We build agentic AI enterprises can trust in production.
From prototype to production: agent and multi-agent graph orchestration on commercial and open-source frontier models, deployed on your cloud with the security and governance enterprise AI actually demands.
Design, build, evaluate, operate.
Five capabilities, run as one practice. Every system we design is one we expect to evaluate, secure, and keep running.
Agentic systems
Single- and multi-agent systems that do real work: orchestration, tool use, memory, and human-in-the-loop control. Built on open interoperability standards, designed for auditability from the first line.
Orchestration · Tool use · A2A / MCP
LLM & GenAI applications
Retrieval, copilots, and document intelligence on commercial and open-source frontier models. Model-agnostic by design, so the system outlives any single vendor.
RAG · Copilots · Document intelligence
Evals, safety & security
Evaluation harnesses, red-teaming, and guardrails, grounded in our own published research on agent security.
Eval harnesses · Red-teaming · Guardrails
Production deployment
Deployed on your cloud with observability, cost and latency engineering, and the governance your security review will ask for.
Your cloud · Observability · Governance
AI strategy
Roadmaps, build-versus-buy decisions, and readiness assessments from people who ship. Advice measured by the outcome it unlocks.
Roadmap · Build vs. buy · Readiness
Depth you can verify.
Our practice is grounded in work anyone can check: research on arXiv, running open-source code, and open contributions to the protocols agents run on.
From Privacy to Workflow Integrity
Communication-graph metadata in agent interoperability protocols leaks workflow structure: a classifier recovers task class from metadata alone at roughly six times chance. The class of risk we design client systems against.
Read on arXivopenclaw-simplex
End-to-end encrypted agent-to-agent messaging with allowlists, pairing approval, and a documented security review. The same patterns we apply to securing agent traffic in client systems.
View on GitHubMetadata-private binding for A2A
A proposed A2A binding that hides who talks to whom, shipped with a spec and reference implementation. Standards work that keeps client systems ahead of where the protocols are going.
Read the proposalFluent across the stack.
No exclusive allegiances. We build with the strongest tool for the job, deploy in your environment, and contribute to the open protocols agents run on.
Trust is an engineering discipline.
mltrix is an AI engineering practice in Austin, Texas, built on a conviction: enterprise AI becomes dependable when it is engineered against the failure modes research keeps finding.
Evidence over claims
What we can prove is one click away. What we cannot prove, we do not say.
Security by design
Agent systems touch real credentials, real data, and real workflows. We architect for the failure modes we study in our own research.
Production is the bar
A demo is not a deliverable. Systems ship when they survive security review, load, and the handoff to your team.
Ready when you are.
A 30-minute discovery call: your context, our read on what would actually work, and a straight answer on whether we should work together.