Notion uses Decagon AI agents across about one million customer inquiries a year. Decagon reports ticket resolution time improved up to 34% and an average ask-for-human rate of 3.4%.
Reported outcomes
ticket resolution time
improved up to 34%
Source [1] · self-reported
ask-for-human rate
3.4% average
Source [1] · self-reported
Outcomes are reported exactly as stated by the source; we have not independently audited them.
How it works
- Decagon AI agent answers inbound customer conversations.
- Escalates to human agents when needed.
Risks & compliance
- Incorrect answers to customers; escalation paths needed
- Vendor-published results; not independently audited
Build this yourself
Tools mentioned & how to build it
Disclosure: Some links may earn us a commission; it never affects grades. Tools are listed by fit to this case, never by payment.
Tools mentioned
- Decagon (Decagon)
AI customer-support agents for chat, email and voice.
Build a version yourself
- n8n
Open-source workflow automation with AI agent nodes; self-host or cloud. - HubSpot (Breeze agents)
SMB-friendly CRM with Breeze AI agents for prospecting and support. - Relevance AI
Build an 'AI workforce' of agents, e.g. BDR agents that research and book meetings.
Sources
“The first major impact of implementing Decagon was ticket resolution time improved up to 34%.”Notion and Decagon
Grade rationale: Named organization with a quantified outcome from the vendor or the organization itself (self-reported). Automated check on Oct 17, 2026: 1/1 quotes and 2/2 outcome claims matched the source. Compiled by the Agent Casefile research desk (AI-assisted; see methodology). Spot an error? Request a correction.
