Large language models have evolved from simple chat interfaces into autonomous systems capable of planning, reasoning, and interacting with external tools. The next stage of this evolution is multi-agent software engineering, where specialized AI agents collaborate to solve complex business workflows instead of relying on a single monolithic model. A planner may decompose work, researcher agents retrieve enterprise knowledge, coding agents generate implementations, reviewer agents validate outputs, and execution agents perform approved actions. Although this architecture appears attractive, production deployments reveal that coordinating multiple agents resembles building a distributed system far more than writing prompt chains.The primary challenge is not model intelligence but system reliability. Every additional agent introduces another opportunity for hallucinations, context loss, latency, retries, and cascading failures. A workflow containing five agents with individually high accuracy can still produce inconsistent outcomes because each handoff becomes another source of uncertainty. The engineering challenge therefore shifts from prompt engineering toward orchestration, state management, resilience, and observability.