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In this article, you learn about the agent development lifecycle and how it differs from traditional software development approaches. The agent development lifecycle includes five phases: discovery, experimentation, build, deploy, and operational steady state. Understanding these phases helps you design and implement effective AI agent solutions.
Agent development requires a specialized approach due to the dynamic nature of AI models and data dependencies. Unlike traditional software development, agent development emphasizes iterative processes, continuous feedback, and early risk mitigation through validation.
| Step | Phase | Description |
|---|---|---|
| 1 | Discovery | Identify requirements, stakeholders, needs, and project scope |
| 2 | Experimentation | Test hypotheses, explore technologies, and evaluate hero responses |
| 3 | Build | Develop the full solution with proper architecture |
| 4 | Deploy | Release to production environment and go live |
| 5 | Operational steady state | Maintain, monitor, and continuously improve the system |
The following principles underpin these phases:
- Iterative: Phases might overlap and iterate
- Feedback-driven: Each phase informs the next
- Risk mitigation: Early validation reduces risk
Discovery and experimentation phases
The discovery phase focuses on understanding business requirements and identifying appropriate use cases for agent implementation. This phase requires careful consideration of whether AI implementation delivers meaningful value to justify the added complexity.
Experimentation must be grounded on real-world data sets and current models rather than synthetic or limited test data. Proof of concept ideation using synthetic data increases the risk of agents not performing as expected in production environments. Minimize the time between experimentation and build phases to reduce the risk of model or data drift affecting agent performance.
Build and deployment phases
The build phase translates experimental insights into production-ready agent implementations. Architecture decisions you make during this phase directly affect operational reliability and maintenance requirements.
Deployment involves transitioning agents from development environments to production systems while maintaining quality and performance characteristics established during experimentation.
Operational steady state
The operational steady state represents the ongoing maintenance and optimization of agent performance. During this phase, you continuously monitor, evaluate, and adjust to maintain operability standards as business requirements and underlying technologies evolve.
Next step
Learn how to choose the right host platform. The host platform determines the orchestration capabilities, model access, and operational features available to your agent. These features directly affect response quality and performance.