AI agents, built and managed
Your AI agent worked in the demo. Do you know what it does today?
We build AI agents for businesses and then run them like production software: a written job, tested before release, watched in production and changed with a record. For IT and operations leads whose agent or AI automation became part of daily work without an owner.
Taking first conversations. Scope, hosting and support are agreed in writing per agent; no prices are published.
Where agents go wrong
The failure is quiet, and nobody owns it.
In production
It fails without an error
No crash, just a plausible wrong answer, found days later by a customer or a colleague.
After launch
Nobody owns it
An agency, a freelancer or a colleague built it. The documentation, the tests and the person who understood it are gone.
On every change
Each update is a gamble
A prompt edit, a new model version or a changed API alters what it does, and nothing tells you.
None of this is your team’s fault. Most agents are built as demos, not as software.
So the fix must be a smarter model, or a better prompt.
It is not.
The part most agents skip
An agent is software. It needs the same foundation.
A written goal, contracts between its parts, tests, review, release discipline, documentation and an owner. Fixed steps belong in ordinary code; the model is used only where a step needs judgment, and that judgment is evaluated separately, because ordinary tests cannot check it.
That foundation is how we build every agent, from the first conversation to its hundredth change.
One lifecycle
Build, deploy, monitor, update, learn. Each step leaves a record.
01
Build
Goal first: the task, the success criteria and the acceptance checks are agreed before anything is built, including whether the task needs an agent at all. Code wherever code works, the model only where judgment is needed, and a written contract for every tool and sub-agent.
02
Deploy
Released like software: tests for the code, evaluations on real cases for the model’s judgment, review by someone who did not build it, and a way back if the release goes wrong.
03
Monitor
Watched in production: failures made visible, cost and usage tracked, and a clear point where the agent stops and hands over to a person instead of guessing.
04
Update
Changed with discipline: every prompt, model or tool change is specified, reviewed, run against the same evaluations and recorded before it reaches your users.
05
Learn
Improved from production: what goes wrong becomes a new test or evaluation case, so the same failure is caught before the next release.
Five steps, one record. Here is what you can hold us to.
What you can hold us to
Commitments about how we work, not promises about results.
A written job
What the agent does, how success is measured and what it must never do, agreed before we build.
Tests you can read
Tests for the code and evaluations for the model, rerun on every change, with results you can open.
A record of every change
Who changed what, why, what was checked and what is still open.
Terms in writing
Scope, hosting, support, ownership and fees agreed in writing before work starts.
Whether it fits your agent is a fair question, so here is the honest answer.
Who it is for
Built for agents your business relies on. Honest about the rest.
This is for you if
- You run an agent or AI automation that daily work now depends on.
- It was built by an agency, a freelancer or a colleague, and nobody owns it now.
- You want a new agent for a real task, with success criteria you can check.
- You want to know when it fails before your customers do.
This is not for you if
- One prompt or a simple chatbot that a no-code tool already handles well.
- Every step is fixed. Ordinary software or automation is cheaper and safer: Build with us.
- You need a hosted product with a public price today, or guaranteed accuracy or uptime.
- High-risk decisions under the EU AI Act without a compliance owner on your side. We are not legal advisers.
- Nobody on your side will define success or own a human approval point.
Every agent is built on the same framework. Here it is, counted.
Key facts
The discipline underneath, counted in our own framework
6
stages every code change travels in the framework we build with, each in its own isolated step: planning, specification, development, review, checks and documentation
paqad-ai 1.95.0 source, checked 6 October 2026
5
mutation-testing tools wired into that framework (Stryker, PIT, mutmut, Infection, mutant), so a test that cannot catch a broken line is found
paqad-ai 1.95.0 source, checked 6 October 2026
1
evidence packet per change: the request, the frozen specification, every stage and its receipt
paqad-ai 1.95.0 source, checked 6 October 2026
Our own framework, our own record. Agents, plainly.
AI agents, in short
Eliyce builds AI agents for businesses and runs them like production software across their whole life: build, deploy, monitor, update and learn. Every agent starts from a written goal, uses ordinary code wherever code works, is tested and evaluated before release, and changes only with review and a record. Scope, hosting and support are agreed in writing per agent.
Our services
Two more ways we make AI work you can check.
Questions
What people ask before they trust an agent
Start from the task. If every step can be written as a rule, ordinary code or a simple automation is cheaper and safer. An agent earns its place only where a step needs judgment, and even then the fixed steps around it stay in code. We decide this with you before anything is built.
Two kinds of checks. Tests for the code: the right tool called with the right arguments, inputs and outputs that match their contracts, failures handled. Evaluations for the model’s judgment: real cases with expected outcomes, rerun whenever a prompt or model changes. Release waits until both meet the criteria agreed up front.
Rerun the same evaluation set on every prompt, model or provider change before it reaches users, and watch production for drift: answers changing, more hand-offs to people, rising cost. A change that lowers the evaluation results does not ship.
Someone has to own it: watch it, update it when tools, data or models change, and turn failures into new tests. We agree in writing who takes that role, us or your team, and what they need to do it.
Tell us about it. We read what exists, from code and prompts to connected systems, credentials and any tests, and tell you plainly what it would take to put it under control, whether or not you work with us.
It can when the basics are in place: a lawful basis, a data processing agreement with every provider involved, only the data the task needs, and access limited to the agent’s job. We design for those from the start, but we are not legal advisers; your privacy owner decides.
It depends on the task, the volume and the models used, so we do not publish prices. Running costs are estimated from tests on realistic cases and kept in check with budgets, caps and alerts. Scope and fees are agreed in writing before work starts.
Last reviewed 13 October 2026 by Eliyce.
Know what your agent does. Every day.
Twenty minutes about the agent you run or the one you want built. You leave with an honest view of what it needs to be under control, whether or not you work with us.
We reply within one working day. Or write to hello@eliyce.com.