Build an AI Agent That Actually Runs in Production

A chatbot responds to a message. A workflow automation follows a fixed sequence of steps. An agent decides what to do and which tools to use based on the situation in front of it. For a predictable, repeatable process, a workflow automation is simpler and more reliable. When the right path depends on the situation, an agent can handle the decisions and tool calls that a fixed workflow can't.

Look for a team that can show you agents running in real production environments, explain how they handle integrations and permissions, and give you a clear approach to monitoring, guardrails, and human oversight. Client examples and measurable results are more useful than a review score alone.

It depends on the complexity of the workflow and how deep the integration needs to go. A single-workflow agent costs less than a multi-agent system with integrations across several tools. Book a scoping call and we'll give you a real figure for your specific case.

Most single-workflow agents go from kickoff to production in 6 to 10 weeks of engineering time, assuming access to the relevant systems and data already exists. Provisioning that access usually takes four to eight weeks on its own at an enterprise. We name what we need, environment, test data, a named SME, sign-off authority, as preconditions in week one before the build starts.

Agents drift as your data and workflows change and, just as often, as the model underneath them changes. We pin model versions instead of floating on latest, and every upgrade runs against a retained regression suite built from your own eval set before it ships, so a silent provider deprecation doesn't become a silent behavior change in production. We can stay on for monitoring and tuning, or hand your team the documentation, eval set, and access it needs to manage it internally.

No. Every opportunity gets a build-vs-buy-vs-partner You do. The code, the architecture, and the prompts all belong to your company under the engagement agreement. Where we use an open-weight model, any license obligations attached to those weights transfer to you too, we tell you what they are before you sign off on its own merits. If an off-the-shelf tool solves it, we'll tell you.

No. We configure data handling according to your requirements, using appropriate API retention settings or deploying within your own isolated environment where required.

Yes. We build around what you already run, your CRM, ERP, or internal tools, through secure APIs and defined permissions, and the exact setup depends on your current infrastructure.

Agentic AI describes AI systems that decide their own next steps rather than following a fixed script, planning, calling tools, and adapting based on what they find. It's the broader category our agents sit inside; the practical difference for your business is whether that decision-making is production-ready or just a demo.

An AI agent is a system built on a language model that can use tools, hold state across steps, and take action inside your real systems instead of just replying with text. Unlike a chatbot, it decides what to do next based on the situation, not a fixed script.

A chatbot handles text. An AI voice agent handles live phone conversations, authenticating the caller, capturing intent, and pulling data from your systems in real time, with the same permissions, guardrails, and handoff logic as our chat-based agents. It replaces IVR menus and phone scripts, not just website chat windows.