Beyond Single-Model Systems
Sophisticated AI applications orchestrate multiple models: routers that choose specialists, chains that refine outputs, and ensembles that aggregate perspectives. Context management becomes critical as information flows between models.
Orchestration Patterns
Sequential Chains
Model outputs become subsequent model inputs. Manage context accumulation as chain depth increases. Implement context summarization between stages to prevent window overflow. Track lineage through the chain.
Parallel Orchestration
Multiple models process simultaneously with shared context. Ensure context consistency across parallel paths. Aggregate outputs considering they share the same context baseline.
Hierarchical Orchestration
Meta-models coordinate specialist models. Route context to appropriate specialists, aggregate specialist outputs, and maintain global context awareness while enabling specialized processing.
Context Transformation
Different models may need different context formats. Implement transformation layers that adapt context for each model's requirements. Cache transformed context to avoid recomputation.
State Management
Multi-model systems generate intermediate state. Design state management that tracks context evolution, enables debugging through execution paths, and supports retry/resume for long-running orchestrations.
