Multi-agent pipelines are not just a trend. They are a different way of building.

Six months ago, chaining multiple AI agents in a production workflow meant stitching raw APIs together and managing every handoff manually. That friction is mostly gone. Orchestration frameworks with persistent memory, conditional branching, and structured output enforcement make it possible to build sequential agent pipelines where each agent has a defined role and passes a validated output to the next.

The part most people underestimate is the supervisor pattern. An orchestrator that delegates to specialised sub-agents and then runs a validation pass before anything moves downstream catches errors that would otherwise propagate through the system and corrupt the final output.

Re-injecting the original task objective at each handoff addresses agent drift, the gradual degradation of context that accumulates when you chain multiple LLM calls without reinforcing the goal. The architecture is becoming standard for serious production builds.

What frameworks are people here using for multi-agent orchestration? What patterns have made the biggest difference in reliability?

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Multi-agent pipelines are not just a trend. They are a different way of building.