Decagon alternatives

Conversational AI agent platform for complex customer-support workflows across channels.

Decagon should be evaluated on complex resolution and operational control, not just conversational fluency. A custom Nagent comparison needs equivalent channels and transaction permissions.

Read the Nagent vs Decagon guide for the detailed comparison.

Three evaluation scenarios for this category. Each names the inputs, the required output and the measure.

Knowledge answer with a verifiable source

Provide a current policy, an obsolete policy and an ambiguous customer question. Require the agent to use the right version, identify uncertainty and avoid inventing exceptions. Ask a human reviewer to verify the answer without reading a long hidden history.

Measure: answer accuracy, source freshness and appropriate escalation.

A bounded customer action

Use a test customer, a low-risk transaction and an explicit action limit. Try a request outside that limit and a case with conflicting identity information. Verify the backend state after execution; a conversational confirmation alone is not evidence that the transaction succeeded.

Measure: correct state changes, blocked unauthorized actions and recoverability.

Learning from a failed resolution

Introduce a repeat contact after an apparently successful interaction. Ask the system to identify the failure, propose a policy or knowledge correction and show the evidence. Require a human owner to approve the change before it affects other customers, then replay similar cases.

Measure: reopen rate, correction quality, regression results and human workload.

The evaluation owner should be the customer-experience and support-operations leads. Use durable, accurate resolutions with appropriate human escalation as the business target.