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Why AI Agent Demos Break in Production
An AI agent demo only has to work once, on a clean input, with you watching. In production it runs again and again on messy real inputs, and nobody's watching.
I've built AI into real products, from a repricing engine that runs 24/7 for a US Amazon seller to a voice automation platform for a company in Germany. These are the three things I put in place before any agent goes live:
1. Scoped tools, not open access
Give the agent a short list of actions it's allowed to take, each with its own limits, instead of raw access to your database or APIs. Anything that touches money, customers or deleting data needs a human to approve it.
2. A plan for when the model fails
Model calls are slow, they time out, and sometimes they return something that makes no sense. Run agent work in background jobs with timeouts and retries, check the output before acting on it, and hand the task to a person when it still can't be trusted.
3. A log of every decision
Store every input, tool call and output. When the agent does something strange (and at some point it will), you can see exactly why and fix it instead of guessing.
None of this shows up in a demo. All of it decides whether the agent is still running a month later.
Planning an AI agent for your product? I offer a free build audit: calendly.com/roshaanali-business
"Works once with you watching" is the best way I've heard it put. Timeouts and a human fallback are the parts most demos skip.
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I’m Roshaan, an AI agents & SaaS developer.
I help founders build AI agents, automations and SaaS products that hold up in production, not just in the demo.
4+ years. 20+ products shipped.
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
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