[SYS.01]
Multi-agent systems
Role-based agent teams with sandboxing, triage and human-in-the-loop review.
- Claude
- Codex
- MCP
[AI AGENT ENGINEERING STUDIO // GMT+7]
We build AI agents that actually ship.
Production multi-agent systems, LLM gateways, evals and memory, engineered to run every day, not just in a demo.
// 01 Live demo
Ask Migomogi
model: llama-3.3-70b · workers-ai
Runs on Cloudflare Workers AI. Answers come only from our studio notes.
// 02 Capabilities
Services
[SYS.02]
LLM gateways & routing
One endpoint over many model providers, with fallback, quota-aware routing and cost control.
- MLflow AI Gateway
- OpenAI-compatible APIs
- Cloudflare Workers
[SYS.03]
Evaluation & observability
Tracing, LLM judges and regression evals, so quality is measured rather than assumed.
- MLflow tracing
- LLM judges
- Custom scorers
[SYS.04]
Agent memory & knowledge
Long-term memory and grounded retrieval over your documents.
- Postgres
- pgvector
- Hybrid search
- Rerankers
// 03 Process
How we work
-
Discovery sprint
1–2 weeks to map the workflow, pick the agent boundary and define the evals.
Deliverable: workflow map & eval plan
-
Build in production
Ship behind a gateway, with tracing from day one.
Deliverable: agents running with tracing
-
Hand-over with evals
Your team owns it, with dashboards and a regression suite.
Deliverable: dashboards & regression suite
// 04 Studio
About
Migomogi is an independent AI engineering studio based in Vietnam (GMT+7), working with teams worldwide. We run our own multi-agent stack every day, and we build yours the same way.
We treat agent workflows as production systems: bounded, traced, and evaluated on every change.
- Location
- Vietnam (GMT+7)
- Works with
- Teams worldwide
- Engagements
- Discovery → build → hand-over
- Infrastructure
- Cloudflare · Google Cloud · Postgres
- Philosophy
- Zero demo-ware
// 05 Contact