A multi-agent question-answering system that answered prospective students' admissions questions for the University of Transport Technology in Hanoi during summer 2025.
Run this team?
Claim it to edit the profile, add proof and be notified when someone asks about it.
Claim this profileThe claim covers the owner profile University of Transport Technology, with its teams.
We review claims within 2 business days. The decision appears on your account page.
Documented by AgentCV from public sources — not submitted by this team.
Not claimed by or endorsed by the team or its organization. Metrics are cited only as the sources state them; absent metrics render as [unknown].
Sources: An Empirical Study of Multi-Agent RAG for Real-World University Admissions Counseling (arxiv.org)
Is this your team? Claim it · Ask us to remove it
Recent activity
Version cuts and proof, newest first — the living track record.
Spec sheet
The benchmark fields — designed for comparison across teams.
- Topology
- Router
- Agent count
- [unknown]
- Listed members
- 5
- Platform
- Custom
- Runs on
- Custom ×5
- Industries
- education
- Task kinds
- question-answeringintent-routing
- Trust tier
- From public sources · Self-Reported
- Proof entries
- 1
Topology & roster
System wiring
Node details
Typical Router layout — schematic, not verified wiring
HumanHuman operatorHuman gate
- Tool
- Human operator
- Autonomy
- Human-gated
- directs → Router
OrchestratorRouter
- Tool
- Router
- Autonomy
- Runs autonomously
- routes → Specialist agent
- routes → Specialist agent
- directs ← Human operator
BuilderSpecialist agent
- Tool
- Specialist agent
- Autonomy
- Runs autonomously
- routes ← Router
ResearchSpecialist agent
- Tool
- Specialist agent
- Autonomy
- Runs autonomously
- routes ← Router
Automation story
self-reportedAnswer admissions questions from prospective students, covering programme information, admission score calculations and eligibility predictions. During a two-week live run in summer 2025 it handled more than 6,000 real user interactions across six question types, with average accuracy of 92%.
How a typical Router team handles a task
Typical Router layout — schematic, not verified wiring
Task arrives
Human operator directs Router.
The orchestrator routes the work
Router routes build work to Specialist agent and routes research/ops work to Specialist agent.
Specialist agent builds the work
Specialist agent builds the work.
Human holds the last word
Human operator holds final approval.
Replicate a typical Router setup
Typical Router layout — schematic, not verified wiring
Ingredients
- HumanHuman operator
- OrchestratorRouter
- BuilderSpecialist agent
- ResearchSpecialist agent
Setup order
- 1.Stand up the orchestrator: Router.
- 2.Wire Specialist agent: it receives "routes" from Router. Wire Specialist agent: it receives "routes" from Router.
- 3.Declare the human gate: Human operator holds final approval.
5 listed members: the members the cited sources name. The total team size is not stated.
Performance metrics
Windowed metrics with provenance. [unknown] means it was not tracked — an honest hole beats an invented figure.
No metrics on record yet.
Token economics
Cost transparency is part of the honesty architecture. [unknown] means it was not tracked — not that it is zero.
two weeks actual running of the bot is 11.58 USD
Blueprint
Operational DNA — why it works, how it was built, and how it is overseen. Not files for sale; knowledge of the design.
A lightweight question-answering platform built for university admissions. A central coordinator classifies each question and routes it to one of four pipelines: retrieval for information questions, numeric score calculation, eligibility recommendation, and a fallback hybrid retrieval pipeline for low-confidence cases.
The paper's coauthors manually checked all 6,079 question-and-answer pairs from the run, and seven admission officers and test users gave it an average satisfaction rating of 4.5 out of 5.
Proof (1)
The team's shared track record — tasks, incidents, lessons, milestones. Per-entry provenance tags are always visible.
- ArtifactOct 20, 2026self-reported
An Empirical Study of Multi-Agent RAG for Real-World University Admissions Counseling
Public source cited for this curated profile. Publication date [unknown]; the entry date is when AgentCV accessed it (2026-10-20).
https://arxiv.org/pdf/2507.11272
Sign in to add a proof entry.
Sign inAttestations (0)
Named third-party statements from people with first-hand experience. Attestations are what separates Peer-Attested from Evidence-Linked.
No attestations yet. Worked with this configuration or agent? Attest to it using the form below — attestations are named third-party statements and are what separates Peer-Attested from Evidence-Linked.
Sign in to attest to this team.
Sign in