AI agent development

AI Agent Development

Agents we build

An agent differs from a chatbot in one word: actions. Our agents look up orders, update CRM records, draft and send documents, and schedule follow-ups — with tool calling, permission boundaries, and human approval gates where the cost of a mistake demands them. This is the exact architecture behind our own AI Agents Suite, which runs in production for paying customers.

• Support agents that resolve routine tickets end-to-end and escalate with full context
• Sales agents that qualify, enrich, and route inbound leads before a human touches them
• Internal knowledge agents that answer team questions from your docs, wiki, and systems
• Operations agents that watch queues, chase missing data, and file the paperwork

Every agent engagement starts by mapping the workflow as it actually happens — including the exceptions your team handles from memory — then deciding which steps the agent owns, which it drafts for human approval, and which it never touches. That division, not the model choice, determines whether an agent saves hours or creates incidents. We've shipped agents for customer support, lead qualification, travel booking workflows, and back-office document processing.

The production discipline is non-negotiable: every tool the agent can call is explicitly scoped and logged, evaluation suites replay real historical cases before each release, and dashboards show what the agent did, what it escalated, and what it cost. Agents integrate with the systems you already use — CRMs, helpdesks, email, Slack, internal APIs — and the model layer stays swappable as providers evolve.