Execution
Workloads created, run and retired inside isolated per-tenant environments, with scheduling and automation around them.
Infrastructure
AI agent infrastructure,
AI agent infrastructure,
already built
The runtime, identity, credentials, memory, policy and tracing an agent needs in production. Describe what you want operated, and the AI agent infrastructure comes with it.
What agent infrastructure has to cover
A prototype needs a model and a loop. Everything after that is infrastructure, and it is the same list every time: somewhere to run, an identity to act as, credentials to act with, memory to carry, policy to stay inside, and a record of what happened.
Identity and secrets
An authenticated identity on every request, and credentials encrypted per user rather than shared in an environment file.
Coordination
Tools attached over MCP, agents exposed to other agents, and work handed between them without bespoke glue.
Evidence
Structured traces of every tool call and result, so an answer can be checked rather than trusted.
Why teams stop building this themselves
None of it is impossible. It is just that each piece is a project, the pieces have to agree with each other, and none of them is the product you set out to build. AI agent infrastructure is where agent timelines go quietly missing.
It has to be right, not clever
Tenant isolation and credential handling are the kind of thing you either get exactly right or find out about later.
It grows with every tool
Each new integration brings another key, another scope and another way for a run to go wrong.
It is invisible when it works
Nobody demos their secret store. The work is real, and none of it shows up in the product.
Runs where your data already lives
The runtime is self-hostable and works against your own database, so agent infrastructure does not mean handing your data to somebody else. Run it in your VPC, or let us host it.
What arrives on day one
You describe the outcome you want operated. Kodeus drafts the application, assembles a model, suggests the tool servers it needs and scaffolds the skills, then runs it on the infrastructure below. Nothing on this list is a later phase.
| You get | So you never write |
|---|---|
| Isolated per-tenant execution | Container orchestration and cleanup for every customer. |
| Authenticated identity per request | A bespoke service-account scheme nobody can audit later. |
| Encrypted per-user credentials | A secret store, an OAuth callback handler and a revocation path. |
| Session and per-user memory | Schema design, compaction and the isolation tests that go with them. |
| Guardrails at defined seams | Your own interception layer, and the argument about where it belongs. |
| Structured traces | A logging convention, a parser and somewhere to read the result. |
Tools attach over MCP in a single command, so adding a capability does not mean adding another integration project to the roadmap.
See it running on your own workflow
Describe what you want operated and we will show you the infrastructure it arrives with.
Frequently asked questions
What is AI agent infrastructure?
It is everything an agent needs besides the model and the prompt: somewhere isolated to run, an authenticated identity, encrypted credentials, memory, policy enforcement and a trace of what it did. Most teams discover the list only after a prototype works.
Can I self-host the agent infrastructure?
Yes. The Kodeus runtime is self-hostable and runs against a database you own, inside your own VPC. Managed services cover drafting the application, the capability catalog and metering.
How is this different from an agent framework?
A framework helps you compose agent behaviour. Infrastructure is what runs that behaviour safely for real users. Kodeus covers both: it drafts the application and then operates it.