Customer Support Resolution Agent Architecture
A supervised AI agent pattern that triages, drafts, and resolves routine support tickets using your knowledge base and order systems.
Design targets, not measured client results.
The Problem
Why teams need this pattern
Support queues fill with repetitive questions that still need someone to look up an order, check a policy, and write a reply. Response times slip at peak volume, and experienced agents spend their day on routine tickets.
Ideal for
- Support teams with high volumes of repetitive tickets
- Companies with a documented knowledge base and an API-accessible helpdesk
Our Approach
Architectural approach
An agent that classifies each ticket, retrieves the relevant policy and customer records, drafts or sends a reply, and performs low-risk actions through scoped tools. Refunds and account changes go to a person for approval, and every step is logged.
System Design
Architectural layers & components
How data and control flow from the edge of the system to the people who use it.
- 01
Ticket Intake & Routing
Connectors to the helpdesk, email, and chat that classify intent and urgency.
- 02
Retrieval Layer
RAG over policies and help articles, with citations attached to each draft.
- 03
Agent & Tool Layer
LLM agent calling order, billing, and CRM APIs through least-privilege tools.
- 04
Approval, Eval & Audit
Human approval for risky actions, regression evals on real tickets, and full tool-call logs.
Design Targets
What this architecture is built to achieve
Targets we design toward. We confirm them against your own data and workload before you commit to a build.
- Resolution rates depend on the quality of your knowledge base and ticket mix; we measure them on your own tickets.
- Model choice and data residency are set per your compliance constraints.
What You Get
Artifacts tailored to your environment
The blueprint is a starting point. These are the working documents and code we adapt for you.
Learn about our AI Agents services- Agent and tool specification
- Evaluation dataset and harness
- Approval policy matrix
- Observability dashboards
How We Work
From first call to working prototype
- 130–45 min
Discovery call
We review your constraints, existing systems, and success criteria, and tell you honestly whether this blueprint fits.
- 21–2 weeks
Fit & feasibility workshop
We adapt the reference architecture to your stack, validate the design targets against your real data, and produce a scoped plan.
- 3Scoped per project
Prototype, then build
We ship a working slice first so you can judge the approach before committing to a full build.
Technology Stack
Default tools & infrastructure
We swap components to fit your stack.
- Claude / OpenAI
- RAG (pgvector)
- Temporal
- Python / FastAPI
- OpenTelemetry
- PostgreSQL
FAQ
Common questions
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Want an architecture like this?
Book a call and we'll tell you honestly whether this blueprint fits, and how we'd adapt it to your stack and constraints.