Engineering harnesses that turn non-deterministic models into verifiable, production-ready workflows.
Built on open standards (MCP, Agent Skills). Governed by deterministic computational checks, not by prompting alone.
Aivio Labs
We are an AI agent engineering studio. We build the harnesses, skills, and pipelines that make model output verifiable enough to put into production.
We operate globally in English. Part of our work involves domain knowledge that no general model holds, because it was never in the training data in a form a machine could use.
Four waves, and what we built from each
The field has moved in waves. Each one arrived as a research result or a specification, and each one took a year or two to become something a business could rely on. We have worked through four of them.
Agent loops and orchestration
Interleaving reasoning with action, then coordinating several agents through a conversation
Orchestration harnesses that dispatch work across sessions and machines, including one that routes a request through planning, delegation, and acceptance
Memory
Managing context in tiers so an agent could carry state past a single conversation
A retrieval layer over our own corpus — keyword, vector, and hypothetical-document search against one index — because generic retrieval did not answer the questions we were actually asking
Skills
A portable folder format for giving an agent procedural knowledge, opened as a cross-vendor standard
Our own library of skills rather than installed ones, running across three agent runtimes from a single directory, and a document-to-deliverable pipeline built on top of them
Harnesses
Naming what surrounds the model — the guides that steer it and the sensors that catch it
A catalogue of every harness and skill we run, with provenance and the evidence each can carry, and the productised version of that pattern
Each wave left a capability, and each capability became something we offer. The skills work became Role Packs. The orchestration and harness work became Engineering Harnesses. Constraining a model against an authoritative source — the problem the memory wave opened and the harness wave closed — is what Personal Tutor exists to prove.
Evidence, not description
Every harness we publish carries up to four things: the artifact it produced, whether you can run it yourself, the trace of how it was made, and the checks that run against the output. Entries missing one show the gap rather than filling it with prose.
Most of our work is built under client confidentiality and is never published. What we can show is the architecture: how a pipeline is bounded, what it validates before it acts, and what it refuses to do by default. The engineering is legible without the contents being exposed.
That is the standard we hold ourselves to — inputs, steps, checks, dates.
Get in touch
Products are shown here but not sold here — engagements currently start with a conversation.
If you are evaluating whether we can build something specific, send the problem rather than a request for a call. We will tell you whether it is something we can do, and if it is not, who we think can.
Every inquiry gets a reply from a person who read it.