Customer success & support · Frontier · updated 2026-10-23

AI support agent that resolves, not deflects

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.

Building block of the Autonomous support agent with actions.

1Is this my problem?

Klarna and Intercom's customers show the ceiling: most routine questions can be resolved end to end, and human agents move to harder work.

You'll recognise this if…

Agents spend the day on refunds and resets.
Slow responses at peak times.
Deflection bots that frustrate customers.

Companies affectedSaaS, fintech and e-commerce with 1,000+ tickets a month. Typical fit: 10 staff to 50 staff.

Cost of doing nothing (estimate)Roughly 20–60 hours a week of manual work stays on the team, and the work is done inconsistently.

2Is this solution worth it?

Reported results

Intercom Fin: 51% average resolution rate out of the box, up to 86% for some customers; Synthesia saved over 1,300 hours in six months (source)
Klarna (first month, 2024): 2.3M conversations, equivalent of 700 full-time agents, resolution time from 11 minutes to under 2 (source)

Quoted from the linked source; results vary.

Your ROI, with the assumptions shown

Prefilled with illustrative assumptions (30 h/week at $35/h, 50% automated, build $25,000). Change them to your numbers.

Time saved (estimate)20–60 h/week

Typical build cost (estimate)$5k–$40k

DifficultyAdvanced

Steps6

Limits and when not to build it

Skip it if: Few tickets or no written policies.

Evaluate against real past conversations before launch, and on every change.
Actions need strict limits (amount caps, allowed operations) and audit logs.
Always offer a human; Klarna kept that option.

3How does it work?

One small internal tool does this for you. The same job, before and after:

Before · by hand

Customer message (chat / email): someone notices
Retrieve from help center + policies
Answer or plan an action (by hand)
Call API: refund, change plan, look up order
Unsure or sensitive → human
Resolution + CSAT tracking

AI support agent that resolves, not deflectsIllustrative

Starts automatically: customer message (chat / email)
Retrieve from help center + policies
AI: Answer or plan an action
Call API: refund, change plan, look up order
Unsure or sensitive → human
Resolution + CSAT tracking

✦ AI step · ● human checkpoint · ✓ automatic

The AI's job here

Understanding the question, grounding the answer in your content, and safely choosing an action. The agent-as-a-product option (Fin) avoids building this yourself.

Answer or plan an action

Your tool connects to

Help deskBillingAI model

Under the hood: flow, architecture and vendors

When: Customer message (chat / email)
Retrieve from help center + policies
Answer or plan an action
Call API: refund, change plan, look up order
Unsure or sensitive → human
Resolution + CSAT tracking

Architecture

Data sourcesHelp center, policies, order/billing APIs

ModelClaude (Fin) / GPT-4-class (Klarna)

LogicRetrieve → answer/act → confidence check → escalate

Human checkpointEscalation path and human review of sampled conversations

Vendors commonly used

Intercom FinClaudeOpenAIZendeskStripe

Maturity ladder

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

Level 1Basic automation
Trigger: customer message (chat / email). Fixed rules then retrieve from help center + policies, call api: refund, change plan, look up order, unsure or sensitive → human. No AI involved.
Level 2AI-assisted
An LLM handles the judgement step (answer or plan an action) and a person reviews every output before it is used.
Level 3Autonomous agent
The agent runs end to end on its own and only stops for exceptions. Escalation path and human review of sampled conversations.
Level 4Self-learning system
Becomes part of the autonomous support agent with actions: 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.

Level 4 lives in the Autonomous support agent with actions: 6 stages with a learning loop.

Seen in the wild

Links checked 2026-10-23. We describe these builds from public material; we are not affiliated with these companies.

4Who can build it?

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