What is Router Agent?
Router Agent is an AI-assisted or deterministic routing component that maps a request to one or more authorized execution paths under an explicit route catalog, decision policy, and fallback contract.
How It Works
Define a versioned route catalog
Each route needs a stable ID, owner, capability description, input schema, required scopes, supported risk class, cost and latency budget, and terminal or return behavior. Filter routes by authentication, authorization, region, data policy, and capability before any semantic ranking. A model-generated route name is only a proposal; application code must reject unknown, disabled, or unauthorized destinations.
Rules, classifiers, and LLM routers
Rules are deterministic and auditable for stable, high-precision conditions. Embeddings and trained classifiers can handle recurring semantic categories at lower runtime cost, but require labeled data and drift monitoring. LLM routers support nuanced or compositional input and can emit multiple targeted sub-questions, but add variance, latency, and prompt-injection exposure. Hybrid systems usually apply hard policy rules first, then rank only the remaining safe candidates.
Abstention, fallback, and ownership
A route decision should include the catalog version, selected candidate or candidates, score or reason code, and fallback. Low confidence, close competing routes, unsupported intent, missing data, and high-impact requests should clarify, choose a safe default, or escalate instead of forcing a guess. Routing differs from handoff: routing chooses a path, while handoff changes the active task owner and context contract.
Evaluate decisions by downstream outcomes
Build a labeled routing set from representative traffic, ambiguous cases, policy boundaries, and production failures. Report a confusion matrix and per-route precision, recall, abstention, unsafe-route, and no-route rates; aggregate accuracy can hide a dangerous minority route. Also measure routing latency, downstream task success, retries, total cost, human overrides, and drift. Evaluate the complete route-plus-handler outcome, not only whether the classifier matched a label.
Key Characteristics
- Selects from a finite, versioned catalog of executable destinations
- Filters candidates by policy and capability before semantic scoring
- May use deterministic rules, classifiers, embeddings, LLMs, or a hybrid
- Supports explicit clarification, abstention, fallback, and escalation states
- Records route version, decision evidence, budget, and downstream outcome
- Requires per-route evaluation because error costs are asymmetric
Common Use Cases
- Dispatching support requests to billing, access, incident, or human-review workflows
- Selecting authorized retrieval sources and targeted sub-questions for a knowledge request
- Choosing a model path under measured quality, capability, latency, and cost constraints
- Routing an agent step to a specialist while preserving task ownership
- Blocking or escalating requests whose destination requires unavailable permissions
Example
Loading code...Frequently Asked Questions
Does a Router Agent need to be an LLM?
No. Stable high-precision conditions often belong in deterministic rules. Embeddings or trained classifiers suit recurring semantic classes, while an LLM can help with nuanced or multi-route requests. Select the simplest method that passes per-route outcome, latency, cost, and audit requirements.
How is a Router Agent different from a Supervisor Agent?
A router primarily chooses the next path and may be stateless after dispatch. A supervisor continues to own shared task state, delegates work, processes results, manages budgets, and decides when the overall run ends. One system can use a router inside a supervisor without making the concepts equivalent.
Can classifier confidence be used directly as a routing threshold?
Only after calibration on representative traffic. Raw probabilities or LLM self-reported confidence may not match observed correctness and can drift by route. Calibrate and monitor per-route errors, define special zero-tolerance policy classes, and provide abstention or human review when the evidence is insufficient.
When should a Router Agent ask for clarification?
Clarify when required fields are missing, safe candidates are tied, confidence is below a calibrated threshold, or different routes would produce materially different side effects. Do not ask when a deterministic policy already requires refusal or human approval.
How should routing quality be evaluated?
Use a versioned labeled set and report confusion matrices, per-route precision and recall, abstention, unsafe-route and no-route rates. Then measure downstream task success, latency, total cost, retries, overrides, and production drift. A correct label with a failed handler is not a successful route.