Industry Insights
Agentic AI vs Generative AI: Key Differences & ITSM Applications
Agentic AI completes workflows autonomously across systems, while generative AI creates content that requires human execution. One eliminates coordination work entirely, the other speeds up content creation but leaves you handling the follow-through.
For IT and operations teams, this distinction determines whether you're actually freed from manual handoffs or just getting better-written tickets. When requests bounce between departments and every approval requires chasing someone down, you need systems that execute end-to-end, not systems that draft responses.
This guide breaks down how each AI type works, what makes them operationally different, and where they deliver value across departments. You'll see practical examples from finance to IT operations, then learn why agentic AI is the clear choice for handling ITSM coordination.
What is Agentic AI?
Agentic AI acts on your behalf, completing entire workflows without human oversight at each step. The term "agentic" refers to systems that operate with delegated authority, making contextual decisions and executing actions across multiple platforms autonomously. Unlike rules-based automation that follows rigid if-then logic, agentic AI adapts to changing conditions, evaluates trade-offs, and handles exceptions.
Three capabilities separate agentic systems from traditional automation.
- Autonomous decision-making evaluates current conditions and selects appropriate actions without requiring human input at each decision point. The system interprets context, weighs options against defined parameters, and proceeds based on its assessment.
- Cross-system orchestration executes workflows across disconnected platforms and departments. The agent authenticates with multiple systems, translates data between different formats, maintains state across long-running processes, and coordinates actions that span organizational boundaries.
- Proactive execution continuously monitors defined triggers and initiates workflows when conditions are met. Rather than waiting for human commands, the system recognizes events that require action and begins the appropriate process independently.
Agentic ITSM example: An employee requests access to a software tool.
The agentic system checks their role in the HR platform, routes an approval request to their manager, waits for confirmation, provisions the access through your identity management system, and notifies the employee that it's done. You never touched the request.
What is Generative AI?
Generative AI creates new content from prompts but requires humans to execute the suggested actions. These systems produce text, images, code, or other outputs in response to user requests, then hand everything back for review and implementation. The output stops at creation; execution remains your responsibility.
Three characteristics define generative systems.
- Reactive operation means the system waits for prompts before producing anything. Nothing happens until someone asks a question or provides instructions, and the system returns to an idle state after delivering its response.
- Content-focused output produces materials humans can use, but not actions the system executes itself. The AI generates drafts, suggestions, analyses, or code snippets that require human review and implementation.
- Single-interaction scope handles one request at a time without maintaining context across multiple systems or coordinating follow-up actions. Each prompt generates a response, and then the workflow ends until the next human input.
Generative ITSM example: An IT manager asks a generative system to draft a knowledge base article about password reset procedures.
The AI produces a well-structured article with step-by-step instructions. The manager still needs to review it for accuracy, edit for company-specific details, publish it to the knowledge base, and update any related documentation.
The Key Differences Between Generative & Agentic AI
The operational differences between generative AI and agentic AI determine which problems each is best suited for:
Agentic AI and Generative AI Differences By Department
Seeing both AI types in action across different departments shows where each fits and why the operational differences matter.
Agentic AI vs Generative AI in Finance & Procurement
Finance teams juggle compliance documentation and multi-department approval workflows where speed and accuracy both matter.
Generative AI drafts policy documents, creates financial report summaries, generates compliance documentation, and produces executive briefings from raw data. The AI transforms data into readable content, but humans verify accuracy and distribute it to stakeholders.
Agentic AI processes expense reports end-to-end, verifying policy compliance, routing to appropriate approvers based on amount and department, processing reimbursements, and updating accounting systems. Procurement workflows handle vendor approvals across Legal, Finance, and Operations, coordinating system updates and approval checkpoints autonomously.
Agentic AI vs. Generative AI in HR & People Operations
HR teams balance high-volume employee communications with complex cross-departmental workflows for onboarding and lifecycle management.
Generative AI creates onboarding documentation, drafts policy updates, generates employee communications, and produces performance review templates. Content creation happens quickly, but HR teams still handle distribution, personalization, and system updates.
Agentic AI coordinates employee onboarding across departments, provisioning accounts, ordering equipment, scheduling training, and notifying stakeholders automatically. Performance review cycles are triggered based on hire dates, routing forms to appropriate managers, and escalating overdue submissions without manual tracking.
Agentic AI vs. Generative AI in IT Operations
IT teams handle the highest volume of cross-system requests, where manual coordination creates the biggest bottleneck.
Generative AI creates knowledge base articles from common request patterns, drafts ticket responses teams can customize, generates documentation when features ship, writes automation scripts, and produces trend summaries from backlog data.
Agentic AI, via AI-powered service desks, executes password resets from start to finish, catching requests in chat platforms, verifying users, resetting credentials, and sending notifications. Software access requests move from submission to completion, checking employee roles, obtaining manager approvals, provisioning through identity management systems, and confirming with requesters.
How Agentic AI Transforms ITSM
IT teams face the highest coordination overhead because every request bounces between multiple systems and departments before completion. Generative AI speeds up drafting, but someone still chases approvals and manually executes each step. Agentic AI eliminates that coordination work by executing complete workflows autonomously, end-to-end.
Platforms like Siit demonstrate what agentic AI-powered ITSM looks like in practice:.
Autonomous request resolution
On Siit, Agentic AI enables autonomous resolution by executing complete workflows without requiring human intervention at each step. The agent pulls data from multiple systems, makes decisions based on context, and executes actions across your entire stack.
Scenario: An employee requests Figma access in Slack. The agent verifies their role in your HR system, routes approval to their manager, provisions access through Okta, updates asset management, and notifies everyone it's done.
Intelligent workflow automation
Agentic AI powers intelligent automation by adapting workflows based on employee role, department, and request history. Siit’s AI learns from unified operational data, not just ticket descriptions, so different employee types trigger different workflows automatically.
Scenario: When someone requests a laptop, the system checks their role and location, routes to the appropriate approval chain, triggers procurement, coordinates shipping, updates your CMDB, and confirms delivery without manual coordination.
Intelligent triage and continuous learning
Agentic AI drives intelligent triage by categorizing and prioritizing requests based on content and requester attributes, improving as it learns from your team's decisions. Every resolved request strengthens the system, converting outcomes into reports that optimize processes and improve self-service deflection.
Scenario: A C-level password reset during a board meeting gets a different priority than routine software access. The agent operates in Slack or Teams, where work already happens, with approvals and confirmations happening in-thread while backend orchestration runs invisibly.
Agentic AI: The Clear Choice for IT Teams
Generative AI produces content but requires humans to execute the work, while agentic AI completes entire workflows autonomously from trigger to completion. If your biggest problem is creating content faster, generative AI delivers. If it's eliminating manual coordination, agentic AI removes you from the handoff loop entirely.
Siit brings agentic AI directly into ITSM coordination, the area where manual handoffs hit hardest. The platform lives in Slack and Teams, automatically routing requests across IT, HR, and Finance without forcing anyone into a ticketing portal, resolving requests 52% faster.
Book a demo to see how agentic AI transforms internal operations.
