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an hour ago - edited an hour ago
Getting Started with AI Agents: Why Context Is Everything
Here's a pattern that shows up quietly across enterprise AI deployments: teams invest in AI, get it connected, and then notice something frustrating. The outputs look reasonable. The language is clean. But the responses keep missing the mark — recommending an action that was already taken, summarizing a ticket without accounting for the current workflow stage, or generating a response that's technically correct but operationally useless.
It's tempting to call this hallucination. But most of the time, it isn't. It's something more precise: context loss.
When AI operates outside the system where work actually happens, it can only reason about what it was told — not what's live, what's in flight, or what the platform already knows. The result isn't broken AI. It's AI reasoning in a vacuum.
Welcome back to our series from the AI Center of Excellence (CoE) team at ServiceNow. Through countless advisory and hands-on engagements, we've gathered valuable insights and practical guidance that we're excited to share with the broader ServiceNow community.
🎬 Watch the Video
This is part of our ongoing Now AI video series, where we've been building out real-world AI capabilities step by step.
What We Covered
In this installment, we get into the mechanics of why AI output quality degrades when context is missing — and what state-aware AI looks like when it's working the right way. Here's what the discussion covers:
- Why AI output quality collapses without state awareness — AI without access to live records has no way to know where a ticket is in its lifecycle, what's already been tried, or what constraints are currently active
- Linguistic accuracy vs. operational correctness — an AI response can be grammatically sound and factually plausible while still being wrong for the moment it's being used
- The specific context that gets lost outside the platform — workflow stage, record relationships, SLA status, assignment history, and business policies that only exist inside the system of record
- Why this isn't hallucination — it's context loss — a meaningful distinction that changes how teams should think about AI output failures and how to fix them
- How state-aware systems produce signal instead of noise — what it looks like when AI generates responses against live records, within active workflows, and under real system constraints
- How Now Assist addresses this by design — because it operates natively on the Now Platform, it generates outputs grounded in the actual state of the system — not a stale snapshot
Resources We Found Helpful
As we thought through this topic, these ServiceNow resources helped ground the discussion:
- Context Engine Overview — ServiceNow's Context Engine grounds AI decisions in live enterprise data by connecting assets, workflows, people, policies, and operational history through the Workflow Data Fabric — without requiring a unified data lake: servicenow.com/products/context-engine
- Workflow Data Fabric and AI Grounding — A detailed look at how ServiceNow's Workflow Data Fabric connects data across systems in real time — giving AI agents not just information, but the business logic and escalation context needed to act correctly: ServiceNow Newsroom
- Advanced AI Agent Instructions Guide — A community deep-dive on how to structure agent instructions for situational awareness, including how to pass relevant background context, scope decision-making, and prevent agents from operating on incomplete signals: ServiceNow Community
- Now Assist Platform Documentation — Core reference for understanding how Now Assist capabilities are administered and embedded within live platform workflows: docs.servicenow.com
- Getting Started with Agentic Workflows & AI Agents — A hands-on guide covering data quality, role configuration, and how AI agents use ticket context and knowledge content to generate plans and take actions: ServiceNow Community
What's Next
Context isn't a nice-to-have for enterprise AI — it's the difference between a response that moves work forward and one that creates more work to untangle. The organizations getting the most out of Now Assist are the ones who've treated data quality, record hygiene, and platform-native deployment as first-order requirements, not afterthoughts.
If you have questions about context quality, AI grounding, or how your team is approaching this in your own deployment, drop them in the comments. We'll respond or update the article as needed.
Contact your ServiceNow representative to learn more about Now Assist and the Context Engine, and how organizations are building AI programs that produce outputs worth acting on.
Check out the rest of our Now AI video series here on getting started with AI Agents!
Views expressed are our own and do not represent ServiceNow, our team, partners, or customers.
