When an application starts failing, the first report rarely explains very much. Someone sees an error, a team notices that a service has slowed down, or the service desk begins receiving tickets from employees who cannot get something to work.
Finding the source requires a different kind of information. An IT team needs to know what infrastructure sits behind the application, how those systems connect, what changed recently, and whether the same problem could be affecting something else.
AI can help move through that process faster, but only if it can see enough of the environment to understand what it is looking at.
That creates a problem for organizations moving toward AI agents in IT operations. Giving an agent permission to take action is only part of the equation. If the underlying asset and dependency information is fragmented or outdated, the agent can reach a decision quickly without necessarily reaching the right one.
Freshworks has been addressing that problem from the infrastructure layer of Freshservice. In April, the company introduced a redesigned IT asset management experience that brings continuous discovery and dependency mapping capabilities from Device42 directly into the platform. Freshservice can now continuously discover infrastructure across cloud, on-premises, and hybrid environments while mapping how those assets support applications and business services.
The significance becomes clearer as more operational work is handed to AI.
A Ticket Rarely Contains the Whole Problem
A service ticket is useful because it tells IT what someone experienced. It may say that payroll is unavailable or that an internal application has stopped responding. What it usually cannot explain on its own is why.
An experienced engineer fills in that missing context by looking elsewhere. They may check configuration information, review infrastructure relationships, see whether another service depends on the same component, and compare what they find against recent changes.
For an AI agent to take on more of that reasoning, it needs access to a similar picture.
A traditional asset inventory can tell the system that a server exists. Dependency mapping goes further by showing how infrastructure, applications, and services relate to one another. Freshservice’s newer ITAM capabilities are designed to keep those relationships updated through automated discovery rather than relying entirely on static records maintained by hand.
That difference matters when the system is being asked to do more than answer a question. If an agent is helping investigate an incident or assess a proposed change, knowing that a particular component exists is less useful than knowing what will be affected if that component goes down.
The same context can change how a human approaches the problem. Instead of starting with a ticket and then searching several systems to reconstruct what sits behind it, an agent can work from service information that already includes the relationships between the affected application and its infrastructure.
Better Automation Depends on Better Data
Enterprise AI discussions often focus on model capability, but IT operations exposes another limitation quickly: a capable model cannot infer infrastructure relationships that were never captured accurately in the first place.
Freshworks has increasingly organized Freshservice around that connection between data and action. IT service management, IT asset management, and IT operations management now sit within what the company describes as a unified service operations platform, bringing tickets, assets, incidents, services, and workflows into the same environment.
That shared context also feeds Freddy AI. Freshworks says its unified data layer combines service, asset, and enterprise knowledge so AI agents can use operational information while executing workflows.
For IT teams, the value is easier to see through the decisions they make every day. A proposed change may appear routine until someone realizes that several business services rely on the infrastructure involved. An incident that looks isolated may deserve a different priority once its downstream dependencies are visible.
AI can help surface those relationships faster, but the quality of the answer still depends on whether the relationships themselves are accurate.
Continuous discovery becomes especially important for that reason. Modern infrastructure does not stay still long enough for a manually maintained snapshot to remain dependable. Cloud resources appear and disappear, applications change, devices move, and dependencies evolve as teams update their environments. Freshservice’s Device42-based discovery is designed to feed those changes back into the CMDB so service and operations workflows can work from a more current view.
AI Agents Raise the Cost of Missing Context
The need for reliable operational data becomes more pronounced as AI moves from recommending actions to taking them.
Freshworks introduced AI Agent Studio for Freshservice in May, giving organizations a no-code way to build and deploy Freddy AI agents that can execute service workflows. The company is also developing an MCP Gateway to connect AI systems with additional enterprise data and tools.
Connecting an agent to more systems expands what it can do. It also makes the quality of the context behind each action harder to ignore.
An assistant can tell an engineer what it thinks should happen next and leave the final judgment to the person reading the recommendation. An autonomous workflow may proceed further before a human becomes involved. If the agent understands which applications depend on a particular asset and which business services could be affected, it has a stronger basis for deciding whether an action is routine or deserves escalation.
This is why asset intelligence is becoming part of the enterprise AI story rather than remaining a separate IT management concern.
Organizations’ biggest challenge becomes finding the right ITSM tool for their business.
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