AI agent development that survives production.
Anyone can demo an agent. We build the ones that still work in month six: scoped tools, human approval where it matters, and error handling for everything else.
Three shapes of production agent.
Tool-using agents
Agents wired to your CRM, docs, calendar, and inbox that read a request, pick the right tool, act, and report back inside one auditable workflow.
Retrieval over your data
Answers grounded in your own content, vector search over docs and tickets, so the agent cites your knowledge instead of hallucinating.
Approval-gated automation
Human-in-the-loop steps exactly where mistakes cost money: the agent drafts, a person approves, the workflow executes.
Built for a regulated enterprise.
Self-hosted n8n doesn't have to mean fragile. This is a hardened, queue-mode deployment we delivered for a large enterprise in a regulated industry, read-only containers, SSO, and SIEM logging, the same blueprint we bring to every build.
Hardening: cap_drop ALL · read-only rootfs · internal-only network · health checks · WAL replication.
How agents get reliable.
The gap between an agent demo and an agent you trust is unglamorous engineering: retries and idempotency so a flaky API cannot double-charge a customer, persistent memory so context survives restarts, structured logging so you can answer "why did it do that", and approval gates where actions are irreversible.
We write publicly about exactly this: how agents keeppersistent memory, why zero trust beats full autonomy, and how production error handling is built. The blog is the methodology; the build is the service.
Learn how we build
AI agent development, answered.
What is an AI agent versus a chatbot?
A chatbot answers questions. An agent takes actions: it has tools, your CRM, your calendar, your APIs, decides which to use for a goal, executes, and reports back. The engineering work is not the model, it is the guardrails, tooling, and reliability around it.
What stack do you build agents on?
n8n as the orchestration layer, with Claude, OpenAI, or Gemini models depending on the job, plus vector stores for retrieval and MCP where it fits. Everything runs as inspectable workflows, not a black box, and can live on your own infrastructure.
How much does AI agent development cost?
Agent builds follow our standard fixed-scope model: from $2,000 for a production agent with error handling, logging, and documentation, after a $500 audit that defines scope. Ongoing evolution is covered by the automation partnership from $1,500 per month.
How do you keep an agent from doing something wrong?
Scoped tools, so the agent can only touch what it needs. Human approval steps where actions are irreversible or customer-facing. Full logging of every decision and call. And zero-trust design by default: we treat the agent like a junior employee with a narrow badge, not an admin.
Can agents run on our own infrastructure?
Yes. Self-hosted n8n means the orchestration, credentials, and logs stay on servers you control. Model calls go to the provider you choose, or to local models where the workload allows it.
What should your first agent do?
Bring the process; we'll tell you if an agent is even the right tool.