As AI-assisted coding rises, multi-agent workflows emerge, leveraging multiple AI agents for specific tasks within the software development life cycle (SDLC). This includes planning, coding, testing, and deployment activities. Specialized agents mirror human engineering teams, each focusing on their role while the developer maintains control. The approach allows for streamlined, efficient task distribution and collaboration, improving the overall software development process. Additionally, multi-agent workflows extend beyond coding to every aspect of the SDLC, ensuring comprehensive management and continuous delivery.
A multi-agent workflow refers to using various AI agents in parallel for specific software development life cycle (SDLC) tasks, whether for planning, scaffolding, writing code, testing, debugging, log analysis, or deployment.
Just like a human team has specialists, like back-end, security, and testing engineers, agentic systems will require multiple specialized agents.
Each agent excels in a specialty, mirroring the roles of a human engineering team. Each agent works on its own thread, while the developer stays in control.
A robust multi-agent workflow combines all aspects of the SDLC, including continuous delivery.
#ai-in-software-development #multi-agent-workflows #software-development-lifecycle #coding-assistants #collaboration-in-coding
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