ENTERPRISE CUSTOMER EXPERIENCE
Customer Support Agent Platform
A multi-agent support platform that carries a conversation from triage through resolution while keeping a clear path to a person.
- PROJECT
- Customer Support Agent Platform
- INDUSTRY
- Enterprise Customer Experience
- STACK
- Python · LangGraph · Multi-Agent Orchestration · RAG · OpenAI · Anthropic · Vector DB
OVERVIEW
The project
Support volume was being absorbed by handoffs, with a conversation passing between queues while the customer repeated themselves at every step. Allzone built a platform where specialised agents own distinct parts of the workflow, from triage to resolution, sharing enough context that the conversation stays coherent as it moves and reaching a person when it should.
CHALLENGE
What made it difficult
Support automation is usually judged on deflection, which is the wrong measure taken alone. A conversation closed without resolving anything costs more than the one it appeared to save.
Splitting the work across specialised agents fixes scope but creates a continuity problem in its place. Each agent needs the conversation history and the customer context without inheriting the whole system's permissions along with it.
WHAT WE DID
The work delivered.
- 01
Multi-agent architecture
Responsibilities separated across triage, support, escalation and engagement workflows instead of one general assistant handling everything.
- 02
Knowledge and memory
LLMs integrated with enterprise knowledge sources, retrieval and conversation memory, with summarisation carrying context across agent boundaries.
- 03
Workflow orchestration
Routing that moves a request through the right specialised capability, with an explicit path to a human agent when the conversation needs one.
- 04
Measurement
Feedback loops, monitoring and performance analytics measuring how the agents actually perform rather than how they were expected to.
ARCHITECTURE
How it fits together
- Multi-agent orchestration separating customer interaction from knowledge driven tasks
- RAG and contextual memory maintaining conversation state across agent handoffs
- Observability and feedback capture on agent performance
- Controlled automation, with human involvement retained wherever the workflow requires it
TECHNOLOGY
The stack
- Python
- LangGraph
- Multi-Agent Orchestration
- RAG
- OpenAI
- Anthropic
- Vector DB
OUTCOME
What exists now.
A conversation moves between specialised agents without the customer having to repeat themselves at each step
A request that needs a person reaches one, rather than being closed to protect a deflection figure
Agent performance is measured in production, so a change is judged on evidence rather than on impression
NEXT STEP
Tell us what you're building.
Bring us the problem with its real constraints attached. We will tell you what we would build, and what we would not.