Autonomous support agents
A multi-agent system that resolves tier-1 support tickets end to end — and hands off cleanly when a human is needed.
The brief
A support organisation spent most of its time on repetitive tier-1 tickets — password resets, order status, simple changes — leaving little capacity for the complex cases that genuinely needed a human. Ticket volume grew faster than the team could hire.
They wanted autonomous agents to own the repetitive tickets end to end, acting safely in real systems, while handing the hard cases to people with full context — and without denting customer satisfaction.
- Resolve common tier-1 tickets end to end, without a human agent.
- Hand off to people cleanly, with full context, when needed.
- Act through governed tools — refunds, lookups, updates — safely.
- Maintain or improve customer satisfaction.
- Keep every action logged and reversible where required.
- Integrate with the existing helpdesk and systems.
Our AI-native approach
We built a multi-agent system that understands the ticket, plans a resolution, and acts through governed tools to actually resolve it — not just reply. When confidence is low or the case is sensitive, it escalates to a human with the full context attached.
Quality was a hard requirement, so agent behaviour was evaluated against curated scenarios before release and monitored against CSAT in production.
What we built
End-to-end resolution
Agents handle common tickets from question to action, not just canned replies.
Governed actions
Refunds, lookups and updates run through permissioned, audited tools.
Clean human handoff
Complex cases escalate to a person with full context attached.
Helpdesk integration
Plugs into existing support systems and channels.
Quality guardrails
Behaviour is evaluated against scenarios before every release.
Full audit trail
Every action is logged and reversible where required.
How we built it
We treated the agent loop as production software: structured tool-calling over free text, typed and permissioned integrations, and an evaluation harness built before the agents were trusted with real customers.
Delivery was incremental — we hardened a handful of high-volume ticket types end to end, proved the handoff and audit, then widened coverage. A senior pod owned orchestration, integration and evaluation together.
How it works
Understand
The agent interprets the ticket and the customer's goal.
Plan
It decomposes the resolution into governed tool actions.
Act
Permissioned tools execute the resolution safely.
Resolve / hand off
It closes the ticket or escalates with full context.
Learn
Outcomes and CSAT feed evaluation and improvement.
Reliability comes from structured tool-calling against permissioned integrations rather than free-form text, so agents act safely inside real systems. A clean confidence threshold decides when to resolve and when to escalate, which protects customer experience.
An evaluation harness scores behaviour against curated scenarios, and production CSAT closes the loop so quality is measured continuously.
The impact
A large share of tier-1 tickets resolved autonomously, end to end.
Customer satisfaction held high through the transition.
Human agents refocused on complex, high-value cases.
Coverage extended around the clock without proportional hiring.
Technology stack
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