Agentic Enterprise: How ServiceNow Is Transforming AI-Driven Operations

At Knowledge 25, ServiceNow unveiled its boldest vision yet: the shift from traditional automation to a new frontier powered by intelligent, autonomous AI agents. These aren’t just next-gen chatbots. They’re systems capable of real-time reasoning, action, orchestration, and continuous improvement. This new model—the agentic enterprise—isn’t just about smarter technology. It’s about transforming how work gets done at every level.

AI tools in the past have focused primarily on generating content or routing basic queries. Virtual agents often failed when conversations went off-script, escalating to humans as soon as ambiguity arose. But the world of enterprise service delivery is rarely simple. It’s nuanced, dynamic, and often unpredictable.

That’s the core limitation the agentic model aims to fix. These AI agents don’t rely on hard-coded decision trees. Instead, they operate based on context, memory, reasoning, and process execution. They can:

• Understand and interpret natural language.
• Make autonomous decisions based on user intent.
• Interact with multiple systems and data sources.
• Take real-time action—not just provide suggestions.

Let’s ground this in a few real-world scenarios:

Imagine you’re booking a flight through an airline’s website and need to change your return date. With older virtual agents, you’d type “change return date,” and if your phrasing didn’t match exactly, it would reply with, “I’m not sure how to help with that. Transferring you to a human.”

With an agentic AI? It understands when you say, “I need to fly back a day earlier,” looks up your itinerary, checks available flights, compares prices, and lets you confirm, all in one seamless interaction.

Or say you’re in IT support and an employee reports, “My laptop keeps overheating and shutting down when I open Zoom.”

A traditional system might log the ticket and wait for a human to respond. An AI agent could analyze historical incident patterns, run diagnostics, check warranty status, and start the replacement process, all before a technician is even notified.

Now take a retail support example.

A customer messages, “Can I return these shoes? They don’t fit.” Instead of linking to a returns policy page, an agentic AI would verify the order, check eligibility, generate a return label, and email it with no back-and-forth required.

The vision is similar to asking a home assistant to “make the house comfortable” without detailing every step. You don’t need to specify temperature or lighting, the system figures it out based on past behavior and environmental data. These AI agents work the same way: you specify the outcome, and the agent determines the best way to get there.

What is an agentic enterprise?

The agentic enterprise goes beyond digital transformation. It introduces a new operational model where AI agents are active participants in workflows, not just passive tools.

According to ServiceNow, AI agents in an agentic enterprise are defined by:

• Autonomy: They can operate independently without requiring every action to be explicitly designed in advance.
• Agency: They can make decisions and complete tasks on behalf of users.
• Learning and improvement: They adapt based on real-time data and historical context.

This model dramatically reduces the need for micromanagement of processes. It also allows for more scalable and adaptive workflows, especially in environments with high variability and customer-facing operations.

Use Case: AI agents in Customer Service Management (CSM)

ServiceNow has already operationalized agentic AI through a full program built into CSM. This is more than a proof of concept, it’s a deployable framework that organizations can use now.

The CSM AI agent can:

• Validate customer records against a foundation data model.
• Identify and clean up duplicates or inconsistent data.
• Handle both informational queries and transactional tasks like address changes.
• Route complex issues to the right team based on context.

A real example shared in the session involved a tier-one service request for a gift card replacement. The AI agent validated the card, checked eligibility based on service location, and offered the right resolution all without human involvement until escalation was required. It even learned to account for nuances like military service when determining valid options.

The major leap here is that these capabilities are out-of-the-box. Organizations only need to configure them to match their internal data models and processes. No custom development required.

AI agents in B2B

In a B2B context, imagine a SaaS provider managing thousands of customer accounts.

Customers often open support tickets to request feature access, billing adjustments, or service upgrades. Traditionally, this would require manual validation, back-and-forth communication, and human review of eligibility criteria.

With agentic AI, a request to “add five seats for marketing” can be interpreted, validated against subscription limits, invoiced, and provisioned—all autonomously.

Similarly, in B2B telecom, clients managing large-scale voice and data deployments frequently request changes to service tiers or inquire about billing disputes.

Instead of routing these to human agents and introducing delays, AI agents can match the customer to contract terms, review historical usage, and suggest the best-fit option in seconds. If the issue is nuanced or high-value, it’s escalated automatically to the right account team with full context already packaged.

These aren’t theoretical upgrades.

They’re critical improvements in speed, scalability, and customer satisfaction, especially in sectors where SLAs, contracts, and multi-touch workflows define success. By leveraging AI agents that can act with autonomy and context-awareness, B2B teams shift from reactive support to proactive service.

Inside the architecture of agentic AI

What makes this possible is a flexible architecture designed around real-world decision-making and execution. Each component of the agentic architecture is responsible for enabling autonomous, intelligent action in dynamic enterprise environments:

1. Triggers: These are the events that initiate agentic workflows. For example, a customer submitting a support ticket, an IoT sensor reporting equipment failure, or a calendar event starting a workflow. Triggers serve as the starting signal for the agent to begin its work.

Example: A logistics company uses triggers to launch a real-time response when a temperature-sensitive package goes out of range. The AI agent automatically checks the shipment path, notifies the handler, and reroutes it to prevent spoilage.

2. Orchestrator: This is the logic layer that coordinates between multiple agents and systems. It decides how work is assigned and which agents should handle which parts of the task, especially when multiple actions or decisions are required in parallel.

Example: In telecom, a customer requests to upgrade a business internet plan. The orchestrator routes the request to one agent to check eligibility, another to simulate usage impact, and another to initiate provisioning—ensuring a smooth, coordinated upgrade.

3. Memory:
   • Short-term memory tracks current interactions and contextual clues (like the last thing the user said or selected).
   • Long-term memory stores patterns, preferences, and user behavior history across sessions.
     
4. Example: A B2B SaaS provider’s AI agent recalls that a customer’s finance team previously asked for net-30 billing. When the same customer later inquires about new plan pricing, the agent suggests options based on that preferred payment structure.
   
5. Tools: These are the functionalities the agent draws from to complete its tasks. This includes:
   
   • Flow actions and subflows: Predefined sequences for things like provisioning, onboarding, or approvals.
   • Script modules: Custom logic to handle nuanced or organization-specific conditions.
   • API integrations: Connections to CRMs, ERPs, ITSM tools, or external systems.
   • Knowledge management: Dynamic access to documentation, FAQs, and internal knowledge bases.
   • Content generation tools: For creating emails, reports, or internal case summaries.
     
6. Example: In HR onboarding, the AI agent uses flow actions to create accounts, provision software, and schedule training. It taps APIs to pull role-specific info from the HRIS, and generates a welcome email based on company tone and past examples.

This modular setup enables agents to process data in real time, adapt to unexpected inputs, and self-correct when needed. Instead of being limited by rigid process maps, agents can chart new paths based on changing conditions.

Crucially, each piece of this architecture can be updated or replaced without rebuilding the entire system. This allows enterprises to incrementally upgrade their capabilities while keeping the AI workforce aligned with evolving business needs.

When should You deploy AI agents?

Agentic AI isn’t for every use case. But there are specific signals that your organization is ready:

• Complex decision-making: If workflows require evaluating multiple variables and contexts.
  
  Example: A financial services firm handling client portfolio rebalancing can use agentic AI to weigh market conditions, client risk preferences, and recent activity to suggest next steps—without manual spreadsheet juggling.
  
• Frequent exceptions: If your virtual agents often escalate due to edge cases.
  
  Example: In B2B insurance, a claim flagged with missing policyholder data could be paused indefinitely in traditional systems. An AI agent can detect the gap, retrieve the data from integrated records, and move the claim forward without delay.
  
• Unstructured data: If you deal with images, freeform text, or multiple input formats.
  
  Example: A manufacturing support desk receives equipment issue reports with blurry photos or vague descriptions like “machine stalled.” An AI agent with vision recognition and language models can still make accurate recommendations, pulling historical fixes for similar incidents.
  
• Regulatory complexity: If your processes must adapt across different rulesets.
  
  Example: A global logistics provider can deploy AI agents that tailor customs documentation processes based on country-specific import/export laws, ensuring compliance without requiring separate teams to handle regional exceptions.
  
• Dynamic customer interactions: If inputs vary significantly in format or timing.
  
  Example: A SaaS company supporting users across roles and industries may receive setup requests in plain text, Excel templates, or even phone calls transcribed to tickets. Agentic AI can normalize this variety and guide each request to fulfillment with consistent outcomes.

These scenarios are where generative, autonomous AI significantly outperforms traditional automation. It reduces bottlenecks, handles variation with context, and scales decision-making without scaling human overhead.

Why your data and process matter more now

AI agents thrive on structure, but they don’t need perfection. One of the most important takeaways from the session was that organizations should aim for data readiness, not data perfection. The key is to have enough structured, accessible, and timely information to empower the agent to operate effectively in the moment.

ServiceNow shared five principles to make your data usable for agentic AI:

1. Access: The agent must be able to reach the data it needs.
   
   • Example: If a customer support agent can’t retrieve order history due to restricted permissions or siloed systems, the value of AI breaks down. Ensuring seamless access across platforms is foundational.
     
2. Freshness: Outdated data leads to hallucinations or errors.
   
   • Example: An AI agent recommending an expired promotional price or referencing obsolete service plans can create customer friction. Real-time syncs and clear update cadences reduce risk.
     
3. Structure: Clean relationships and labels allow easier interpretation.
   
   • Example: If “client,” “account,” and “organization” are used interchangeably in your CRM, the agent might misroute a query. Clear taxonomies and semantic consistency drive performance.
     
4. Context: Metadata and usage parameters help guide AI behavior.
   
   • Example: An agent that knows a file is tagged “for executives” will present it differently than one intended for a technician, tailoring responses accordingly.
     
5. Ownership: Someone needs to manage and maintain these standards.
   
   • Example: Without clear accountability, stale records or duplicates pile up. A designated data steward ensures that what the agent relies on remains reliable.

On the process side, think of workflows as flexible frameworks, not rigid scripts. Agentic AI is most powerful when it can make decisions at runtime, based on current inputs and conditions, rather than following a pre-built flow.

Example: In traditional automation, if a customer changes the delivery address mid-process, it might halt the workflow. In an agentic setup, the AI detects the update, recalculates shipping timelines, and routes the change downstream—all in real time.

Preparing for this kind of flexibility means mapping not only what the process is but where it breaks, how exceptions are handled, and where AI can step in to reduce manual overhead. This shift requires collaboration between business stakeholders, data owners, and process architects—but the outcome is a system that’s resilient, responsive, and ready for scale.

Prompt engineering: The hot enterprise skill

With traditional automation, scripting was everything. With AI agents, prompting is the new scripting—but it’s a skill rooted more in communication than code.

Prompt engineering doesn’t require deep technical knowledge. It’s about being intentional and structured in how you instruct the AI. The better the prompt, the better the result. That means:

• Giving clear, specific instructions: Be explicit about what you want. Ambiguity leads to generic or incorrect outputs.
• Avoiding vague or open-ended phrasing: “Tell me about our customers” is less useful than “List the top five complaints received from enterprise customers in the past 30 days.”
• Iterating based on output: Treat it like a conversation. If the first result isn’t right, refine and clarify.
• Matching the prompt to the task: Use structured formats (like bullet points, tables, or character limits) when needed.

Let’s look at a few real-world enterprise examples:

• In customer success: Instead of asking, “Summarize this customer’s history,” try “Summarize this enterprise customer’s activity over the last 90 days in under 150 words, including key product usage milestones and support tickets.”
• In IT: Instead of “Explain this incident,” try “Provide a 3-bullet explanation of root cause, affected services, and resolved actions for incident #11239, in plain language suitable for an executive.”
• In sales enablement: Instead of “Give me battlecard info,” ask “Summarize the top 3 competitive differentiators of our solution vs. [competitor] for a VP of Operations in manufacturing.”

These improved prompts reduce friction, improve consistency, and free up team members from rewriting or correcting AI outputs. Done well, prompt engineering becomes a core part of digital fluency—especially as agentic workflows scale across business units.

Tip: Think like a director. Your AI is the actor, capable, flexible, and talented, but you have to provide the context, tone, and direction for the performance you want.

Implementation strategy: How to start now

You don’t need to wait to get started. ServiceNow laid out a clear roadmap:

1. Focus on CSM: Start where AI is already built in.
2. Audit your data models: Ensure your customer, product, and service data are connected.
3. Use Journey AI: Crawl your workflows and identify pain points.
4. Experiment with Agent Studio: Build and test simple use cases first.
5. Scale with confidence: Once proven, expand to more complex cases and departments.

It’s okay to run proof-of-concept pilots. In fact, that’s the recommended approach. The key is to treat the agent as a collaborator—one that needs clear inputs and thoughtful guidance.

At Astreya, we specialize in guiding enterprises through this transformation. Our expertise in Digital Workplace solutions and proprietary AI frameworks help clients maximize their ServiceNow investment from day one. Whether you’re assessing readiness or looking to build your first agent, our team can help map, implement, and scale your AI agent strategy effectively.

Set up a free session with us to explore where agentic AI fits in your business and how to accelerate time-to-value with proven frameworks and experienced practitioners.

Final thoughts

As ServiceNow put it, “You cannot solve 21st-century challenges with 20th-century architectures.”

Agentic AI is a new way of working. It challenges old assumptions about who (or what) does the work, how processes are built, and what technology should be capable of.

It requires rethinking more than retooling. But for those ready to embrace it, the reward is a scalable, adaptable, self-improving enterprise that can meet challenges faster, serve customers better, and operate with unmatched efficiency.

The future isn’t just automated. It’s agentic, and it’s already here.

Source: Mastering AI for Customer Service Implementations with Fernando Castro, Senior Staff Outbound Product Manager-Customer Service AI.