Linear pipelines look great on whiteboards but break immediately in production. Real world complexity requires graph architectures with cycles and that changes everything about how you build agent systems.
Consider a content generation pipeline. The design seems simple enough. Research feeds into writing which feeds into editing. Clean linear flow. Then reality hits. Research needs clarification from writing. Writing needs style guidelines from editing. Editing triggers additional research. The linear model falls apart instantly.
The solution is a directed graph with cycles. Components can call each other based on actual needs. Research asks writing for clarity. Writing asks editing for guidelines. Editing triggers new research. The graph topology evolves dynamically based on the task.
Managing cycles without infinite loops requires careful design. Each edge in the graph has a cost. Each component has a budget. When the budget depletes that path terminates. This economic constraint prevents runaway recursion while allowing necessary iteration.
Keep reading
- Sub Agents Changed Architecture
Monolithic agents are brittle. Hierarchical delegation might be how complex AI systems should work.
- Multi Agent Coordination Works
Single agents get overwhelmed by complexity. Agent swarms with emergent coordination might be the answer.
- Agent Guardrails in Production
Letting agents handle real money requires serious safety measures. The industry has learned some lessons the hard way.