Issue No. 001 / 2026
Close
The
Loop.
An AI triage agent that classifies, routes, and resolves support tickets — then gets smarter every time a human corrects it.
System / Active
agent.triage — live
Running
› LoopDesk v0.1 — RAG loaded (8 chunks)
› Corrections store: active
customer "Charged twice this month"
classifier → BILLING (conf: 100%)
billing_agent searching docs... match found
confidence 1.0 > 0.75 → AUTO-RESOLVE
✓ resolved powered by Claude Sonnet 4.6
$
01
67→100%
Accuracy w/ Learning
2
Corrections to Fix Errors
3×
Agent Types
0
Retraining Required
Classify
Route
Resolve
Learn
LangGraph
RAG
ChromaDB
Claude Sonnet 4.6
Auto-Learning
Human-in-the-Loop
Classify
Route
Resolve
Learn
LangGraph
RAG
ChromaDB
Claude Sonnet 4.6
Auto-Learning
Human-in-the-Loop
§ 01 — How It Works
From message
to resolution
Ticket Arrives
Customer submits a support message in plain language. No categories, no forms. Just words.
Input Layer
Classify & Route
LangGraph classifier node reads intent, routes to the correct specialist agent — Billing, Technical, or General.
LangGraph Node
Resolve or Escalate
High confidence → answer sent automatically. Low confidence → human gets a pre-written handoff summary.
Output Layer
§ 02 — Auto-Learning
Gets smarter
every correction
1
Agent answers a ticket with a confidence score attached
2
Human reviewer marks it correct or wrong — adds a note if wrong
3
Correction stored and injected as a few-shot example on future similar tickets
4
Accuracy improves visibly over time — no retraining, no fine-tuning
Mirrors RLHF at a systems level — without touching model weights. Pure prompt engineering at production scale.
○
△
□
◇
↺
§ 03 — Live Demo
Try it
right now
Describe your issue
First request may take up to 60 seconds — server warming up.
Try an example
Double charge
502 error
Export data
Webhook issue
Enable 2FA
Cancel subscription
Review agent decisions. Correct mistakes to improve accuracy.
No tickets yet — submit one in the Customer tab
Agent Response
Waiting for a ticket...
Category
—
Confidence
—
Status
—
Response
—
§ 04 — Features
Built for production,
not demos
F-01
LangGraph Routing
Multi-node state machine with conditional routing. Each specialist has isolated RAG context and toolset.
F-02
RAG over Docs
Answers grounded in your docs via ChromaDB. No hallucinations — only what you define as ground truth.
F-03
Auto-Learning Loop
Every correction feeds back as a few-shot example. No retraining. Just smarter prompts.
F-04
Confidence Scoring
Knows when it doesn't know. Sub-threshold confidence triggers graceful escalation with a pre-written context summary for the human reviewer.
F-05 / F-06
LangSmith + Reviewer Dashboard
Full trace of every decision — chunks retrieved, routing reason, token cost. One-click reviewer dashboard. Deploy in minutes.
§ 05 — Stack
The stack
LangGraph
Agent Orchestration
LangChain
RAG + Prompting
ChromaDB
Vector Store
Claude 4.6
Specialist Agents
LangSmith
Observability
Render
API Deployment
"
The gap between a demo that impresses stakeholders and a system that handles real workload is exactly what LoopDesk is built to close.
LoopDesk — Self-improving AI triage, 2026
Ready to
close
the loop?
Open Source · MIT License
Built with LangChain, LangGraph, ChromaDB, and Claude Sonnet 4.6. Fully open source. Replace our docs with yours and deploy in minutes.