What Is Conditional Routing?

What Is Conditional Routing?

Conditional routing is an LLM-gateway routing strategy that chooses a model, provider, guardrail path, cache path, or fallback path by evaluating request conditions. Instead of sending every call through one static route, the gateway checks fields such as user tier, task type, risk score, region, token budget, and required latency before the provider call. It shows up in production traces as a route decision, and FutureAGI exposes it in Agent Command Center through the gateway:routing surface.

Why it matters in production LLM/agent systems

Bad routing hides as ordinary success. The request returns 200, but the wrong model answered: a regulated workflow used a non-approved provider, a premium support user hit the cheap queue, or a low-risk FAQ consumed an expensive reasoning model. Conditional routing prevents those mistakes by making the decision explicit and observable.

The pain lands on several teams. Developers chase inconsistent behavior because the same prompt behaves differently across tenants. SREs see p99 latency spikes when urgent traffic shares a route with batch traffic. Compliance teams cannot explain why a region-restricted user crossed a provider boundary. Product teams see conversion or escalation changes without a clean route label.

Logs usually show mismatched model or provider fields, high fallback rates for one condition, cache hit rates that only improve for one cohort, or cost-per-trace spikes after a launch. In 2026-era agent systems, one user action can create planning, retrieval, tool-selection, verification, and final-response calls. A single static route treats those calls as equivalent. Conditional routing lets the gateway send high-risk tool use through stricter pre-guardrail and post-guardrail paths, while sending low-risk summarization to cheaper, faster targets.

How FutureAGI handles conditional routing

FutureAGI handles conditional routing in Agent Command Center through the gateway:routing surface. A routing policy can include conditional routes that evaluate eq, ne, in, nin, regex, gt, lt, gte, lte, and exists against request fields before selecting a target. The inventory anchor for this family is routing/load balancing, with gateway controls such as semantic-cache, model fallback, pre-guardrail, post-guardrail, retries, timeouts, and traffic-mirroring.

Example: a support agent sends every call with metadata.account_tier = "enterprise" and metadata.risk_score >= 0.7 to a route named enterprise-risk-review. That route selects an approved provider, runs a pre-guardrail, disables broad cache reuse, and records agentcc.routing.policy_id, gen_ai.request.model, gen_ai.system, and selected provider on the trace. Lower-risk FAQ traffic can match a different condition and use semantic-cache before model inference.

The engineer then watches route match rate, fallback rate, p99 latency, token-cost-per-trace, and sampled eval failures for that route. If enterprise-risk-review shows high latency but low post-route failures, they add a faster secondary target under model fallback; if the wrong cohort matches, they fix the condition. FutureAGI’s approach is to keep route logic in the gateway, with trace evidence for every decision. Unlike LiteLLM middleware embedded in application code, Agent Command Center makes conditional routes policy objects that can be reviewed and rolled back.

How to measure or detect conditional routing

Measure conditional routing by segmenting every trace by the condition that matched, then comparing outcome quality, cost, and latency against the route’s intent.

• Route match rate - percentage of requests matching each named condition, including priority order.
• No-match/default-route rate - requests that fall through to the default route because no condition matched.
• Fallback rate by route - share of routed calls that trigger model fallback, retries, or timeout recovery.
• Trace fields - compare gen_ai.request.model, gen_ai.system, gen_ai.usage.input_tokens, selected provider, and routing policy ID.
• Eval fail rate by route - sampled outputs can run ToolSelectionAccuracy, JSONValidation, or Groundedness, depending on route purpose.
• User-feedback proxy - thumbs-down rate, refund rate, or escalation rate grouped by matched condition.

from fi.evals import ToolSelectionAccuracy

score = ToolSelectionAccuracy().evaluate(
    input=user_task,
    output=agent_tool_call,
    expected_output=expected_tool,
)

ToolSelectionAccuracy evaluates whether the agent chose the expected tool after the gateway selected the route. That helps detect routing rules that send tool-heavy tasks to a model with weak function calling.

Common mistakes

Most conditional-routing failures come from unclear predicates, missing defaults, or measuring the route without measuring the answer.

• Encoding tenant logic in app code and gateway policy at the same time, then debugging two sources of truth.
• Matching on free-form prompt text instead of structured metadata such as tenant, task type, region, and risk score.
• Forgetting the default route, so unmatched traffic silently inherits a route meant for another cohort.
• Evaluating route success only by HTTP 200 instead of quality score, latency, cost, and fallback rate.
• Letting conditions overlap without priority tests; the first matching rule may hide a more specific rule below it.