AI Agent Development
Stell Media provides AI agent development for ecommerce and enterprise commerce teams that need more than an impressive demo. We design, build and manage production AI agents for customer support and merchandising. Each one comes with an evaluation harness, clear human escalation and firm rules on what the agent must never do. This service sits within our Commerce Build & AI Automation practice, and every agent connects to your real systems, not a sandbox.
The problem
Why AI Agent Pilots Fail at Evaluation, Not Capability
Most AI agent pilots prove a happy path and then stall. Teams find they cannot measure regression, have no escalation design and have no shared definition of an unacceptable answer.
We build the evaluation set before the agent. Every change afterwards has a score attached, so a rollback decision is never a debate. It is the same discipline we apply in site search and discovery, where new rules are tested against frozen benchmark queries before they reach production.
What You Get with Our AI Agent Development Service
Every engagement ships four concrete deliverables.
Production AI Agent
We wire retrieval, tools and guardrails into your helpdesk, order data and product catalog. The agent answers from your own documents and acts inside the systems your team already uses.
Evaluation Harness
You get a scored test set written before the build and run on every change, with regression gates in deployment. This keeps quality measurable as models and data change.
Human Escalation Design
When the agent is not confident, it hands off to a person with the full conversation context. It also refuses clearly on anything out of scope, so customers never get a confident wrong answer.
Observability and Monitoring
We track transcript review, containment rate and customer satisfaction from day one. You also get a weekly failure readout that shows what to fix next.
How We Build and Run Your AI Agent, Week by Week
Scope and Evals
We define what the agent handles, what it refuses and the scored test set that defines "acceptable."
Build
We integrate retrieval and tools against your systems. Guardrails are tested against adversarial cases, not just friendly ones.
Shadow Mode
The agent drafts and humans send. We measure the agreement rate before the agent is allowed to respond on its own.
Managed Evaluation
We run weekly transcript reviews and eval runs, because model updates can change behaviour without warning.
Stack we work in
Platform-agnostic. We work in whatever you already own and tell you honestly when it is the constraint.
AI Agent Development FAQs
We combine scope refusal, retrieval grounded in your own documents, an adversarial test set in the evaluation harness, and a human escalation path for anything the agent is not confident about.