
Review the ADLC Phases documentation to understand what happens in each phase.
2
Start with Prepare
Begin by preparing your APIs and data. Ensure MCP servers are available and data pipelines are set up.
3
Use Starter Kits
Leverage Starter Kits to jumpstart the Build phase with pre-configured templates.
4
Follow CI/CD
Use the platform’s CI/CD Workflows to automate the Run phase.