Multi-agent workflows a single engineer can reason about
A focused multi-agent system one engineer can reason about beats a sprawling one nobody trusts. Scope each agent, define tools explicitly, and observe everything.
The best multi-agent system is the one a single engineer can hold in their head, trace when it misbehaves, and recover when it fails. A sprawling swarm of loosely scoped agents looks impressive in a diagram and becomes untraceable in production: when something goes wrong, nobody can say which agent did what or why. A focused system with a few well-scoped agents, explicit tools, and observability from the start is slower to look clever and far more likely to ship. Reasonability is the feature.
Why does sprawl kill trust?
Because trust comes from being able to predict and explain behaviour, and sprawl destroys both. Every additional agent multiplies the interactions you have to reason about. Add vague responsibilities and shared tools, and you get emergent behaviour nobody designed and nobody can reproduce.
When an engineer cannot answer “what happens if this step fails” or “which agent touched this record”, the system is not production-ready, regardless of how well it demos. The teams that succeed with agents are not the ones with the most agents. They are the ones whose agents each do one legible thing.
What does a reasonable design look like?
An orchestrator with a small number of scoped agents underneath it, each owning a bounded task and its own narrow set of tools, with everything flowing through a logging layer.
Which patterns keep it reasonable?
Four, and they reinforce each other.
Scope each agent to a bounded task. One agent, one job, one clear input and output. A retriever retrieves, a writer writes, a checker checks. When responsibilities are sharp, you can predict behaviour and swap a component without disturbing the rest.
Define tools explicitly. Each agent gets exactly the tools its task needs, no more. Narrow tools mean fewer ways to go wrong and a tool list a reviewer can read in one sitting. This is the same boundary discipline that makes MCP systems hold up under scrutiny.
Build observability from the start. Log every delegation, every tool call, every result, before you have a problem. Observability bolted on after an incident is too late. With it, debugging is reading a trace instead of guessing.
Make failure modes predictable and recoverable. Decide in advance what happens when an agent fails: retry, fall back, or halt and surface the error. An unhandled failure in one agent should never silently corrupt the whole run.
None of this is exotic. It is the same engineering discipline you would apply to any distributed system, pointed at agents. The teams getting real value are the ones who resisted the urge to build something sprawling and instead built something they could explain. For more on why pilots fail to reach this kind of production discipline, see why AI pilots stall before production.