# Application Life Cycle Management, Artificial Intelligence, Generative AI, Software Development

Published articles for Application Life Cycle Management, Artificial Intelligence, Generative AI, Software Development.

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## Managing the life cycle of AI agents at scale

DevFeed: [Managing the life cycle of AI agents at scale](<https://devfeed.tech/articles/managing-the-life-cycle-of-ai-agents-at-scale-59214.md>)

Original publisher: [Read original article](<https://www.infoworld.com/article/4225702/managing-the-life-cycle-of-ai-agents-at-scale.html>)

Author: Malith Jayasinghe

Published: 2026-09-24T09:00:00Z

Content type: article

Language: en

Sources: [InfoWorld](<https://devfeed.tech/sources/infoworld.md>)

Topics: [Agentic AI](<https://devfeed.tech/topics/what-is-agentic-ai.md>), [Loop Engineering](<https://devfeed.tech/topics/loop-engineering.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [agent observability](<https://devfeed.tech/topics/agent-observability.md>), [Requirements](<https://devfeed.tech/topics/requirements.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [agent-observability](<https://devfeed.tech/tags/agent-observability.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [application-life-cycle-management](<https://devfeed.tech/tags/application-life-cycle-management.md>), [application-life-cycle-management-artificial-intelligence-generative-ai-software-development](<https://devfeed.tech/tags/application-life-cycle-management-artificial-intelligence-generative-ai-software-development.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [observability](<https://devfeed.tech/tags/observability.md>), [requirements](<https://devfeed.tech/tags/requirements.md>), [software-development](<https://devfeed.tech/tags/software-development.md>)

### AI overview

This article explains why conventional software delivery practices do not fully fit non-deterministic, context-dependent AI agents. It introduces an agent development life cycle that adds continuous evaluation, observability, identity, tool access, and governance from design through production.

### Source excerpt

There's a new reality emerging for development teams: existing software delivery practices don't translate cleanly to agentic AI systems. Practices built around deterministic execution paths and well-defined application behaviors are no longer sufficient when the software itself can make decisions about how to accomplish a task. The main difference is that agents exhibit non-deterministic, context-dependent behavior. In contrast to traditional applications, where behavior is determined by code and configuration, agents can make dynamic decisions about how to tackle a task, which tools to use, and what actions to carry out. Their behavior is influenced by models, prompts, tools, data, memory, and the runtime context. These new requirements now apply to how we design, evaluate, observe, govern, and run software, since conventional software development life cycles were not intended to meet them. This situation creates the need for an agent development life cycle (ADLC). ADLC extends existing software development practices by incorporating agent-specific considerations such as continuous evaluation, agent observability, agent identity, tool access, and governance at every stage of the process, from agent definition and design through development, deployment, and production operation. Let's look at some key aspects of ADLC and how they address the unique requirements of building and operating agents. Start by defining the agent Before you start writing any code, you must clearly define the problem the agent is supposed to solve, the scope and boundaries of that problem, and the criteria for judging success. For example, a hotel booking agent could help users find appropriate hotels, compare options, and make, modify, or cancel reservations. Its scope should also clearly define what the agent is not responsible for. From this scope, establish measurable success criteria. For instance, the agent should successfully handle 95% of valid booking requests without human interve