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58m ago
AI Agents: From Build to Production
Building an AI Agent is only half the work — getting it ready for production is where the real discipline begins.
Overview
In this session, Ritesh Ramesh and Dustin Snell walk through practical guidance for improving AI Agent prompts, selecting the right tools, understanding retrieval-augmented generation, and preparing agents for enterprise deployment with guardrails, security controls, and evaluations.
Learn how ServiceNow AI Agents can operate across conversational channels, connect to enterprise systems, use internal and external data sources, and be tested before going live.
Video Timestamps
What You'll Learn
How AI Agents can operate across channels like Messenger, Slack, Teams, SMS, and more
Why prompts and tools are the two core controls when building AI Agents
How Retrieval-Augmented Generation improves accuracy with business context
When to use context stuffing — and when it can become too much
How to simplify prompts so agents follow instructions more reliably
Why agents need permission to say "I don't know"
How roles, examples, and prompt structure affect output quality
What tool types are available inside ServiceNow AI Agent Studio
How to prepare agents for production with security, privacy, and evals
Tools Covered
Search Retrieval & AI Search
Find and retrieve relevant information efficiently
Knowledge Articles
Leverage internal and external content sources
Knowledge Graph
Connect entities and relationships for context
Flows & Scripts
Build automations with flows, subflows, and scripts
Integration Hub
Reuse existing connectors as agent tools
MCP Tools
Connect to external systems and services
Production Readiness, Security & Evaluations
Deploy AI Agents confidently with these production-grade capabilities:
Agent evals help teams prove whether an AI Agent is actually doing its job before turning it on for employee-facing or customer-facing use cases.
💡 Key Takeaway
Good AI Agents are built with both creativity and control.
- Prompts help define how the agent thinks
- Tools determine what the agent can do
- Guardrails define what the agent must not do
- Evals prove whether the agent is ready
The future of AI Agent deployment will not be just about building agents — it will be about measuring, governing, and improving them continuously.
About This Series
This series explores AI Agents in ServiceNow — from use case selection and architecture to hands-on implementation, prompting, tool selection, governance, evaluations, and production readiness.
Built for ServiceNow architects, developers, administrators, platform owners, AI practitioners, and business leaders looking to build enterprise AI solutions that are useful, measurable, and safe to deploy.
