To create an AI agent for a business workflow, do not start with the model. Start with the job. The agent should have a clear trigger, a narrow task, approved tools, context boundaries, and a human handoff rule.
How to create an AI agent
- Pick one job, such as lead triage, follow-up drafting, inbox routing, CRM summary, or weekly reporting.
- Write the inputs the agent is allowed to read: form data, CRM fields, notes, conversation snippets, files, or knowledge base articles.
- Define the tools the agent can use: search, CRM lookup, database query, email draft, Slack post draft, ticket creation, or workflow trigger.
- Define what the agent must never do without approval: send messages, delete data, change payment status, update high-value CRM records, or contact customers.
- Add output structure so the answer is predictable: summary, decision, confidence, missing information, proposed action, and next owner.
- Log every run with inputs, tool calls, model output, approval decision, and final action.
- Test against real messy examples, not only clean demo prompts.
Simple agent stack
- n8n or Zapier for no-code and low-code workflow entry points.
- LangGraph when the agent needs state, branching, approvals, retries, or long-running steps.
- LangSmith when you need traces, debugging, evaluation, and production monitoring.
- Supabase or PostgreSQL when you need durable records, audit trails, and structured data.
- React or Next.js when operators need a queue, dashboard, approval screen, or admin panel.
The safest first version is not a fully autonomous employee. It is an AI reviewer that drafts the next step, explains its reasoning, and waits for a human to approve before anything leaves the system.