What Is Agentic AI? AI Agents Explained

A handful of generational technologies—those with the potential to transform how business is done—have come along in the last few decades. The World Wide Web, cloud computing, and smartphones are on that short list. Generative AI, or GenAI, is inarguably in that category. And although all these technologies improved worker productivity, few fundamentally changed how businesses operate.

Agentic AI is different.

Although it relies on the same underlying LLM-based foundation, agentic AI autonomously acts based on its own decisions and observations, while GenAI typically relies on human interaction to start and complete a task. Agentic AI is so promising that, according to an assessment from McKinsey, “at [their] current levels of capability, AI agents could perform tasks that occupy 44 percent of US work hours today.”

Based on that extraordinary potential alone, organizations that delay adoption risk falling behind competitors and startups already putting agentic AI to work.

What Is Agentic AI?

Agentic AI refers to a platform or collection of enabling technologies that make it possible to create AI agents, describe the business conditions that trigger them into action, choreograph their activities if multiple agents must collaborate to complete a complex business process or workflow, and put them to work on behalf of you or your business.

Think of how a real estate or travel agent takes direction from you using natural language, then acts autonomously in your best interests, sometimes tackling tasks that weren’t specifically requested based on their own knowledge and instincts. In some cases, human agents collaborate to see a task through to its conclusion. AI agents work in much the same way, autonomously tackling repetitive business tasks and processes while at the same time considering shifting conditions before acting. For example, an AI agent can be instructed to use predictive analytics to anticipate supplier disruptions due to regional factors, such as weather or geopolitical events; source affected parts from a cost-friendly substitute; and update your inventory and financial systems accordingly, all without human intervention.

AI agents can act with full autonomy, or they can be programmed to keep humans in the loop for some or all decisions.

Key Takeaways

• Agentic AI offers businesses of all sizes an unprecedented opportunity to automate tasks and processes.
• Unlike generative AI, which simply responds to prompts in pursuit of one-time objectives, goals persist in agentic AI, and AI agents repeatedly pursue those goals until instructed otherwise.
• Agentic AI builds on GenAI, using large language models as the cognitive engines that supply the reasoning and natural language capabilities necessary to create and launch agents into action.
• Multiagent systems tackle complex workflows by coordinating specialized agents through an orchestration layer that manages sequencing, parallel execution, and shared context.
• Agentic AI is most powerful when embedded in systems where it can access operational data and understand business context.

Agentic AI Explained

Agentic AI can be viewed as a sophisticated successor to business process automation (BPA) tools. With BPA, static automations are chained together to automate end-to-end business functions, but these automations are brittle and thus easily broken by unexpected conditions. Agentic AI’s potential to unlock new levels of productivity stems from its ability to combine automation with AI-driven decision-making that can handle curveballs.

Moreover, it’s accessible to a range of users. Because agentic AI relies on the same natural language processing (NLP) foundation as GenAI-driven chatbots, it doesn’t take an expert to create, train, and launch an AI agent to automate a repetitive task. The “artificial intelligence” part of “agentic AI” means that the agent understands the intent of your automation to the point that it can, with little or no human intervention, make multistep decisions and dynamically adapt its logic to deal with unanticipated events and business conditions.

Another key ease-of-deployment benefit of agentic AI is its inherent ability to integrate with existing business systems of record, from spreadsheets and inboxes to comprehensive CRM and ERP systems. However, reliance on this superpower should always involve oversight from your company’s security team, which can check that integrations follow corporate policies.

Agentic AI vs. Generative AI

Operationally, GenAI and agentic AI are two different interaction contexts for working with foundational AI technologies, such as LLMs and NLP.

With GenAI, goals are largely ephemeral; they are achieved one at a time in very synchronous fashion. GenAI solutions like OpenAI’s ChatGPT and Anthropic’s Claude typically wait for instructions from a human or machine before generating a response. For example, provided your GenAI tool has access to your inbox and online calendar, you can prompt it to send an email or book an appointment. But once GenAI completes its task, it waits for the next instruction.

In contrast, goals persist over time with agentic AI. You can create an agent that autonomously reads your emails as they arrive, automatically books necessary appointments based on the content of those emails, and then cleans up your inbox. As it does this, it can take other factors into consideration. For example, if you tell it you don’t like to sit in heavy traffic, it will not only optimize appointment times to that avoid rush hour, it can also spot unexpected traffic incidents and replot driving instructions or rearrange your schedule altogether.

With agentic AI, the level of autonomy is up to you. Using the previous example, if you prefer to approve every email before one is automatically sent, you can create an AI agent that drafts the email, puts it into your drafts folder, and then reminds you that the email is waiting for you to click the send button. Or you can give it complete autonomy in the same way you might give such autonomy to a trusted employee.

For these reasons, GenAI is frequently perceived to be reactive while agentic AI is perceived to be proactive. The more your business’s technology is proactively acting on your behalf, the more you’re completely reinventing the operation of your business.

How Agentic AI Works with Existing Systems of Record

You’ve spent years, maybe decades, feeding data into your ERP, CRM, HR, and other business systems of record. Now that data is perfectly positioned to help you unlock the full potential of agentic AI.

Virtually every agentic workflow will need access to one or more of your systems of record, as well as to external resources. Maybe you’ll create agents to help your support team better manage their tickets. Or maybe a team of AI agents will work in concert with one another to optimize your supply chain for cost, speed, and contractual compliance.

Many of your providers will be hip to the potential and not only make it possible for agentic AI to access their data, but have some agentic capabilities directly in their systems. But, in cases where the vendor of a system record doesn’t explicitly provide a means of programmatic access for agentic or GenAI, such as support for Model Context Protocol (MCP), there are alternatives.

Agentic AI tools are adept at finding the best way to connect. If a system of record is popular, an agentic AI tool may have its own custom connectors and prioritize them as the preferred integration method.

If that’s not the case, it typically looks for MCP support. Failing that, it usually looks for a REST API. REST APIs have long been the standard for how most systems of record offer their data and functionality for consumption by other systems. However, they’re not always as optimized for AI-driven applications as custom connectors and MCP are.

Most providers offer MCP support, APIs, or both. NetSuite offers an API called SuiteTalk (opens in new tab) and the NetSuite AI Connector Service for MCP.

In cases where a system of record doesn’t advertise one of the aforementioned integration method, an agentic AI tool will explore other possible means of connection. For example, it might discover that a system of record is powered by a database whose data is accessible through a query language like SQL; NetSuite’s underlying database is accessible via SuiteQL, for example. Such query language-based integrations could make sense if your organization used a database to build a custom application that’s inaccessible via API or MCP.

Lastly, if the system of record involves a web front-end, the agentic tool might rely on robotic process automation (RPA) to emulate a human by robotically clicking around a website to discover and update data.

Regardless of which path your agentic AI tools take to integrate with your systems of record, security will be of the utmost concern. Your security team will consider the means through which the organization’s systems of record issue and revoke permissions for agentic AI access, how the identities and activities of AI agents are tracked, and what measures have been taken to secure the AI agents themselves from unauthorized access or manipulation.

How Does Agentic AI Work?

At a base level, agentic AI systems work by running through five logical steps as many times as necessary to achieve their goals. In practice, these steps may unfold in different sequences, with decisions being made in all of them.

In addition to the five sequential steps, agentic AI systems tackling more complex business processes have an orchestration layer to coordinate the activities of multiple agents working together. Here’s what happens during each of the main steps:

1. Perception: The AI agent gathers information or, as AI researchers like to say, it “perceives its environment.” This means it collects data from databases, APIs, documents, emails, sensors, and user inputs. The agent then interprets this data to appraise the current situation and identify what it’s supposed to do and how it can accomplish that goal.
2. Reasoning: Using its LLM as a cognitive engine, the system analyzes what it perceived, spotlights what matters, and determines what the data means in context. It draws inferences, recognizes patterns, and connects information from different sources.
3. Planning: Some AI researchers like to call this “decomposition.” The agent must break down its overall goal into a sequence of smaller steps for the software to execute. Rather than following a fixed script, it figures out what needs to happen next based on the goal, current context, and what it’s learned so far.
4. Decision-making: The system evaluates its options, weighs tradeoffs, and chooses actions, including determining when to ask for human help. Well-designed agents operate autonomously within defined boundaries but escalate when confidence is low, stakes are high, or the situation falls outside their authority.
5. Reflection: The agent evaluates the results and considers any human feedback. Did its actions achieve what it intended? If not, it learns from the outcome and adjusts its approach for the next attempt. This self-correction is what allows agentic AI to improve over time rather than repeat the same mistakes.
6. Orchestration: More complex systems must coordinate the work of multiple specialized agents that share information and hand off tasks as they collaborate toward a common goal. An orchestration layer manages this coordination, guiding agents to work sequentially or in parallel, as needed, maintaining shared context and escalating when appropriate. A simple business process might rely on a single agent, but enterprise systems performing multistep processes might deploy separate agents for data extraction, validation, recordkeeping, and other tasks, all coordinated by an orchestrator.

Types of Agentic AI Systems

As previously noted, agentic AI can be deployed as a single agent or as multiple agents working together. The right architecture depends on the complexity of the work, how many systems and data sources are involved, and whether it’s more efficient to run tasks sequentially or in parallel.

Single-Agent Systems

As the name implies, a single AI agent handles end-to-end tasks within a defined domain. A single-agent system perceives, reasons, plans, decides, and reflects independently, looping through that cycle as often as necessary until the goal is met or it needs to escalate to a human.

Single-agent systems are simple to implement and easy to govern. They work best for tasks that are well-defined, contained within one functional area, and don’t require cross-functional coordination. Common use cases include IT help desk ticket resolution, invoice processing, and individual customer support interactions. But single-agent systems can struggle with tasks that require diverse expertise or coordination of multiple business functions.

Multiagent Systems

When tasks grow more complex, multiple specialized agents can collaborate. An orchestration layer coordinates their efforts to manage sequencing, parallel execution, and shared context. Think of it as assembling of a project team: Rather than asking one generalist to handle everything, you bring together specialists who each contribute their expertise. Multiagent systems work the same way—a research agent gathers information, a writing agent drafts content, and an editing agent refines it. To the extent that different end-to-end workflows might involve the same subtask, for example, reserving inventory in an ERP system, a common pattern in multiagent systems is to create a single agent blueprint for that subtask and then reuse it as necessary across multiple agentic workflows.

Multiagent systems come in two main architectures:

Vertical Multiagents

The orchestrator agent delegates tasks to specialized subagents in a top-down hierarchy. The orchestrator decomposes the goal, assigns subtasks, monitors progress, and synthesizes results. Worker agents report back to the manager. This suits complex business processes that have clear authority structures and sequential handoffs. In a month-end financial close, for example, an orchestrator might coordinate separate agents for journal entries, reconciliations, variance analyses, and report generation.

Horizontal Multiagents

Here, agents of equal standing collaborate as peers, without a strict hierarchical manager. Each contributes its expertise, and they coordinate through such mechanisms as voting, where multiple agents evaluate the same question and must agree, or deliberation, where agents exchange reasoning and critique each other’s work. In practice, though, horizontal systems may still have a lightweight coordination layer. This model works well for problems requiring diverse perspectives or parallel analyses, such as responding to a supply chain disruption where procurement, logistics, and inventory agents might each assess the situation from their individual viewpoints and then coordinate on a response.

8 Agentic Use Cases

Agentic AI is finding traction across a wide range of business functions—anywhere high-volume tasks combine with multisystem data to fulfill a need for consistent, accurate, fast, and proactive execution. These eight use cases illustrate how organizations are deploying agents to take over the routine work while humans focus on judgment and exceptions.

1. Customer Service and Support Operations

   Consulting firm Deloitte expects agentic AI to have its earliest and most visible impact on customer service and support. Characteristics like high volumes of repetitive interactions, multisystem lookups, and the need for consistent and fast resolutions are well suited to autonomous agents. Deloitte notes that one airline is already “using AI agents to help customers complete the most common transactions, such as rebooking a flight or rerouting bags, freeing up time for human agents to address more complex matters.”

   Use cases

   • Autonomous tier 1/2 ticket resolution: AI agents handle routine inquiries, such as password resets, order status, or billing questions, from intake through resolution. When a car rental customer reports a breakdown, for example, an AI agent can retrieve the rental agreement, check warranty or replacement policy terms, create a return authorization, generate a replacement order, and send the customer an email with procedural instructions—all on its own, logging every action
   • Proactive outreach agents: Agents monitor systems and proactively identify and resolve customer issues. An agent might detect that a shipment has been delayed due to weather, then automatically notify the customer and offer alternatives.
   • Voice-to-action: These agents go beyond transcribing customer service calls to actually executing actions like initiating a refund or changing account settings during or after the call.
   • Agent copilots: Agentic systems assist human agents in real time by suggesting responses, performing knowledge retrieval, and initiating next-best-action recommendations. Copilots help human reps work faster and more consistently by placing relevant information from knowledgebases on their screens, drafting responses for review, and auto-populating case notes after customer interactions.
   • Post-interaction quality assurance: Agents can automatically review completed interactions for compliance, accuracy, and quality, then generate coaching recommendations for human agents.

   Benefits

   • Operational costs: Agentic AI customer service can reduce the cost per interaction.
   • Call volume: AI agents can reduce the call volume human reps must handle by “deflecting” callers to self-service resolution. In many cases, the need to open a support ticket is avoided.
   • Resolution times: First response times can drop from hours to minutes. In one study, resolution times dropped from 32 hours to 32 minutes.
   • Productivity value: Joint research from Stanford University and the Massachusetts Institute of Technology found that AI assistance increased overall support agent productivity by 14%, measured by issues resolved per hour. The largest gain—34%—was among newer workers; highly skilled and experienced workers saw negligible improvement.

2. Sales Pipeline and Revenue Generation

   Drawing from massive prospecting databases to perform repetitive outreach and qualification is time-intensive and subject to human inconsistency and error. Agentic AI systems are indefatigable. They can operate around the clock without loss of accuracy, consistently apply qualification criteria, and personalize each engagement, freeing human sellers to focus on relationship-building and “ABC”—always be closing. McKinsey’s 2025 global study on the state of AI found that 67% of respondents who used AI in sales and marketing credited it with increasing revenue.

   Use cases

   • Multitouch SDR agents: AI sales development representatives (SDRs) autonomously prospect, engage, and qualify leads across email, chat, social, and voice channels. Sales automation is not new, but before agentic AI, it could only follow predefined rules and was incapable of adjusting to unanticipated conditions. An AI agent can dynamically adapt its approach in keeping with prospect behavior. It can switch channels when a lead doesn’t respond, adjust messaging based on engagement signals, and maintain context across channels and interactions so prospects don’t have to repeat themselves.
   • Lead scoring and routing: AI agents dynamically prioritize leads and route them to the right rep based on scoring criteria and behavioral signals, such as website engagement, content consumption, social activity, firmographic data, and intent indicators. High-intent buyers get immediate attention and lower-priority leads enter nurture sequences.
   • Proposal and RFP generation: Agents gather data, customize proposal templates, and generate tailored responses to requests for proposals by pulling from knowledgebases, CRM history, pricing guidelines, and competitive intelligence. For complex deals, agentic workflows can route proposals through pricing analysis, discount approval, legal review, and delivery—with each step handled by specialized agents or escalated to human experts, as appropriate.

   Benefits

   • Revenue: Through faster, personalized, and consistent engagement, AI agents can expand pipeline coverage and improve conversion rates. Faster lead response is particularly helpful because the steep falloff in conversion rates with each hour or day of delay after an initial contact is a well-documented sales phenomenon.
   • Sales productivity: With AI agents handling time-consuming groundwork, the shift from administrative burden to relationship-focused selling and closing can reclaim hours per week for each rep while improving win rates.
   • Sales ROI: Pipeline generation becomes more predictable and cost-efficient. AI agents engage more prospects than a human team can while maintaining quality through personalization and persistence. The result: higher-quality opportunities at a lower cost per lead.

3. Finance, Accounting, and Procurement Automation

   Finance processes are document-heavy, rules-intensive, multisystem, and time-sensitive—especially when it comes to month-end closes, audits, and compliance. In other words, agentic AI’s speed., scalability, and accuracy make it perfect for repetitive accounting tasks. Despite, or maybe because of, significant investments in digitization and automation, many finance functions are reaching what PwC calls “terminal value,” where their cost efficiencies are peaking, the team’s capacity is stretched, but the organization’s expectations keep rising. This predicament can arise at any scale, from a simple P&L reconciliation to enterprise AP departments that process thousands of invoices per month. According to PwC’s AI Agent Survey, 79% of executives say their companies are already adopting AI agents, but only 34% are using them in accounting and finance—a gap that represents untapped potential.

   Use cases

   • Autonomous invoice processing: AI agents extract data from invoices, match invoices against purchase orders (POs), route purchase orders through approval workflows, and schedule payments. A finance specialist needs to weigh in only if an exception or an escalation point is reached. Multiple agents can work together: One extracts key fields, a second retrieves the relevant contract, a third compares invoice details to contract terms and flags discrepancies, and a fourth drafts vendor communications.
   • Month-end close agents: These are multiagent workflows in which specialist agents handle journal entries, reconciliations, intercompany eliminations, and variance analyses, with an orchestrator managing sequencing, dependencies, and deadline tracking. Agents can continuously process accounts and highlight discrepancies throughout the period, avoiding those frantic, end-of-month pushes.
   • Procurement: The AI agent monitors contracts, evaluates supplier performance, identifies savings opportunities, and initiates POs triggered by inventory thresholds and demand signals. When invoice discrepancies reveal recurring overcharges or contract term issues, agents can flag patterns for renegotiation.
   • Expense audit agents: These agents review expense reports for policy violations, duplicated submissions, and fraud indicators. They automatically approve compliant submissions and flag exceptions for human review.
   • Working-capital optimization: Agents analyze payment terms, cash flow forecasts, and early payment discount opportunities to recommend payment timing that optimally balances discount opportunities with liquidity needs.
   • Fraud detection: Agents continuously monitor transaction flows to identify suspicious activity before payments are processed. By leveraging cross-enterprise fraud databases and anomaly detection, these systems can intercept duplicate or counterfeit invoices.

   Benefits

   • Invoice processing time: Agentic workflows can dramatically shorten invoice processing cycles. Exception backlogs shrink as agents automatically resolve simple errors and route only novel cases to human specialists. PwC’s analysis shows that using AI agents for invoice processing and PO-matching can slash cycle times by up to 80%, improve audit trails, decrease compliance issues, and foster growth without added cost.
   • Operating costs: By automating tasks that are part of the procure-to-pay and record-to-report cycles, finance teams can shift from processing to performance. PwC reports potential time savings of up to 90% in key processes, with up to 60% of team time redirected to higher-value work. The result isn’t just efficiency—it’s new capacity to focus on vendor performance management, contract optimization, and strategic planning.

4. Operations, Help Desk, and Cybersecurity

   AI agents can help companies make sense of complex infrastructure while dealing with staffing shortages and alert fatigue. Cybersecurity alone is suffering from 4.8 million unfilled staff positions worldwide, according to the International Information System Security Certification Consortium. That helps explain why Deloitte’s “State of AI in the Enterprise” report for 2026 found that 23% of IT leaders are already using agentic AI moderately, extensively, or fully integrated into their operations, and 74% expect to do so within two years. The report notes that cybersecurity is among the highest-potential domains for agentic AI.

   Use cases

   • IT service-desk agents: As in customer service, IT agents autonomously handle Level 1 and Level 2 support requests, such as password resets, access provisioning, and software troubleshooting. Unlike rule-based chatbots that follow static decision trees, service-desk agent troubleshooting is an iterative process. When an employee reports, “My Wi-Fi keeps dropping,” the agent gathers details, runs diagnostics, adapts its approach as it reviews results, and either resolves the issue or escalates it, with a diagnostic summary, to a human technician, saving the technician from having to start over.
   • Infrastructure monitoring: Agents continuously monitor servers, networks, and cloud resources, detecting anomalies, correlating alerts, performing root cause analyses, and initiating remediation. Forrester predicts that at least one major enterprise outage will be prevented by an agentic AI workflow acting autonomously in 2026.
   • Security operations: AI agents work in security operations centers (SOCs), triaging automated alerts and investigating and responding to incidents. They can decode malicious scripts, correlate threat intelligence, query indicators of compromise, and guide analysts through containment and remediation.
   • Patch management: Agents identify vulnerable systems, prioritize patches by risk level, schedule deployment windows to minimize disruption, and validate successful updates. They replace periodic manual reviews with always-on protection.
   • Multiagent onboarding systems: Coordinated agents are able to provision accounts, configure workstations, set up access permissions, and guide new employees through setups. Rather than a new hire waiting days for access while tickets bounce between teams, multiple agents work in parallel to have everything ready on day one.

   Benefits

   • Cost reductions: Savings accumulate from service-desk agents resolving routine tickets without human intervention, monitoring agents that catch issues before they cause downtime, and security agents filtering alert noise so analysts can focus on real threats.
   • Productivity: Automating routine tickets and alert triage can allow IT teams to redirect their expertise toward strategic initiatives. For example, organizations already using AI agents to assist in SOCs report slashing their mean time to resolution (MTTR) by nearly one-third.
   • ROI: The return on investment comes from both direct cost savings and risk reduction. Faster threat detection and response can mean less damage from security incidents, while automated service desk resolution can result in fewer hours spent on repetitive tickets.
   • Cycle times: Use cases compress the time elapsed from problem to resolution, improving MTTR, mean time to detect, ticket turnaround time, patch deployment cycles, and incident response. The result: faster detection, faster response, faster recovery.

5. Supply Chain and Inventory Management

   Supply chains are subject to constant disruption from demand volatility, geopolitical events, and extreme weather. Traditional management approaches can’t adapt fast enough when conditions shift. Deploying agentic AI in supply chains can facilitate real-time decision-making in forecasting, procurement, logistics, and inventory. Agents can detect disruptions as they unfold and act within their defined limits to reroute shipments, adjust orders, or alert procurement teams before a delay cascades throughout the operation.

   Use cases

   • Dynamic forecasting: Agents can analyze data from Internet of Things devices, point-of-sale systems, market trends, and external signals, such as weather and local events, to produce demand forecasts and update them continuously. They learn and adjust as conditions change, weighting new factors that prove relevant and discounting those that aren’t.
   • Supplier monitoring: Agents track supplier performance, financial health, and other risk indicators by drawing from multiple data sources. When an early warning sign emerges, such as a delayed shipment or a credit downgrade, the agent alerts procurement staff, giving them time to shift sourcing.
   • Logistics orchestration: AI agents can optimize routing, carrier selection, and delivery scheduling in real time. These agents incorporate external data, such as weather forecasts, fuel prices, port congestion, and traffic conditions, to adjust shipments and routes. When disruption strikes, they can calculate the economics of alternatives, such as rerouting, switching carriers, or adjusting timing, and recommend or execute the best option.
   • Planning agents: Orchestration-level agents coordinate procurement, manufacturing, warehousing, and distribution. Suppose an operations manager asks how to reduce manufacturing costs by 5%. In response, the planning agent gathers data from suppliers, production systems, and logistics partners to identify promising options, such as switching materials, changing suppliers, or moving assembly locations.

   Benefits

   • Inventory carrying costs: Better forecasting can let companies carry less safety stock without increasing stockout risk. Agents continuously optimize reorder points based on actual demand signals, not static thresholds, reducing both overstock and the warehouse space needed to hold it.
   • Stockout reductions: By predicting demand more accurately and responding faster to supply disruptions, AI agents help make sure products are available when customers want them. During a promotion, for example, an agent can align the demand surge with logistics constraints to reallocate inventory among warehouses and adjust carrier bookings to prevent stockouts.
   • Logistics costs: Route optimization, carrier selection, and load consolidation decrease transportation spending. Agents that monitor conditions in the moment can avoid expedited shipping costs by anticipating problems and rerouting shipments.

6. Marketing Pipeline and Demand Generation

   Marketing is an early adopter of agentic AI, driven by complexity. Today, a marketing campaign might require managing as many as two dozen digital channels, personalizing content for microsegments, and analyzing signals across the entire buyer journey to optimize spending in real time. Humans can’t do that at scale, but agentic AI can quickly build and launch campaigns, sequence actions, adjust performance levers, and reallocate budget.

   Use cases

   • Content review and scheduling: Agents analyze content performance across channels, identify gaps in coverage, generate or curate assets to fill those gaps, and schedule publication based on audience behavior patterns.
   • Campaign optimization agents: AI agents monitor campaign performance and reallocate budget, adjust targeting, or modify creative elements, as and when needed. When an ad’s click-through rate degrades, an agent can swap in fresh creative from the asset library. When one channel outperforms another, it can shift budget to capture the opportunity.
   • ABM agents: Account-based marketing agents can identify high-value accounts based on fit criteria, then enrich that account data with intent signals—website visits, content consumption, third-party research activity, funding rounds, or key hires. With this type of up-to-the-minute picture of an account’s stakeholder activities, the agent executes personalized outreach across email, LinkedIn, and other channels. It adapts messaging as new stakeholders engage or buying signals change or intensify. The result is continuous, adaptive engagement, rather than periodic campaign bursts.
   • Web performance: These agents don’t merely report that a page has a high bounce rate; they monitor site performance, identify conversion bottlenecks through behavioral analysis, test variations, and implement optimizations.
   • Customer journey: Agents track individual customer journeys at all touchpoints to trigger the right engagement at the right moment. When a prospect visits the pricing page after opening several emails, for example, the agent recognizes that as a high-intent signal and accelerates the follow-up sequence. For prospects demonstrating minimal engagement, it shifts to a longer-term nurturing approach.

   Benefits

   • Customer acquisition costs: The cumulative effect of better targeting, optimized spending, and personalized engagement is lower customer acquisition costs. One AI vendor says its agentic AI marketing solution lowered this spending by up to 80%.
   • Marketing automation ROI: Traditional marketing automation executes predefined sequences, whereas agentic systems adapt in real time. The difference shows up in results: Agents that continuously optimize outperform static workflows because they respond to what’s actually happening, not what was planned weeks ago.
   • Conversion rates: Personalization at scale drives higher engagement. When outreach is tailored to each prospect’s behavior, industry, and timing, open rates and response rates climb. More important, conversion rates rise when agents prioritize fit and intent signals over raw volume.

7. Human Resources and Talent Management

   For growing companies, HR often operates lean—a small team juggling recruiting, onboarding, benefits administration, and employee questions. Agentic AI helps these teams punch above their weight by automating back-end tasks so they can focus on the people-centered decisions that require human judgment.

   Use cases

   • Recruitment: An agent sources candidates across job boards and LinkedIn, screens résumés per your criteria, and coordinates interview schedules with busy hiring managers. For a company hiring 20 to 30 people a year, this means your HR generalist isn’t spending half their week on logistics. The agent handles the pipeline; your team handles the conversations that matter.
   • Onboarding: When a candidate accepts an offer, an onboarding agent triggers downstream tasks—IT gets a ticket for laptop and software access, payroll receives the new hire’s information, and the hiring manager gets a planning checklist. For smaller companies where onboarding sometimes falls through the cracks, this coordination can make a material difference in terms of retention and time-to-productivity metrics.
   • Self-service agents: Employees expect quick answers to questions about benefits, paid time off, and company policies. A self-service agent handles these inquiries around the clock, freeing HR from being a human FAQ. Better yet, it can execute actions, including submitting a time-off request, adding a beneficiary, or routing a complex question to the right HR staff member with full context attached.
   • Retention risk analysis: Agents monitor employee engagement signals, performance data, and market conditions to identify employees at risk of leaving, so managers have a chance to intervene. You may not have the people analytics team that a Fortune 500 has, but an agent can provide some of that visibility without the headcount.

   Benefits

   • Talent acquisition and retention: Faster hiring cycles and better employee experiences have a compounding effect. Faster, smoother hiring processes help organizations compete for talent. And when onboarding and employee experiences are good and consistent—not dependent on which manager happens to be most organized—you’re more likely to keep the people you hire.
   • Recruitment cycle times: Speed matters in hiring. A two-week delay can mean losing your top candidate to a faster-moving competitor. Agentic workflows compress the time from résumé to offer by eliminating much of the back-and-forth that slows the process down. Instead of spending hours coordinating calendars and chasing approvals, your team can focus on evaluating fit and selling candidates on the opportunity you can provide them.

8. Knowledge Management and Decision Support

   Institutional knowledge tends to fragment as organizations grow, becoming scattered across SharePoint, email threads, Slack channels, shared drives, and the minds of long-tenured employees. People spend hours searching for information they know exists somewhere, or, worse, re-create work that’s already been done. Agentic AI addresses this by navigating, synthesizing, and presenting knowledge at the point of need—turning fragmented information sources into something closer to a unified, searchable repository.

   Use Cases

   • Enterprise knowledge agents: These agents simultaneously search all company repositories—documents, WIKIs, email, chat history—and synthesize what they find into actionable answers with source citations. Instead of returning a list of links and leaving the employee to sort through them, the agent pulls together relevant information from contracts, policy documents, and Slack threads to actually answer the question.
   • Document-workflow agents: Documents have lifecycles, from creation to routing, review, approval, versioning, and archiving, and managing those lifecycles manually is tedious and error-prone. Document-workflow agents classify incoming documents, route them to the right reviewers, track approvals, flag compliance requirements, and archive them according to the company’s retention policies.
   • Onboarding and training copilot agents: New employees typically face a firehose of information in their first weeks, with systems to learn, policies to absorb, and relationships to build. Onboarding copilot agents guide them through role-specific learning paths, answer questions in context, and adapt recommendations to the pace of their progress. The agent handles routine knowledge transfer, freeing managers to focus on questions that require human judgment and relationships.

   Benefits

   • Knowledge management ROI: Productivity rises when people spend less time hunting for information. When employees get answers in seconds, instead of digging through folders and chat history, savings compound quickly. Organizations also report less duplicate work because enterprise knowledge agents can spot existing work products before new projects begin.
   • Employee enablement: By handling routine information retrieval, agentic AI frees employees to focus more on judgment, creativity, and relationship-building—the work that requires human intelligence. Meeting follow-through improves when agents capture, summarize, and distribute action items automatically. New hires ramp up faster when they can quickly get answers to role-specific questions. The net effect is that people spend less time on information logistics and more time on the work they were hired to do.

Embedded AI Capabilities, Now in NetSuite

Companies looking to adopt agentic AI often run into a big, practical challenge: The AI needs access to operational data to be useful, but that data lives in spreadsheets and disconnected systems—sales, CRM, HR, billing. NetSuite ERP embeds AI capabilities directly into its suite to provide a single, unified view of finance, operations, and customer data. And, NetSuite embeds a conversational AI interface for querying data and triggering workflows through natural language, autonomous close capabilities that monitor financial processes and flag exceptions throughout the month, and narrative insights that explain what’s driving metric changes—with visible reasoning so finance teams and auditors can see how the AI reached its conclusions. Organizations that want to go further can create custom AI agents tailored to their own processes and compliance rules, without requiring separate AI infrastructure.

Because AI actions operate under the same role-based controls and approval workflows as any other user, organizations get the benefits of agentic AI without sacrificing the governance that auditors and boards expect.

Agentic AI represents a fundamental shift in how businesses operate, progressing from systems that follow instructions to systems that act on goals. As a result, we see agentic capabilities moving from pilot projects to production workflows in virtually every business function, whether automating customer service triage, accelerating the financial close, or formulating insights from fragmented knowledge. Organizations that build the right data foundations and governance structures now will be positioned to capture these benefits as agentic technology matures.

Agentic AI FAQs

Why is it called agentic AI?

The term “agentic” derives from “agency,” or the capacity to act independently and make choices. AI researcher Andrew Ng popularized the term in 2024 to distinguish AI systems that can take autonomous action from those that merely generate content in response to prompts.

What is an example of agentic AI?

A simple example is an accounts payable AI agent that receives an invoice, extracts relevant data, matches it to purchase orders, flags discrepancies for review, routes approvals based on amount thresholds, schedules payment according to terms, and updates the general ledger. The agent perceives the incoming document, ferrets out what needs to happen, and executes a multistep workflow autonomously.