Execution
Workloads created, run and retired inside isolated per-tenant environments, with scheduling and automation around them.
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.
What you stop building when AI agent infrastructure is already there
AI agent infrastructure is the work that shows up the week after a prototype impresses someone. Somewhere isolated to run. An identity on the request. A vault for credentials. Memory that does not leak across tenants. A check that can refuse a tool call. A trace you can query. Each of those is a small platform. None of them is the product you described at the start, and they have to agree with each other or the seams become the bugs.
Kodeus drafts the application and runs it on that infrastructure, so you are not staffing a second team to host an agent you wrote by hand. You describe the outcome. The spec, whether you edit it in the Console or as kodeus.yaml, carries the model, the tools, the skills, the memory, the policy and the limits. Tools attach over MCP. Which servers are worth connecting is a separate judgment, covered in best MCP servers. The runtime still sees the call and can refuse it.
Where it runs is a choice of boundary, not a rewrite. Local, your VPC, Kodeus Cloud, or airgapped, with the same policy engine. The sequence is how to deploy AI agents in production. What "ready" means before the first real user is production ready AI agents. Policy detail is AI agent governance, and the record of each call is AI agent observability.
Two limits, so the phrase AI agent infrastructure does not get asked to mean something it is not. Kodeus is not a place to upload a LangGraph or CrewAI project and have it hosted. And the infrastructure does not make the model wise. It makes a bad call visible, attributable, and, when you configured it that way, impossible to complete. That is the difference between a demo and a system your own reviewer will sign.
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.
What arrives on day one?
Isolated per-tenant execution, an identity per request, encrypted per-user credentials, session and per-user memory, guardrails at defined seams, and structured traces. None of that is a later phase.
How do tools attach?
Over MCP, in one command. Choosing which servers deserve a connection is covered under best MCP servers. The runtime still applies policy before the call.
Can I run it airgapped?
Yes. Airgapped means no outbound dependency, with models hosted inside the perimeter. The policy engine is the same one you run locally.
What should I read if I am deploying this month?
How to deploy AI agents in production is the sequence. This page is the list of what the infrastructure has to cover. Enterprise is the control catalogue and the four deployment boundaries.
Do I bring an agent I already built?
No. You describe the outcome. Kodeus drafts the application and runs it on this infrastructure. It is not a hosting layer for a framework you already finished.