2 Support Operations AI Agents, Live in Production.
2 live agents automate support operations within customer service. 1 runs on a schedule and 1 runs on demand. Each one publishes the inputs it needs, the steps it works through and what it hands back.
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Campaign Inquiry Readiness
Before a campaign goes live, work out what support will be asked, whether the answers exist yet, and which claims in the campaign will generate questions nobody can answer.
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Support Interaction Analysis
Read what customers actually wrote across a period of tickets and chats — the tone, the effort it cost them, and the moments a conversation turned — rather than what a survey afterwards remembers.
Knowing what a campaign will be asked before it goes live
Support operations carries the consequences of decisions made elsewhere. Marketing launches a campaign, and the questions it generates arrive in a queue that was staffed and briefed for last week’s volume — including the questions arising from claims nobody in support had seen, let alone been given an answer to. The pattern is entirely predictable and almost never pre-empted, because the check requires reading the campaign as a customer would and asking what it invites someone to ask. The measurement problem is adjacent. Satisfaction is assessed by survey, which is a reconstruction after the fact by a customer who has moved on; what they actually wrote during the interaction — the tone, how much effort it cost them, the exact point where a conversation turned — is far more informative and nobody reads it at volume.
These agents work on the inputs rather than the queue. Campaign Inquiry Readiness establishes, before a campaign goes live, what support will be asked, whether the answers exist yet, and which claims in the campaign will generate questions nobody can answer. That third output is the one that changes the campaign rather than just the staffing. Support Interaction Analysis reads what customers actually wrote across a period of tickets and chats — the tone, the effort it cost them, and the moments a conversation turned — instead of what a survey afterwards remembers. Two agents cover this process, and both produce analysis for an operations lead to act on; neither changes staffing or replies to anyone.
What this moves
- Campaign questions support cannot answer
- The claims that will generate questions are identified before launch, so the answers exist on day one rather than being improvised in the queue.
- Insight from what customers actually wrote
- Tone, effort and the moments a conversation turned are read from the interactions themselves rather than from a survey completed afterwards from memory.
Customer Service
How AI agents handle support operations
Drawn from the 2 agents above — what they require, how they run, and what comes back.
What they need
- The campaign
- Launch details
- Interaction export
- Analysis settings
What comes back
- Support readiness
- Readiness note
- Claims that will generate unanswerable questions
- What will be asked, and whether we can answer it
- Before it goes live
- What support has today
- What it cost the customer
- What customers are telling us
How they run
- Run on demand
- 1
- Runs on a schedule
- 1
- Steps per run
- 3–5
- Credits per run
- 7–8
- Use a knowledge base
- 1
Where support operations fits in customer service
Most tickets are about one customer’s specific order or account. An agent that can read that state actually fixes the problem, instead of replying with a help article the customer already found.
Next Step
Deploying support operations agents
These run as-is against the inputs listed above. Most deployments adapt one — a different source system, a different tolerance, a different approval path. The first call establishes which.