AI agent development.
Chatbots answer. Agents do. We build agentic AI that executes real workflows — resolving support tickets, processing orders, reconciling documents, running back-office operations — with the permissions, audit trails and human oversight that make automation safe to deploy.
Agents that ship
Built on the same production stack as our live Trooply.AI products — commerce and ERP integration is our home turf, which is exactly where agents earn their keep.
Agentic AI for real workflows.
Support resolution agents
Beyond answering: agents that look up orders, process routine refunds within policy, update tickets and escalate edge cases with full context.
Commerce operations agents
Catalog enrichment, price and stock monitoring, order-exception handling, vendor onboarding checks — the marketplace back office, automated.
Document workflow agents
Invoices, claims and KYC processed end to end: extract with AI OCR, validate against your rules, post to your ERP, flag exceptions for humans.
Research & monitoring agents
Competitor pricing, review monitoring, lead research and report drafting on schedule — delivered to your inbox or dashboard, sources cited.
Tool & API orchestration
Agents wired to your real systems — CS-Cart, Magento, ERPs, helpdesks, payment providers — through typed tool interfaces with per-action permissions.
Guardrails & oversight
Approval gates for sensitive actions, spend limits, full audit logs and rollback paths — autonomy earned gradually, never assumed.
What AI agents can — and cannot — do in 2026.
An AI agent is an LLM given tools, memory and a goal — it plans steps, calls systems, checks results and iterates until the job is done or a human needs to decide. In 2026 this reliably automates bounded, high-volume workflows: the support queue’s routine 70%, document processing, catalog operations, scheduled research. It does not reliably replace open-ended human judgment, and vendors promising “fire your ops team” are selling demos.
Our approach: pick one workflow with measurable volume, wire the agent to your systems with narrow permissions, run it shadow-mode against human decisions, then expand autonomy as accuracy proves out. That sequence is why our agent projects reach production instead of dying in pilot.
AI agent questions.
What is the difference between a chatbot and an AI agent?
A chatbot answers questions; an agent takes actions. Agents plan multi-step work, call your systems through tools, verify results and iterate — a support agent does not just explain your refund policy, it processes the refund within the limits you set.
How much does AI agent development cost?
A single-workflow agent with shadow-mode rollout typically runs $8,000–$25,000 depending on integrations; multi-workflow platforms are phased from there. We scope against the hours the workflow currently consumes, so the payback math is explicit.
Is it safe to let an AI agent touch our systems?
With the right architecture, yes: narrow per-action permissions, approval gates for sensitive operations, complete audit logs, spend limits and staged autonomy. Agents get the access an intern would get on day one — and earn more the same way.
Which workflows should we automate first?
High-volume, rule-describable, low-blast-radius ones: routine support tickets, document intake, catalog updates, status chasing. We help you rank candidates by volume × automatability in the scoping call.
Which workflow is eating your team’s week?
Describe it. We will tell you honestly whether an agent can own it — and prove it in shadow mode before it touches anything.