What's the difference between an AI agent and a chatbot?

A chatbot answers questions inside a chat window. An agent reasons through a multi-step task and takes action inside your systems, like generating a report, updating a record, or routing a request, not just answering a question about it.

How do you test an agent before it touches production data?

We run it against real systems in a controlled environment first: edge cases, malformed inputs, and permission boundaries, along with the failure modes that don't show up in a clean demo. It doesn't reach production data until it's handled all of them predictably.

How long does it take to build and integrate an AI agent?

It depends on how many systems the agent needs to touch and how much judgment the task requires. A well-scoped agent usually goes from design to tested production in four to eight weeks. Legacy systems or unusual permission structures add time.

What happens if the agent runs into something it wasn't built to handle?

It stops and hands off to a person instead of guessing. That's what the human checkpoints are for. An agent that improvises at the edge of its scope is the failure mode we're built to prevent.

Do we need an AI strategy session before building an agent?

Not always. If the use case is well defined and clearly needs an agent rather than simpler automation, we can move straight to design. A strategy session earns its keep most when more than one use case is competing for the same budget.

What's included in an AI Agent Feasibility Assessment?

A short working session where an engineer looks at your use case, the systems it would need to touch, and whether an agent is actually the right build before you commit to a full one.

AI Agents

Agents that are tested
before they touch your data

We design, build, and integrate AI agents into your existing systems, governed and tested before go-live, so automation doesn't mean losing visibility into what's actually happening.

Get an AI Agent Feasibility Assessment See Our Agent Governance Framework

THE PROBLEM

Why Do Most AI Agent Pilots Die Before Production?

Because a demo that works on stage and an agent that's safe to run against your real systems are two different builds, and most pilots only finish the first one.

It's easy to get an agent to look impressive in a sandbox: clean data, a scripted question, a demo environment nobody's counting on. Production is different. Your CRM has duplicate records. Your ticketing system has edge cases nobody documented. And an agent that reasons its way to the wrong action inside a live system doesn't fail quietly. It fails inside your business.

That's the gap between a proof of concept and something you can actually run. Closing it is the job.

WHAT IS AN AI AGENT

What Is an AI Agent, and How Is It Different From a Chatbot?

A chatbot answers questions. An AI agent reasons through a multi-step task, decides what to do next toward a goal, and takes action inside your systems, not just inside a chat window. Building one that's safe to run comes down to four pieces:

Reasoning and planning

The agent works out the sequence of steps a goal requires, instead of responding to one prompt at a time with no memory of what came before.

System integration

It connects to the tools it actually needs, your CRM, your data lake, your ticketing system, whatever the task calls for, not a mocked-up version of them.

Guardrails

Defined limits on what the agent can see, touch, and do, set and tested before it goes anywhere near production.

Human checkpoints

Clear points where a person reviews or approves before the agent acts, wherever the stakes call for one.

HOW WE BUILD

How Does Netrix Build and Integrate an AI Agent?

Three phases, in this order every time.

Design

We scope the task with the people who'll actually rely on the agent. Then we map every system it needs to touch before a line of code gets written.

Build and test

We build against your real systems, not a sandbox, and run the agent through edge cases and failure modes before it's anywhere near production data.

Govern and run

Permission boundaries and human checkpoints get set before launch. After that, our AI Operations team keeps watching it, because an agent that was safe on day one can drift by month three.

AGENT OR AUTOMATION

How Is This Different From Workflow Automation?

Fair question, since the line blurs.

AI Agent

Reasons through steps that aren't fully scripted and decides what to do next toward a goal.

Workflow Automation

Handles a process that's already defined: rules, sequence, and a clear end state, done automatically. See Workflow & Automation.

If the work genuinely needs judgment at every step, an agent is the right build. If the process is well understood and mostly repeats itself, workflow automation is usually faster to build and cheaper to run. Plenty of clients start with one and add the other once they know exactly where the judgment is actually needed.

IN PRODUCTION

What Does This Look Like Once It's Running?

Pharmaceutical Company Case Study

We've worked with one pharmaceutical company for two years, since deploying the data lake this agent now runs on. Its field sales team needed answers from company data without waiting on a dashboard or knowing SQL.

So we built a generative AI agent, delivered inside Microsoft Teams. Reps can ask something like 'which doctor prescribed the most of a given product in the last three months' in plain language and get a real answer back. Under the hood, it's a modular set of agents that handle query interpretation, generation, and execution against the company's AWS data lake, not one agent doing everything.

Reps got real-time answers without technical training, and the tool saw broad adoption across commercial and executive teams. Read the full pharmaceutical AI agent case study ›

YEARS RUNNING IN PRODUCTION

Teams

DELIVERED INSIDE MICROSOFT TEAMS

ROADMAP FIT

Where Does This Fit in an AI Roadmap?

Usually second, right after you know which use case is worth building.

Strategy and prioritization tells you which use case needs an agent instead of simpler automation. This is where it gets built and tested against your real systems. From there, user adoption and training makes sure your team actually uses it, and a Fractional Chief AI Officer can own the roadmap long term if AI turns into an ongoing practice instead of a project.

QUESTIONS WE GET FIRST

Before an Agent Touches Anything Real

READY WHEN YOU ARE

Find Out If Your Use Case Is Ready for an Agent.

Thirty minutes with an engineer to look at the use case, the systems it touches, and whether an agent is actually the right build.

Get an AI Agent Feasibility Assessment

No sales pitch. Just an engineer looking at whether an agent makes sense for what you're trying to do, and telling you honestly if it isn't.

Agents that are testedbefore they touch your data