Everything Your Agents Produce Belongs to You

TL;DR

The outputs of your agentic systems are your IP. Not just the work product. The memory, the traces, the evals. All of it.
The moat in knowledge work is that accumulated layer, not the model. Models are commoditising. What your Digital Workers learn is the only part that compounds.
Default architectures hand that layer to the model vendor. It accumulates on their servers, in their format, with no export path.
The right split: the customer owns the outputs, the vendor provides the infrastructure. Buying an agent should never mean renting the learning.

Rent the labour. Own the learning.

Our Argument:

Every enterprise has now heard the pitch: adopt agents or fall behind. So they pick a frontier model, wire agents into its memory features and stateful APIs, and get to work. It feels like progress. It is actually a quiet transfer of assets.

An agentic system running real work produces far more than the work itself. It produces memory: how your organisation decides, what good looks like in your domain, which exceptions matter. It produces traces: a complete record of how every task was reasoned through and executed. It produces evals: the accumulated definition of quality for your workflows, tested against thousands of real runs. Together these are the moat of knowledge work. The model is not. Models are converging on capability and collapsing on price. The learned layer on top is the only part that compounds, and in the default architecture it compounds on someone else's servers, in someone else's format, with no export path. LangChain warned in April 2026 that closed agent harnesses make memory inseparable from the vendor, and a 2026 Parallels survey found 94% of organisations now worry about vendor lock-in. Data export clauses solve the easy problem. Files are portable. Learned behaviour is not.

We take a clear position on where the line sits. Everything your Digital Workers produce is owned by you: the memory, the traces, the evals, the outputs. We provide the infrastructure that generates it, runs it, and keeps it reliable. You store it wherever you like. Switch LLMs tomorrow and your Digital Workers keep everything they have learned. Fire us and you keep all of it. Ownership that evaporates when the contract ends was never ownership.

Two objections come up in every serious buying conversation. Both deserve straight answers.

First: "We need to build our own orchestration infrastructure and raise the floor of our internal talent before outsourcing anything." Infrastructure is not the asset. Orchestration frameworks are commoditising as fast as models. The asset is what running real work produces: the memory, traces, and evals accumulating in a store you own. A quarter spent on plumbing is a quarter of that asset not accumulated. Vendor-led deployments succeed at roughly twice the rate of pure internal builds, and when you own every output, none of that learning leaks.

Second: "Evals and continuous learning from traces are our IP, so we won't rely on off the shelf agents." The premise is exactly right. That is why the split matters. Build everything or rent everything is a false choice. When every trace and every eval lands in your infrastructure, buying the agent does not mean renting the learning. It all stays yours, portable across any model and any vendor. Including us.

Rent the labour. Own the learning.