Multi-agent systems, wired to actually ship.
Loop Science designs multi-agent workflows and wires large language models into the systems you already run — not a rip-and-replace, a practice built around your existing stack.
Areas of expertise
Agentic systems, built on what you already run
Four disciplines, shown in the order they actually happen in a workflow.
Agentic workflow design
Orchestration across specialized agents — handoffs, shared state, and what happens when one of them fails.
MCP integration into existing systems
Wiring your internal tools and data into LLM workflows through scoped, auditable interfaces — without rebuilding them.
MLOps & training infrastructure
Pipelines, experiment tracking, and the human-in-the-loop labeling that decides whether your eval set is actually measuring anything.
Evaluation & guardrails
Knowing when an agent is right, and catching it fast when it isn't — before your customers do.
Selected engagements
Work delivered across cloud, platform & engineering teams
A sample of past engagements, spanning technical leadership to consulting and engineering.
DevOps / Engineering / CTO · US
DevOps / Engineering · US
DevOps / Engineering · US
DevOps / Engineering · US
DevOps · Australia
Consulting / CTO · US
DevOps · Spain
DevOps / Engineering · US
DevOps / Engineering · US
Engineering · US
Consulting / Project Management · Australia
Consulting / Project Management · Australia
DevOps / Engineering · US
DevOps · US
DevOps / Engineering · US
DevOps / Engineering · US
Consulting · US
DevOps · US
Engineering · US
Hextrap
Platform Eng / AI Tooling · US
Expiring.at
Platform Eng / AI Tooling · US
Consulting · US
A tangential practice
LLMs amplifying how we build software
Separate from building agentic products for your organization — the same discipline changes how we (and your team) build everything else, day to day.
AI-assisted code review
A second, tireless reviewer on every change — still with a human signing off before it ships.
Test & doc generation
Coverage and documentation that actually keep pace with the code, instead of trailing it by a quarter.
Faster, not looser
Speed from the tooling, not from skipping the review step it's supposed to strengthen.
How we work
Scoped pilot first. No platform rewrite.
Assess
Where would an agent actually help, and where would it just add risk? We start with a scoped pilot against a real workflow, not a platform build-out.
Build
Multi-agent workflows and MCP integrations, built against your real systems and data — with evals from day one, not bolted on after.
Operate
Guardrails, cost controls, and monitoring so agentic workflows stay reliable as usage grows — billed simply through your Loop Science account.
Scoped by design
Agents that earn the access they're given
Every tool an agent can call is scoped explicitly: a deploy_service call requires human approval before it executes, a database read might be permitted while a write sits behind review, and every action gets logged the same way a person's would. That's what makes an agent auditable after the fact and constrained before the fact — real guardrails, not a policy document nobody checks against the running system.
// mcp-server.json (scoped tool access)
{
"name": "deploy-server",
"tools": [
{ "name": "deploy_service", "approval": "required" }
],
"scopes": ["read:metrics", "write:deploy"]
}
Have a workflow worth automating?
Existing client? Log in to your account. New engagement? Tell us about it and grab time on the calendar.