What's the risk of letting AI agents hand off work to each other without human review?

Error compounding is the core issue. Each agent in a workflow assumes its input is accurate. If a research agent misinterprets a data source or pulls outdated information, the analysis agent builds conclusions on that flawed foundation. The writing agent then produces content that confidently states something incorrect. None of the agents question what came before because questioning inputs isn't their job. They execute their instructions against whatever they receive.

This risk increases with workflow length and complexity. A two-step workflow has one handoff point where errors can propagate. A five-step workflow has four. Parallel branches that converge into a single step can combine multiple flawed inputs into one output, making the problem harder to trace. The more agents involved, the more opportunities for small issues to accumulate into larger ones.

The practical consequence is wasted work. If you only review the final output and find problems, you often need to trace back through multiple steps to find the source. Fixing it may require rerunning several agents. A single review checkpoint mid-workflow would have caught the issue earlier and saved the downstream effort. Debugging a five-step chain is significantly harder than catching an issue at step two.

The solution isn't to review every handoff. That defeats the efficiency gains of automation. Instead, place review steps strategically based on risk. High-stakes outputs (anything published, sent to customers, or used for decisions) warrant checkpoints. Intermediate transformations (reformatting, combining, filtering) usually don't. As you gain confidence in specific agents and workflows, you can reduce checkpoints. Start with more oversight and relax it based on observed reliability. The goal is informed trust, not blind automation.