The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Thinking about making a course on how I actually work with AI agents.
Not prompts.
The rules, skills and checks that make the agent build things the way my repo wants.
Would that be useful to anyone?
Yeah, definitely! I’m not that good with AI yet, so something like this would really help me understand how to work with AI agents properly. I’m sure a lot of others could learn from it too.
Okay! Is there anything specific you would like to learn or everything just in general? Are you a designer or a developer?
Yes please. The rules and checks part is what nobody teaches. Prompt tips are everywhere, repo setup isn't.
Awesome! I think this is definitely the area where I could help.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
AI Revenue Ops Mesh: Multi-Agent System Design
A system design for a multi-agent revenue operations workflow, built by UI Trax.
The problem: when several AI agents handle research, sales and support, leads get lost in the handoffs. Duplicate messages, unverified bookings, and risky actions with no human check.
The design:
• One shared customer record, deduplicated with idempotency keys
• An orchestrator that assigns one owner at a time, with step and budget limits
• Four specialist agents: Research, Qualify, Sales and Support
• A tool layer for CRM, calendar, WhatsApp API, payments, email and docs
• Guardrails: policy engine, human approval gate and audit log
• A supervisor for loops, tool errors and customer replies
• An eval gate, so no update ships if it breaks a case that used to pass
Deliverable: architecture, workflow logic and a 64-second animated explainer.
Report figures in the video are example data.
AI Agent for ServiceNow Automation (LangGraph + GPT-4o-mini + Slack, Jira, Email)
I built a LangGraph agent that turns natural-language requests into ServiceNow incidents, so users never have to open the ServiceNow UI. A StateGraph workflow pairs an LLM node (GPT-4o-mini) that understands the request with a tool node that runs the ServiceNow Table REST API. It supports caller, description, and assignment group fields, and it resolves group names to sys_ids by looking them up in the sys_user_group table, so users can just say “Network Team”. The agent is exposed through a Flask REST endpoint and an interactive CLI, with credentials kept in environment variables and error handling around every API call. I then extended the same pattern to Slack, Email, and Jira to automate workflows across platforms.
Understand: The LLM node interprets the request and extracts the caller, description, and assignment group.
Act: The tool node executes ServiceNow actions through the Table REST API.
Resolve: Group names are looked up in the sys_user_group table to get the sys_id, so users refer to teams by name.
Expose: A Flask REST endpoint and an interactive CLI give two ways to use the agent.
Secure and robust: Credentials are managed through environment variables, and API calls have error handling.
Extend: The same agent pattern was reused for Slack, Email, and Jira integrations.
Tools to tag
Python, LangGraph, OpenAI GPT-4o-mini, Flask, ServiceNow REST API, Slack API, Jira, Agentic AI, Automation
I built a little hotel you can explore.
With an AI teammate that turns guest requests into action.
Meet SuiteDesk, my entry for the #Base44Challenge, built with @Base44.
It’s an AI concierge and service workspace for boutique hotels.
The idea started with a small moment:
“Could we have two extra towels?”
A simple request still needs to reach the right person, get handled, and get back to the guest. SuiteDesk connects those steps.
Mira captures the room request.
Housekeeping receives the task.
A team member picks it up and completes it.
The guest receives an update
* Mira is the AI agent persona in SuiteDesk, trained on the hotel's specific service policies and local recommendations
What she does:
- Answers guest questions using only the hotel's own approved details: Wi-Fi, breakfast hours, amenities, policies.
- Turns a sentence like "we need towels" into a real request: it lands on your board, and the guest follows its status in the conversation.
- Recommends places and activities around the hotel, researched live.
- Writes in the guest's own language and always speaks to them as a guest, never as staff.
Guests ask from their phones. Staff manage the work from a shared service desk.
I wanted that experience to feel as welcoming as the hotel itself.
So I built an illustrated world around it: a courtyard, rooms, a spa, and Mira waiting to help.
The design gives you places to explore and ways to try the service.
Inside Base44, I worked on the agents, their instructions, hotel knowledge, and request workflow.
I kept reworking the artwork, copy, and navigation until the hotel helped explain the product.
The walkthrough shows both sides: the interactive experience and a labelled test request moving through the live service desk.
My goal was to give boutique hotel teams less to chase, while keeping hospitality personal.
Watch the build, then step inside:
https://elite-stay-stream-flow.base44.app/guestbook
Starting from "two extra towels" is such a good way to frame it. The guest seeing the status update is the part hotels usually miss.