Agentic AI doesn't wait for instructions — it plans, executes, evaluates results, and retries until the task is done. We build AI agents that manage customer enquiries, analyse performance data, update inventory, and orchestrate campaigns across tools your team uses every day.
A regular automation follows a fixed script: if X happens, do Y. An AI agent reasons about a goal and decides which steps to take, in what order, using available tools. If a step fails, it adapts. This makes agents far more capable for tasks with variable inputs — like responding to a unique customer complaint, researching a competitive opportunity, or managing a reorder based on a complex supply chain situation — where rules-based automation would require thousands of pre-written branches.
We use LangGraph for stateful, graph-based agent workflows where we need precise control over the execution path and state management. CrewAI for role-based multi-agent collaborations where specialised agents have defined responsibilities. AutoGen for conversational multi-agent workflows. For simpler ReAct-pattern agents, we build directly on top of the Anthropic or OpenAI function-calling APIs. Framework choice depends on the task structure, required reliability guarantees, and your infrastructure constraints.
We design agents on a supervised autonomy spectrum. Low-stakes actions — reading data, drafting content for human review, generating reports — run fully autonomously. Medium-stakes actions — sending customer emails, updating product prices — require a human approval step. High-stakes actions — placing supplier orders, issuing refunds — are always human-approved. You define the stakes thresholds; we build the approval gates and audit logs.
Yes — agent tool libraries are one of our core builds. We create typed tool functions for every platform your brand uses: Shopify Admin API (orders, products, customers, inventory), Klaviyo (profiles, lists, campaign triggers), Gorgias (ticket creation, notes, tags, macro application), Meta Marketing API, and Google Ads API. Each tool is tested in isolation before being deployed into an agent.
The economics depend on task volume and complexity. For customer service agents handling 200+ tickets/day, the AI cost (typically $0.50–$2.00 per resolved ticket) is 85–95% lower than human cost. For content production agents generating 1,000+ pieces per month, cost per piece is typically under $0.10 versus $10–50 for human-only production. Development cost is a fixed investment that amortises rapidly against these per-task savings.
Our agentic AI team designs, builds, and deploys AI agents with the safety controls, logging, and reliability guarantees production D2C operations require.