Is Your Multi-Agent System burning your wallet ? - Designing for Predictable Spend at Scale

As agentic systems matured beyond prototypes, we encountered a recurring engineering problem to one of our apps: adding more agents made the system worse, not better. While multi-agent architectures promised parallelism and modular reasoning, in production they introduced coordination failures, compounding errors, runaway latency, inflated inference costs, and debugging nightmares. The question quickly became not how to build multi-agent systems, but when they are justified—and when they are over-engineering.
To address this, we built and evaluated multiple agentic architectures across real workloads, ranging from single agents with structured tools to hierarchical and peer-to-peer multi-agent systems. We introduced explicit agent contracts, bounded responsibilities, shared state controls, and execution orchestration layers to contain complexity and improve reliability.
If building again, we would default to fewer agents, introduce multi-agent designs only when strict boundaries and measurable gains exist, and treat agent count as a scaling risk rather than a feature. This talk distills concrete engineering lessons, architectural patterns, and evaluation metrics to help AI engineers decide when multi-agent systems are worth the cost—and when they are not.