AI Agents Integration
An agent is software that decides its next step instead of following a fixed path — and that autonomy is exactly what makes it risky in a business process. The engineering that matters is not the prompt. It is what the agent is permitted to do, what happens when it is wrong, and whether anyone can reconstruct why it acted as it did. Zelpex builds agents with those boundaries defined first.
We build agents that work inside real systems: reading from your ERP or catalog, drafting and routing rather than sending, running multi-step research or reconciliation, escalating when confidence drops. We favour tightly scoped tool access, human approval on anything consequential, and a full audit trail of decisions.
- Tool access scoped to specific, auditable actions on your systems
- Human approval gates on anything that moves money or contacts a customer
- Full decision logs — inputs, tools called, output — for every run
- Evaluation harnesses so a prompt or model change is a measured change
Define the blast radius
Before any build, we agree what the agent may touch and what it may never touch. An agent with write access to production data and no approval gate is a liability regardless of how well it performs in testing.
Start read-only
First versions read, summarise and draft; a person approves the action. Autonomy is extended only where the log shows the agent is consistently right, and revoked where it is not.
Make every run reconstructible
Each decision is logged with the inputs, tools called and output produced, so an incorrect action can be explained afterwards rather than shrugged at.
Test changes like code
Prompts, tools and models run against an evaluation set before release, because a model upgrade can silently change behaviour in ways no manual spot-check catches.
What teams ask us about AI agents
No. A chatbot answers; an agent acts — calls your API, updates a record, triggers a workflow. That difference is the entire risk profile. A wrong answer is a bad experience, a wrong action is a business incident, which is why we design permissions and approval gates before capability.
Anything where being wrong is expensive and hard to reverse without human review: issuing refunds, changing prices, sending customer communications unsupervised, or making decisions with regulatory weight. Agents are strong at gathering, drafting and routing; the final commit should stay with a person.
An evaluation set of realistic tasks with expected outcomes, run on every prompt, tool or model change. Without it you cannot tell whether a new model version improved things or quietly degraded a path you rely on, and you will find out from a customer.
Our stance on agents
Useful where the work is gathering, drafting and routing. Dangerous where it is deciding without oversight.
We build agents as ordinary production software: permissions, logging, tests, monitoring and rollback. The interesting questions are governance ones — what it may do, who approves, how you find out it was wrong — and those are worth settling before the first line of code.
Thinking about a project like this?
Tell us what you are building and what is getting in the way. You will get an honest read on scope, approach, and whether we are the right team for it — including when the answer is that you do not need us.
Services that pair well with AI agents
Adobe Commerce Integration
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Shopify Commerce Development
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