Frontier system · Customer success & support · 6 stages · updated 2026-10-23

Autonomous support agent with actions

A support agent that answers from your docs, follows policy, performs real actions like refunds and plan changes, collects pre-work for engineers, and hands off cleanly. Every change is tested against a baseline.

1Is this my problem?

Ticket volume grows with customers, and most tickets are repeat questions or simple account actions a person shouldn't need to do.

You'll recognise this if…

First response times slip at peak hours and weekends.
Agents spend the day on refunds, resets and "where is my..." questions.
Engineers are pulled into tickets that lack basic details.

Cost of doing nothingSupport headcount grows linearly with revenue and customers wait longer as you scale.

Companies affectedSaaS, fintech and e-commerce companies with 1,000+ tickets a month, 3-50 support staff, and a decent help centre. Seen at Klarna, Intercom customers, Retell AI and AssemblyAI.

2Is this solution worth it?

Reported results

Fin resolves 51% of conversations on average out of the box, up to 86% for some customers (source)
Klarna: 2.3M conversations in month one, two-thirds of chats; resolution time from 11 minutes to under 2 (source)
Retell AI automated 80% of L1 tickets, saving 12-15 engineer hours a week (source)

Quoted from the linked source. Results vary by company and setup.

Your ROI, with the assumptions shown

Prefilled with our illustrative assumptions for a typical team (40 h/week of manual work at $35/h, 50% automated). Change them to your numbers.

Build cost (estimate)$15k–$60k

Time to first version (estimate)6-12 weeks

AI agent stages3 of 6

Human checkpoints1

Limits and when not to build it

Not yet if you get under ~300 tickets a month, have no written policies or docs, or most tickets need deep investigation.

Guardrails it needs

Action limits by type and amount.
A human handoff is always one message away.
Evals gate every change.
Personal data masked in logs.

3How does it work?

The agency builds you one internal tool, Support Copilot, that runs the whole process. Watch it work on a sample case:

Support CopilotIllustrative demo with sample data

Pick a sample on the left and press Run example to watch Support Copilot work through a case step by step.

Your tool connects to

Help deskData warehouseAI model

Most of it is ordinary automation. AI agents handle 3 of 6 steps, and people approve at 1 checkpoint.

Under the hood: stages, vendors and the learning loop

AI agent Automation Human checkpoint

1Understand
Classify intent, language and urgency.
ClaudeGPT
AI agentDecides: Classify
2Retrieve
Search help centre and past tickets with contextual retrieval.
Vector DBHelp centre
Automation
3Answer or act
Answer in brand tone, or call an action API (refund, change plan) within policy limits.
Intercom FinOpenAI Agents SDK
AI agentDecides: Answer or act, within limits
4Runbooks and pre-work
For known L2 cases, follow a runbook and collect the IDs engineers need.
Pylon
AI agent
5Hand off
Low confidence or out-of-policy goes to a person with the full context.
ZendeskIntercom
Human
6Evaluate
Each prompt or model change runs against real past tickets: accuracy, resolution, CSAT.
n8n Evaluationscustom evals
Automation

Learning loop

Unanswered and handed-off questions are clustered into a weekly knowledge-gap report; docs get fixed, and every real failure is added to the eval set.

Example run

Illustrative sample data showing what moves between stages. Not taken from the sources.

1
Understand · in"I was charged twice this month"
outIntent: billing/duplicate charge, urgency medium
2
Retrieve · inQuery
outPolicy doc: duplicate charges refundable within 30 days
3
Answer or act · inOrder #4471
outRefund of $49 issued via API; reply sent
4
Runbooks and pre-work · inn/a
outNot needed
5
Hand off · inn/a
outNot needed (confidence 0.91)
6
Evaluate · inNew prompt v12
outAccuracy 93% vs 91% baseline on 400 past tickets: ship

Vendors commonly used

ClaudeGPTVector DBHelp centreIntercom FinOpenAI Agents SDKPylonZendeskIntercomn8n Evaluationscustom evals

Where AI decides and where people do

AI agents decide

Understand: Classify
Answer or act: Answer or act, within limits
Runbooks and pre-work: For known L2 cases, follow a runbook and collect the IDs engineers need.

Human checkpoints

Hand off: Low confidence or out-of-policy goes to a person with the full context.

Maturity ladder

What each level adds. Most teams start at level 1 or 2 and climb. Highlighted: where this build sits.

Level 1Basic automation
Canned replies and a help-centre search box.
Level 2AI-assisted
An LLM suggests answers that agents send.
Level 3Autonomous agent
An agent answers, takes bounded actions like refunds, runs runbooks and hands off. Evals gate changes.
Level 4Self-learning system
Failures and handoffs become knowledge-gap reports and eval cases each week.

Building blocks

Start with one of these smaller workflows and add stages.

AI support agent that resolves, not deflectsIntercom Fin: 51% average resolution rate out of the box, up to 86% for some customers; Synthesia saved over 1,300 hours in six months

A support agent answers from your knowledge base in your tone, follows your policies, and takes actions (refunds, account changes) through APIs, handing over to people when unsure..

Customer success & supportFrontier~20–60 h/week$5k–$40k Ticket pre-work and runbooks for technical supportRetell AI: 80% of L1 tickets automated and 12–15 engineer hours saved per week (as reported by Pylon)

Before an engineer sees a ticket, an AI agent answers L1 questions from docs, runs structured runbooks for known L2 processes (e.g.

Customer success & supportAdvanced~10–15 h/week$3k–$10k Support ticket triage and suggested resolution

New tickets are classified, prioritised and labelled; similar resolved tickets are found and a suggested resolution is attached for the agent..

Customer success & supportStarter~3–8 h/week$1k–$3.5k Bug and request triage with duplicate detection

Every new issue is compared against your history to suggest team, assignee, labels, project and likely duplicates, with the reasoning shown.

Customer success & supportStarter~2–5 h/week$0–$1.5k Daily support digest in Slack

Every morning, AI summarises yesterday's escalations, resolved issues and recurring themes into one formatted Slack post; ticket categories and reply counts also go to a sheet for metrics..

Customer success & supportStarter~1–3 h/week$500–$1.5k

Seen in the wild

Links checked 2026-10-23. The pipeline combines patterns from these public builds; we are not affiliated with these companies.

4Who can build it?

Matched on the services, platforms and industries agencies list on their own sites, and on their published projects.