Service

AI Agents

Autonomous agents that take goals as input and produce outcomes as output. No human clicking through steps.

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What this covers

Task-completion agents

Agents that receive a goal and work through it autonomously — browsing, querying APIs, writing files, looping until done.

Tool-using agents with memory

Claude API agents wired to real tools: databases, external APIs, vector stores. They remember context across sessions.

Multi-agent pipelines

Orchestrator-worker patterns where specialist sub-agents handle discrete steps. Built with CrewAI or LangChain depending on the problem.

Customer-facing agents

Live agents embedded in products — like StoreAlert, which handles real customer interactions end-to-end without a human in the loop.

How it works

01

Define the goal and boundary

We map exactly what the agent needs to accomplish, what tools it needs access to, and where human oversight should kick in.

02

Build, wire, test

Agent logic, tool integrations, and failure handling built in a single tight loop. You get a working prototype fast — then we harden it.

03

Ship to production

Deployed with observability so you can see what the agent is doing, catch edge cases, and improve over time.

In the wild

$ agent.init('StoreAlert')

> Connecting to store API...

> Agentic assistant: ready

StoreAlert

AI agent

Agentic store assistant with live customer interactions. Handles queries, escalations, and fulfilment lookups autonomously. No human in the loop for standard interactions.

Have a process that should run itself?

Tell me the goal. I'll tell you if an agent can own it.

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