Customer context workflows
Bring people, companies, tickets, conversations, and AI summaries together before support action happens.
Use this product state to connect the visible UI to the operational decision the lesson is teaching.
Learn the system by following the product states.
Use the screenshots as the primary map: start with the full context, trace the connected workflow, inspect the focused UI, then compare against the completed operating state.
Customer signalWorkflow pathVisible outcomeUse this product state to connect the visible UI to the operational decision the lesson is teaching.
Customer signalWorkflow pathVisible outcomeThis is the state to compare against when the system is configured, connected, or ready for review.
Summary
Bring people, companies, tickets, conversations, and AI summaries together before support action happens.
Concepts covered
Step breakdown
- Define the system to deployStart from this Support operating system and confirm the business outcome, source signals, owners, and review gates.
- Create the Frontline assetsBuild the workflow canvas, agent prompt, channels, CRM records or tables, routing logic, and Max Activity evidence described in the blueprint.
- Launch the narrow versionDeploy the smallest reliable version first, keep human review visible, then use activity and analytics to expand the system.
What you will build
A customer context assembly layer that collects Person, company, open Tickets, recent conversations, and previous AI activity before any support action runs — so every AI reply and human handoff is informed by the full customer picture, not just the current message.
After deployment: every support interaction begins with a context briefing. AI agents and human agents see the customer's history, open issues, and relevant account information before they respond.
When to use this
Agents are asking customers to repeat information they already provided in previous interactions.
AI replies are generic because the agent only sees the current message, not the history.
Escalations arrive without context and the receiving agent has to investigate before helping.
You have good CRM data but it is not flowing into your support conversations.
System components
CRM Person record: name, phone, tier, language, account owner, last contact date.
CRM Company record: segment, account status, current contract, technical tier.
CRM Ticket records: open tickets, status, category, last update, owner.
Conversation history: recent messages from this Person across all channels.
AI context summary agent: reads all available context and produces a briefing note.
Studio workflow: assembles and passes context before the AI support agent or human handoff node.
Max Activity: context briefing stored as activity note so teammates can see what was assembled.
Step-by-step implementation
1. Map the data sources you have: Person fields, Company fields, Ticket fields, conversation history. Identify which are most useful for your support agents.
2. Create a Studio workflow. Trigger: new inbound support message.
3. Add Person lookup node. Retrieve: name, tier, language, account owner.
4. Add Company lookup node (linked to Person). Retrieve: segment, account status, contract tier.
5. Add Ticket lookup node. Filter: open tickets linked to this Person. Retrieve: category, status, owner, last update.
6. Add conversation history node. Retrieve the last 5 messages from this Person across all channels.
7. Add AI context summary agent node. Pass all retrieved data. Instruct to produce a 3-bullet briefing: who the customer is, what they have open, what they are contacting about now.
8. Pass the briefing to the next node — either AI support agent or human handoff — as a context variable.
9. Write the briefing to Max Activity as a context note so it is visible to any teammate who opens the Ticket.
10. Verify that context appears correctly in Max Activity and in the AI support agent's visible prompt.
Agent prompt
You are assembling a customer context briefing for a support interaction.
You will receive: the customer's current message, their CRM Person fields, linked Company fields, open Tickets, and recent conversation history.
Produce a briefing with 3 items: (1) Who is this customer (name, tier, account context). (2) What open issues do they have (ticket summary). (3) What are they contacting about right now (current message intent).
Keep each item to one sentence. Be specific — include names, ticket IDs, and dates. Do not add generic statements.
Output JSON: { customer_summary: string, open_issues_summary: string, current_intent: string, urgency_signal: 'none' | 'mild' | 'strong' }.
Workflow logic
Context assembled: pass to AI support agent or human handoff with briefing as a context variable.
Open ticket exists and matches current intent: route to existing Ticket owner. Include the briefing in the owner notification.
Multiple open tickets: summarize top 2 in the briefing. Route to most recent ticket owner.
No open ticket and no history: create a minimal briefing noting first contact. Proceed to standard classification.
Briefing assembly fails (lookup error): log the failure in Max Activity. Continue workflow without context rather than blocking the customer response.
Final operating state
Every support interaction begins with an assembled context briefing covering identity, open issues, and current intent.
AI support agents use context to produce specific, relevant replies instead of generic ones.
Human handoffs include the briefing so agents can respond immediately without a research step.
Max Activity stores the briefing for every interaction — visible to any teammate who opens the Ticket or Person record.
Troubleshooting
Context briefing missing Ticket data: verify the Ticket lookup filter uses Person ID, not phone number directly. The Person ID is the stable link to Tickets.
Briefing is too long for AI agent context: limit conversation history to the last 3 messages (not 10). Summarize Tickets to status + category only.
Person not matching across channels: a customer contacting via WhatsApp and Instagram may have two Person records. Merge duplicates in CRM using phone as the canonical identifier.
Context assembly slowing down response time: run Person, Company, and Ticket lookups in parallel if your workflow builder supports it.
Operational playbook
Use Customer context workflows as part of the Frontline Solutions Support operating loop: inspect the current product state, confirm the source context, and decide what should happen next.
The goal is not to memorize screens. The goal is to understand how the product surface supports repeatable work, AI assistance, and accountable handoff.
Best practices
Start with the operational job before changing configuration. Name the owner, define the trigger or source context, and decide how the result should be reviewed.
Prefer narrow, inspectable setups over broad automation. Teammates should be able to explain why the system took an action from the visible product state.
Platform layers involved
Studio defines the workflow and AI agent behavior. Channels capture the customer interaction. CRM provides customer memory. Max Activity shows what the system did and what needs follow-up.
Use the solution page as the business-facing map, then open the related product tutorials when you need configuration detail.
Outcome metrics
Track a small set of operational signals: response time, handoff rate, completion rate, escalation quality, CRM field completeness, reply rate, and repeated failure patterns.
The metric should reflect the business outcome, not only whether the automation ran.
Agent Builder visual map
FAQs
What does Customer context workflows teach?
Bring people, companies, tickets, conversations, and AI summaries together before support action happens.
How should teams use this lesson?
Use it as an implementation guide: create the assets, connect the systems, verify the completed state, and operate the blueprint with review gates.
Which Frontline products are involved in this solution?
Most solution playbooks connect Studio workflows, Channels, CRM records, AI agents, and Max Activity. The business outcome is the entry point; the platform layers make it operational.
How should we decide whether to automate this use case?
Automate when the path is repeated, has clear source context, needs consistent follow-up, or benefits from AI classification, routing, summaries, or structured capture. Keep human review where judgment or risk is high.
What should be visible before this goes live?
Verify the workflow trigger, CRM context, channel permissions, AI agent instructions, handoff owner, logs, and Max Activity output so the team can trace what happened.
How do we keep the customer experience personal?
Use CRM context, conversation history, and approved message patterns. AI should use relevant customer memory, not generic copy, and workflows should escalate when context is missing.
What is the best first version of this playbook?
Start with one channel, one workflow, one owner group, and a narrow success metric. Expand only after logs, activity, and customer-facing outputs are trustworthy.