When Your Team is AI: Designing Multi-Agent Workflows

In modern software teams, the critical parts of the development lifecycle — code reviews, requirement clarification, and test design — are still manual, inconsistent, and hard to scale. What if these responsibilities could be delegated to a team of AI agents? In this talk, it is presented a real-world system built using n8n and OpenClaw that augments the software development lifecycle through collaborative AI agents. We start from a practical use case: an AI-powered code review agent triggered on Bitbucket pull requests. This agent analyzes code changes, validates them against internal guidelines, and checks alignment with Jira tickets and acceptance criteria. From there, we evolve the system into a multi-agent architecture: - a Product Manager agent that refines and structures requirements, - a Developer agent that performs context-aware code reviews, - a QA agent that generates test cases and identifies edge scenarios. Rather than relying on a single general-purpose LLM, this approach models how real teams work: through specialized roles, structured artifacts, and coordinated workflows. The talk will cover: - how to design agent responsibilities and boundaries, - how to orchestrate agents using workflow automation, - lessons learned from building and running such a system, - and common pitfalls when moving from single-agent to multi-agent architectures. Attendees will leave with practical design patterns to build their own AI-driven development workflows — and a new perspective on what “AI teammates” can actually mean in production systems.