TECHNIQUE
Agentic Orchestration
Routing / intent classification is deployed as a control-plane decision: operators use it to choose agents, retrieval paths, backends, widgets, workflow branches, reviewers, or human experts before committing downstream work.
Use an explicit routing or intent-classification step to choose the next downstream path rather than treating the model output as the final product.
9 of 9 operators in the deployed/pilot roster are cited with routing or intent-classification evidence.Route early to scope the context, retrieval strategy, domain skill, or review profile before deeper processing.
4 of 9 operators are cited using routing to narrow context, retrieval, domain, or sub-agent scope.Use routing to select among specialist agents or assistants instead of sending every request through one generalist path.
4 of 9 operators are cited splitting work across specialists with a lead scout, classifier, supervisor, or pluggable assistant framework.Gate or suppress outputs using confidence, category, or value filters after classification.
3 of 9 operators are cited using confidence-based filtering or category suppression around routed/classified outputs.Classify customer search intent to decide which product/search surface to show, including visual widgets.
2 of 9 operators are cited using customer intent detection in visual autocomplete.Use reward-based routing to assign operational work to a human expert.
1 of 9 operators is cited routing tickets by expected reward over possible experts.Every cited operator uses routing / intent classification as an intermediate orchestration decision that changes what system component runs next: agent, assistant, backend, graph node, review profile, widget, branch, or expert.
Operators differ on what the router selects.
APPROACH 01
Select a specialized agent, sub-agent, or assistant.
APPROACH 02
Select a retrieval strategy, backend, graph node type, or memory layer.
APPROACH 03
Select a customer-facing surface or human expert.
APPROACH 04
Select a domain review profile or workflow branch.
Operators differ on the decision mechanism used for routing.
APPROACH 01
Supervisor, LLM, tool-calling, or function-calling based routing.
APPROACH 02
Learned, probabilistic, ranking, or reward-scoring models.
APPROACH 03
Workflow branches or domain profiles that constrain the path.
APPROACH 04
Post-generation category classifier that tags and suppresses classes of output.
Wrong routing in complex workflows can produce false positives or false negatives; Rexera explicitly reports agents taking the wrong path and veering off course, while Amazon and Audible add confidence filters for ambiguous general-vs-specific queries.
Noisy or low-value classified outputs hurt user trust: DoorDash says noisy generic reviewers get ignored, Uber says simple standalone prompts create many false-positive comments, and Rexera ties off-course agents to false positives and false negatives.
Routing logic needs continuous validation as systems and data change: Dropbox warns that changes to intent classification and adjacent stages can ripple into hallucinations, and Wix calls out regular updates plus heavy validation for integrity and data drift.
| Name | Kind | When | Maturity |
|---|---|---|---|
| Constrained JSON-schema classification on a fast model | pattern | routing decisions need typed enum outputs at low latency and cost | commodity |
| Embedding-similarity routing | pattern | route by nearest exemplar without an LLM call per request | established |