Built for support automation

Build support agents around resolution, escalation and safe tool use

A support agent succeeds when it resolves a permitted case correctly or hands it to a human with enough context. KeepRouter can provide model access, scoped keys and request usage evidence; the application must own customer permissions, knowledge freshness, tool execution, escalation rules and resolution measurement. [1] [2] [3]

Last reviewed 2026-09-28 · Editorial review: KeepRouter Editorial

Measure the case, not the cheapest request

A support conversation may contain classification, retrieval, drafting, tool calls and a human handoff. A lower token rate can still cost more per resolved case if it creates extra turns or bad tool decisions. Define a case outcome before comparing models: resolved and verified, correctly escalated, reopened, unsupported answer, or unsafe action. The cost-per-resolution worksheet shows how to combine model charges with retries and human review without pretending these are KeepRouter customer results.

Separate answer generation from account actions

Work	Safe first release	Gate before expansion
Answer policy questions	Retrieve approved, dated articles and cite the passage	Sample answers for unsupported claims
Check an order or account	Read-only tool with customer authorization	Audit tool arguments and access filters
Change an order, credit or subscription	Human confirmation or handoff	Idempotency, permission and reversal tests
Escalate	Summarize the attempted path and known facts	Verify that private fields do not leak into a new case

Anthropic's tool-use documentation distinguishes client tools, which execute in your application, from server tools. A compatible model response does not give KeepRouter authority to execute your business action. Keep the tool runner and approval policy in your service. Use the agent builder guide for loop and retry boundaries.

Pick a small model shortlist and test the full path

Choose candidate model IDs and their exact endpoints from the live catalog. Begin with a redacted set of actual support intents and a separate adversarial set: missing order, conflicting policy, unauthorized user, stale knowledge, and tool timeout. Run the same cases with each candidate. Record completed outcome, escalation correctness, tool arguments, latency distribution, input/output usage and charge. OpenAI's agent evaluation guide describes traces that include model calls, tool calls, guardrails and handoffs; use an equivalent trace in your own stack even if the selected model is not an OpenAI model.

Where the gateway helps and where it stops

Keep a support-service key on the server, allow only evaluated model IDs, and set a spend limit appropriate for repeated conversations. Separate production from evaluation keys. The usage view can explain model, route, status, token and charge at request level. It cannot prove that a customer was helped, that a refund was justified, or that the retrieved article was current. Join gateway request IDs to your case ID and outcome ledger in your own system, with a retention policy for private data.

Start with answer drafting and human review. Add read-only tools only after permissions pass. Add write actions only after you can test escalation, duplicate delivery and rollback.

Frequently asked questions

Does KeepRouter resolve support tickets?

No. It provides model routes and request usage evidence. Your application owns retrieval, tools, case state and human escalation.

Should a support agent execute account changes directly?

Begin with draft and read-only paths. Test permission, confirmation, idempotency and reversal before any write action.

What is the right cost metric?

Use cost per verified resolution and cost per correctly escalated case, with model, retry and human-review costs separated.

Can a gateway request log prove answer quality?

No. Pair request metadata with a case-level evaluation and human-reviewed outcomes.

Sources reviewed

Sources last reviewed 2026-09-28