AI Agent Development

An AI agent is different from a chatbot in a specific, load-bearing way: a chatbot responds, an agent acts. Give an agent access to your CRM, your ticketing system, or your codebase, and it can look things up, take multi-step actions, and keep going until the task is actually done — not just produce a paragraph of text about what someone else should do next. That distinction is where most of the real business value sits, and it's also where most of the engineering risk sits.

Single agent or multi-agent — and why it matters for cost

A single-agent workflow handles one well-defined task with a fixed set of tools — a support-ticket resolution agent, an invoice-processing agent. It's the right starting point for most teams: faster to build, easier to evaluate, and easier to trust in production because there's one thing to get right. A multi-agent system splits a complex workflow across specialised agents that hand off to each other — a research agent, a writer agent, a verification agent — which is powerful for genuinely multi-stage work but multiplies the surface area for something to go wrong. We default to single-agent unless the workflow actually has distinct stages that benefit from different tools or prompting strategies.

Why guardrails aren't optional

An agent that can take actions is an agent that can take the wrong action, at scale, unattended. Every production agent we ship has input validation, output filtering, rate limiting, and audit logging built in from the first sprint — not added after an incident. For anything touching customer-facing decisions or financial data, we add human-in-the-loop checkpoints so the agent proposes and a person approves, until the failure modes are well enough understood to remove the checkpoint.

Ready to scope your agent? Get a free AI assessment → Tell us the workflow you want to automate; we'll tell you honestly whether it's a 4-week single-agent build or a bigger multi-agent system.