Multi-Model Routing

And the patterns that stand alongside it, or against it —

• complementsCost Gating★★— Block actions whose expected cost exceeds a threshold without explicit user (or operator) acknowledgement.
• complementsFallback Chain★★— Try a primary handler; on failure or low confidence, fall through to a sequence of fallback handlers.
• alternative-toHero Agent✕— Anti-pattern: stuff every capability into one agent with one giant prompt.
• complementsProvider Fallback★★— When one provider's API errors mid-stream, transparently switch to another provider while preserving state.
• alternative-toHidden Mode Switching✕— Anti-pattern: silently swap the underlying model between requests without disclosing the change to users or operators.
• complementsMultilingual Voice Agent Stack★— Compose a voice agent as a tightly co-located pipeline of speech-to-text, language-aware LLM reasoning, and text-to-speech, where one vendor owns all three so language and dialect propagate cleanly across stages.
• alternative-toRL-Trained Conductor Orchestrator·— Train a small meta-model with reinforcement learning to dispatch sub-tasks across a pool of frontier LLM workers, learning the communication topology end-to-end and allowing the conductor to recursively invoke itself as a worker.
• complementsProvider-String Routing★— Select the model and provider for a request through a single namespaced string (`provider/model`) backed by env-var credentials, so the caller specifies what to run with one parameter rather than a typed provider object.
• alternative-toVendor Lock-In✕— Anti-pattern: couple application code directly to one model provider's SDK, request shape, and proprietary features so that switching providers requires rewriting application code rather than swapping an adapter.
• complementsAdaptive Compute Allocation★— Allocate inference-time compute (thinking tokens, samples, depth, model size) per query based on input difficulty, rather than using a fixed budget across all queries.
• complementsHybrid Symbolic-Neural Routing★— Per query, route between a symbolic path (rule engine, knowledge graph) and a neural path (LLM), using the LLM for interpretation and the symbolic layer for exact constraints.
• alternative-toHierarchical Retrieval★★— Route a query through a multi-level cascade — coarse source or index selection, then per-source narrower retrieval, then chunk-level — so each retrieval decision is pushed to the cheapest tier that can answer it.
• alternative-toTop-Tier Model For Everything (Cost)✕— Anti-pattern: route every request through the highest-tier model regardless of difficulty, treating cost as a model-choice problem instead of a routing one.
• complementsLarge Action Models (LAMs)·— Use a model class specifically trained for action execution (tool calls, UI navigation, workflow steps) rather than text generation, when the workload is dominated by reliably completing actions in real systems.
• complementsMRKL Systems (Modular Neuro-Symbolic)★★— Route each request through an LLM dispatcher to specialized symbolic or neural expert modules (calculator, knowledge base, code executor) rather than asking one LLM to do everything; integrate the modules' results for the final response.
• complementsLarge Reasoning Model (LRM) Paradigm★— Route reasoning-heavy tasks to a reasoning-tuned model that trades inference time for deliberation, rather than to a fast LLM that exhibits premature-closure.