Topic starter
23/10/2026 12:46 am
TL;DR: Routing decisions for agentic workflows can be made with much lower token spend and shorter wait times, with Tetrate saying Jev projected about 98.3% lower routing token costs than larger LLMs. Routing criteria and permission boundaries still need joint governance, or speed gains can outpace review of downstream outcomes.
Editorial analysis by NHI Mgmt Group, based on content published by Tetrate: “Jev-directed Model Routing”.
By the numbers:
- At 1,000 routing decisions, the benchmark projected roughly 3 cents for Jev versus $1.61 for GPT-6 Sol.
- Across 1,000 developers making that many decisions each month, the annual volume reaches 12 million decisions, about $322 for Jev versus $19,281 for Sol.
- Jev made the decision 12.3 times faster than Luna and 19.8 times faster than Sol, comparing medians.
Key questions
Q: Why can cheaper routing still leave overall agent risk unchanged?
A: Because routing cost is only one part of the workflow.
Q: What are the signs that model routing is becoming a governance problem?
A: The clearest signs are repeated route changes without clear criteria, rising decision latency across many small tasks, and teams unable to explain why a specific route was chosen.
Q: How should security teams govern model routing in AI agent workflows?
A: Security teams should treat model routing as a policy decision, not a performance shortcut.
Practitioner guidance
- Define routing policy as governed logic Document which tasks may be routed by a decision model, which criteria are allowed, and which routes require host enforcement before tool use.
- Measure routing separately from task completion Track token spend, decision latency, and downstream task outcomes as separate metrics so you can see whether routing changes total workload economics.
- Keep permissions outside the router Maintain explicit permission checks in the host or execution layer so a routing model cannot become the authority boundary for tool access.
Bottom line: Model routing is now a control point in agentic workflows because it decides which model or action receives the next task.
Glossary terms: Model Routing Layer, Routing Control Debt, Typed Decision Model, Host-Enforced Permissions
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This topic was modified 7 days ago by NHI Mgmt Group
23/10/2026 9:49 am
Model routing is becoming part of the agent identity control plane: once a system decides which model or action receives a task, that decision behaves like policy, not just optimisation. Tetrate’s benchmark shows the routing step can be cheaper and faster, but the deeper point is that route selection shapes what the agent can do next. Practitioners should treat routing criteria as governed access logic for agentic execution.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Should agent workflows keep permissions in the host or inside the router?
A: Keep permissions in the host or execution layer. A router can rank, classify, or select, but it should not own the final authority to grant tool access, because that collapses the separation between decision support and policy enforcement.
👉 Read our full editorial: Model routing for agentic workflows: lower cost, lower wait
This post was modified 7 days ago by NHI Mgmt Group