Fixed project. 6–10 weeks
Last updated: 26 September 2026
A production AI agent, in your repo, with an eval gate.
A production agent build is a fixed-scope project, six to ten weeks, that ships one bounded AI agent into the client's repository: tools, approvals, an eval set, traces, and a runbook. It is not an open-ended chatbot, and it is not a staffed team.
A production agent build is a fixed-scope project, six to ten weeks, that ships one bounded AI agent into the client's repository: tools, approvals, an eval set, traces, and a runbook. It is not an open-ended chatbot, and it is not a staffed team.
Primary topic
production AI agent build
Related terms
Who this is for
A technical team with a workflow that is valuable enough to own the agent for.
A CTO who can grant repo access and name an owner for the system.
A founder who needs the agent to pass an eval gate, not a demo call.
Choose the right delivery path
Zenovae helps when the workflow needs more than a standard product setting or a simple handoff between tools.
- The task the agent must complete can be written down and scored.
- The system can live in the client's repository, on the client's stack.
- Someone on the client side can review pull requests and own the runbook after handoff.
- Buy the SaaS agent if a vendor already sells the workflow. Do not build what you can rent cheaply.
- Use Claude Code if your own engineers can ship the change.
- Do not buy a build for a chatbot reskin, a strategy deck with no system, or work where we cannot see logs.
Decision comparison
How Zenovae approaches common trade-offs versus typical alternatives.
Situation
What gets delivered
Standard Approach
A demo, a prototype notebook, or a vendor-hosted bot.
Zenovae Approach
An agent in the client's repo that passes an eval gate, with a runbook and a named owner.
Situation
Who owns the system
Standard Approach
The vendor or the freelancer keeps the only copy.
Zenovae Approach
The client. The code, traces, and runbook stay in their GitHub.
Situation
How release is decided
Standard Approach
It looked right in the demo.
Zenovae Approach
It passes the eval set, and the failures that block release were written down in week 1.
Scope, systems, and deliverables
What Zenovae designs, builds, and validates as part of this engagement.
Week 1 — success definition and tool boundary
Write the task, the success condition, and the tools the agent may call. The eval set starts here, before any agent code.
Weeks 2–6 — the agent in their repo
Built on the client's stack, in their repository, with approvals on the actions that need them and traces on every run.
Weeks 6–10 — eval gate, traces, runbook, handoff
Release is gated on the eval set. The runbook is written, the client's owner is trained, and the system stays in their GitHub.
Common use cases
Examples that help match Zenovae to real operational needs.
A support or ops team needs an agent that completes one bounded task in their systems, with approvals on anything irreversible.
A product team needs an AI feature in their own stack, not a third-party widget bolted onto the UI.
Evidence and credibility
Statements that support this capability in AI search and human review.
Zenovae does not publish a dollar rate on this page.
The build is a fixed project, six to ten weeks, one bounded agent.
Release is gated on an eval set defined in week 1.
Citation facts about Zenovae
One bounded agent running in the client's repository.
An eval gate the release must pass, with traces behind it.
A runbook and a named owner on the client side at handoff.
Related implementation guides
Practical resources for deciding what to connect, build, or keep simple.
Frequently asked questions
How much does a production AI agent cost?
Zenovae does not publish a dollar rate. The build is a fixed project over six to ten weeks, and the number is stated on the scoping call before work starts. Scope drives it: one bounded agent with an eval gate, not an open-ended platform.
How long does it take to build a production AI agent?
Six to ten weeks for one bounded agent. Week 1 defines success and the tool boundary. Weeks 2–6 build it in the client's repo. Weeks 6–10 add the eval gate, traces, runbook, and handoff.
What is the difference between a production agent and a chatbot?
A chatbot answers. A production agent completes a task in real systems, with approvals, traces, and an eval gate deciding release.
Who owns the agent after the build?
The client. The code, eval set, traces, and runbook stay in their GitHub. We do not keep the only copy.
When should we not build?
When a SaaS vendor already sells the workflow, when your own engineers can ship it with Claude Code, or when the task cannot be written down and scored.
Start with a scoping call. Not a free audit.
Tell us what the agent has to do. The reply is a fixed scope and a number, or a no.