Input signals
- returns requests or alerts
- returns operating data
- help desk
- compensation policy
Back to AI agents
Customer service
Returns Agent
It can understand requests, search knowledge, answer with context, summarize cases and escalate sensitive exceptions, focused on returns. It works with authorized system context, connected tools and verifiable evidence before closing or escalating a case.
Supports the team in returns: understands requests, checks sources, prepares recommendations and executes only actions allowed by permissions and policies.
Signals, actions and outputs
This model keeps the agent from being just a conversation: it defines what it reads, what it can execute and what it leaves ready for the team.
Connected tools
- knowledge search
- intent classifier
- case updater
- response generator
Agent actions
- Classifies the request within returns and determines urgency, owner and confidence level.
- Checks knowledge search and intent classifier before recommending or preparing an action.
- Prepares drafts, tasks, alerts or updates so the team can act faster.
- Hands off when confidence is low, there is financial impact, an external commitment, a policy exception or a decision requiring human approval.
Operating outputs
- actionable returns summary with cited sources
- recommendation with confidence, owner and next step
- evidence ready for review, audit or operational follow-up
How the agent operates
The cycle starts with context, applies rules, executes actions and ends with reviewable evidence.
01
Reads context
Checks authorized sources, messages, documents or process data.
02
Reasons with limits
Uses guardrails, thresholds and policies to prioritize and decide next steps.
03
Acts or escalates
Runs an automation, prepares an answer or assigns the case to an owner.
04
Leaves evidence
Stores summaries, decisions, errors, files and session traceability.
Operating governance
Guardrails
- Human approval for critical changes, sensitive external messages or financial impact.
- Role-limited access; every lookup and action is audited.
- Mandatory escalation when sources are missing, confidence is low or a policy exception is detected.
Channels
- support portal
- CRM
- web chat
Human handoff
Hands off when confidence is low, there is financial impact, an external commitment, a policy exception or a decision requiring human approval.
Evidence
Each interaction can stay linked to session, execution, user, source consulted and proposed or executed action.
Applied real-world pattern
Based on real service-agent deployments that understand intent, search knowledge, execute self-service and transfer with context when needed.
help desk
knowledge base
CRM
chat and WhatsApp
Related agents
Customer service
Customer Service Agent
Supports the team in customer service: understands requests, checks sources, prepares recommendations and executes only actions allowed by permissions and policies.
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Supports the team in support: understands requests, checks sources, prepares recommendations and executes only actions allowed by permissions and policies.
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SLA Agent
Supports the team in SLA management: understands requests, checks sources, prepares recommendations and executes only actions allowed by permissions and policies.
View agentReview Returns Agent with a real process
We validate sources, permissions, available tools and escalation criteria before proposing the first deployment.