Use Cases
Enterprise AI Agents: The Future of Business Automation
Enterprise AI agents go beyond simple automation to handle complex, context-dependent business processes. They're shifting enterprises from "systems of record" to "systems of agents"—and context graphs are the key to making them work.
15%
of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from essentially none in 2024.
— Gartner
What Are Enterprise AI Agents?
Enterprise AI agents are autonomous AI systems that can execute complex business processes with minimal human intervention. Unlike simple chatbots or rule-based automation, these agents can:
- ●Understand context — Grasp the full situation, not just the immediate request
- ●Make decisions — Choose between options based on policies, precedent, and goals
- ●Handle exceptions — Navigate edge cases that would break traditional automation
- ●Learn from experience — Improve over time based on outcomes and feedback
- ●Coordinate with other systems — Work across multiple tools and data sources
From Systems of Record to Systems of Agents
For decades, enterprise value has centered on "systems of record"—CRMs, ERPs, HCM platforms that store business data. The next decade will see a shift to "systems of agents"—AI that acts on that data.
| Systems of Record | Systems of Agents |
|---|---|
| Store data | Act on data |
| Human-driven workflows | AI-driven automation |
| Rules-based logic | Context-aware reasoning |
| Salesforce, Workday, SAP | AI agents + context graphs |
Enterprise AI Agent Use Cases
Customer Support Automation
AI agents that can resolve support tickets by understanding customer history, checking policies, and taking action—not just answering questions.
With context graphs: Access to past resolution patterns, customer-specific exceptions, and escalation precedents.
Sales & Deal Desk
AI agents that handle pricing approvals, discount requests, and contract negotiations using institutional knowledge about what's been approved before.
With context graphs: Precedent search for similar deals, understanding of customer tier exceptions, approval patterns.
IT Operations (AIOps)
AI agents that diagnose issues, correlate alerts, and take remediation actions based on historical incident patterns.
With context graphs: Past incident traces, successful resolution patterns, change management history.
Procurement & Vendor Management
AI agents that handle purchase requests, vendor selection, and contract approvals using historical purchasing decisions.
With context graphs: Vendor performance history, approval thresholds, exception patterns.
HR & People Operations
AI agents that manage employee requests, policy exceptions, and routine HR processes while maintaining compliance.
With context graphs: Policy exception history, compensation precedents, approval patterns.
Why Enterprise Agents Need Context Graphs
Enterprise processes are full of exceptions, edge cases, and unwritten rules. Standard AI approaches fail because they can only follow explicit rules:
Without Context Graphs
- ✗Follows rules blindly
- ✗Escalates every exception
- ✗No learning from outcomes
- ✗Can't handle edge cases
With Context Graphs
- ✓Applies precedent-based reasoning
- ✓Handles exceptions autonomously
- ✓Builds compound intelligence
- ✓Navigates real-world complexity
“Without context graphs, an AI agent is like an extremely smart intern on day one—it can follow written rules but gets tripped up by every unwritten exception.”
Building Enterprise AI Agents
Key considerations for enterprise AI agent deployments:
- 1
Start with Decision-Dense Processes
Focus on processes with lots of decisions and exceptions—that's where AI agents add the most value.
- 2
Build the Context Layer
Instrument decision points to capture traces. Start building your context graph from day one.
- 3
Human-in-the-Loop Initially
Start with AI-assisted decisions, graduating to autonomous as confidence builds.
- 4
Ensure Governance
Build audit trails from the start. Compliance is essential for enterprise adoption.