Introduction
Many businesses already use AI to draft emails, summarise reports and prepare content. The next step is to connect that assistance to everyday operations, where information needs to lead to a quote, a purchase request, a customer response or another completed task.
Understanding the difference between Agentic AI and Generative AI helps business leaders assess where each approach fits. Generative AI helps people create and interpret information. Agentic AI adds the ability to plan and carry out actions towards an objective, within defined controls.
Neither technology replaces the other. Their combined value becomes clearer in Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems, where reliable data, established processes and human accountability matter as much as speed. This article explains the distinction, practical business applications and how Microsoft Dynamics 365 brings these capabilities into operational workflows.
What Is the Difference Between Agentic AI and Generative AI?
Generative AI creates and analyses; Agentic AI plans and acts. The key difference is whether the system primarily produces information or uses information to pursue an objective through actions.
Generative AI might summarise a customer’s purchase history and draft a follow-up email. An agentic system could use that summary, check outstanding actions, create a follow-up task and update a permitted CRM record. It would pause for approval wherever the organisation’s rules require it.
These are overlapping capabilities, rather than rigid product categories. A generative model can form part of an agentic system, and a conversational assistant can invoke tools or workflows. Equally, a process with several automated steps is not necessarily agentic: conventional workflow software can already execute complex sequences. Agentic AI adds goal-directed interpretation, planning and adjustment within the scope it has been given.
Agentic AI vs Generative AI – Quick Comparison
Aspect | Generative AI | Agentic AI |
|---|---|---|
Primary Purpose | Create and interpret information | Plan and act towards a defined objective |
Typical Output | Drafts, summaries, explanations and other content | Completed tasks, updated records or work ready for approval |
How Work Starts | A user request or an application trigger | A user-defined goal or a configured business event |
Use of Systems | Uses available context to generate relevant output | Uses permitted tools and applications to progress work |
Human Involvement | People validate content and decide how to use it | People set boundaries, supervise and approve specified actions |
Response to Change | Produces revised content from new context | Can adjust the next step, seek help or stop within its rules |
Business Example | Draft a customer briefing or explain a report | Prepare a quote and coordinate its review |
Best Fit | Work centred on creating or understanding information | Work requiring authorised actions and coordination |
Capabilities depend on the application and configuration; a single solution can combine both approaches.
What Is Generative AI?
Generative AI produces content such as text, images, code, audio or video, depending on the model and application. In business settings, its most useful outputs often include email drafts, document summaries, product descriptions and explanations of information that would otherwise take time to review.
It can turn a lengthy customer conversation into a concise briefing or help a manager compare the issues raised in several reports. The result is generated content that people can use, refine or validate. It should not be treated as a verified record simply because it reads fluently.
Generative AI is commonly accessed through conversational assistants and features embedded in business software. Although users often initiate it with a prompt, an application can also trigger generation automatically. Sending a generated message or changing a business record requires the surrounding application, tools and permissions to support that action.
How Does Generative AI Work?
At a business level, Generative AI takes a request and relevant context, then produces a response. For example, a sales manager might ask for a short account briefing before a customer meeting. An appropriately connected application can provide permitted customer records and recent interactions to help make the briefing relevant.
The quality of that response depends on the information available, the clarity of the request and the application’s design. An instruction such as “Summarise this account’s open opportunities, unresolved service issues and agreed next steps” gives the system a more useful purpose than “Tell me about this customer”.
Connecting the response to trusted business information, often called grounding, helps it reflect the organisation’s actual circumstances. It does not guarantee accuracy. Missing records, outdated information or ambiguous wording can still lead to omissions or unsupported conclusions.
The practical workflow is therefore straightforward: provide an objective and relevant information, generate a draft or explanation, check it against the source, and use the approved result. For figures and calculations, established reporting tools and business rules should remain the basis for verification.
Key Capabilities of Generative AI
1. Content Creation and Refinement
Generative AI helps employees move from a blank page to a workable draft. Marketing teams can prepare product descriptions, sales teams can develop proposals, and HR teams can draft onboarding materials. People then refine the content for accuracy, audience and company tone. It can also produce alternative versions without requiring the writer to start again.
2. Summarisation and Information Analysis
It can condense reports, meeting notes and customer interactions into clearer summaries, highlight recurring themes and organise information for review. This is useful when employees need to understand a situation quickly. The summary should preserve important qualifications and point users back to underlying records when detail matters.
3. Natural Language Interaction
Employees can ask questions in everyday language rather than knowing where every field or report sits. Within a suitably connected application, this can make business information easier to access and explain. The usefulness of an answer still depends on the data the system can retrieve and the access rights of the user.
4. Adaptation Across Formats and Audiences
The same source material can support a management briefing, a customer explanation or a training draft. Depending on the platform, Generative AI may also assist with translation, images or code. These capabilities help teams reuse information, although specialist review remains necessary where technical accuracy or cultural nuance is important.
What Is Agentic AI?
Agentic AI describes an approach in which AI works towards a goal by planning steps, using available tools and responding to the results. Its defining characteristic is the connection between reasoning and action. Instead of only suggesting what someone could do, the system can carry out permitted parts of the work.
An AI agent is a software implementation that can use some degree of these capabilities. One agent might perform a narrow task, while another coordinates a multi-step workflow. The label alone does not tell a business how much independence, integration or judgement the software has.
Agentic AI can operate with varying levels of autonomy depending on permissions, rules, controls and approval requirements. A system may be allowed to retrieve information and prepare drafts independently, but need human approval to release a purchase order or send a commercially sensitive message.
For example, in an appropriately configured environment, a procurement agent could identify a replenishment need, review approved supplier information, prepare a purchase request and submit it to the responsible manager. Budget checks, supplier approval and purchasing authority would still govern whether the request proceeds.
How Does Agentic AI Work?
The source of a task may be a user’s instruction or a business event, such as an incoming enquiry. The following cycle explains how an agentic system can move from that starting point to a controlled outcome.
1. Understand the Objective
The system interprets the intended result and relevant constraints. “Prepare a replenishment request for these items” should be accompanied by clear boundaries, such as approved suppliers, stock policies and spending limits. Where information is missing, the appropriate next step may be to ask for clarification.
2. Plan the Work
It identifies the steps and information needed to progress. For procurement, these might include checking stock, considering existing orders and reviewing supplier terms. The plan must stay within the tools and actions that the organisation has authorised.
3. Act Through Connected Systems
The agent retrieves data or performs permitted actions in business applications. It might create a draft document, update an allowed field or pass work into an established approval process. Planning an action does not grant permission to execute it.
4. Monitor the Result
After acting, the system checks whether the step succeeded and whether the objective remains achievable. A rejected update, unavailable item or incomplete customer record should trigger a defined response. This helps prevent a failed step from being treated as completed work.
5. Adapt or Escalate
The agent may revise its next step within its authorised scope, ask a person for help or stop. Adapting to new information during a task does not necessarily mean that it learns permanently from every interaction. Persistent changes to behaviour depend on the solution’s design and should be managed deliberately.
Key Capabilities of Agentic AI
1. Goal-Oriented Execution
An agentic system connects individual activities to an intended result. This can reduce the effort employees spend coordinating handovers and remembering the next action. The objective should be specific enough to test, such as producing a complete quote ready for review, rather than a broad instruction to “improve sales”.
2. Controlled Autonomy
The system can proceed through authorised steps without a person directing every interaction. Different actions can require different levels of review. Reading a record, drafting a response and committing a transaction have different consequences, so they should not automatically carry the same level of independence.
3. Connected Working
Agents can use connected applications, records and tools to move work forward. This creates opportunities across departments, but only where integrations and access rights support the process. A connection to ERP or CRM is not permission to use every record or change every field.
4. Adaptive Responses
An agent can adjust its approach when it encounters new information, uncertainty or an error. For example, it may seek clarification when an item description matches several products. The ability to escalate appropriately is part of effective operation; completing every task without help is not the measure of success.
Agentic AI and Generative AI Work Better Together
Generative AI and Agentic AI address different parts of the same business problem. One helps make information usable; the other helps turn it into progress. Combining them can reduce the gap between understanding a request and completing the associated work.
Consider a customer asking for a revised quotation. Generative AI can interpret the message and draft a clear reply. An agentic workflow can retrieve the relevant account, check product information, prepare the revision and present it for review. The reviewer confirms that the commercial terms and proposed response are appropriate before the process continues.
This combination is reflected in Business Central’s distinction between collaborative Copilot assistance and agents that handle defined processes with reviewable work and human involvement. It illustrates why businesses should assess the whole workflow rather than assume that either technology must replace the other.
The best division of work depends on the process. A complex negotiation may need human judgement throughout, while a routine request with complete information may allow more steps to proceed independently. The aim is to use each capability where it improves the outcome.
Business Benefits and Key Considerations
The commercial case for AI should connect a practical benefit to the conditions needed to deliver it. Faster output is useful only if it reduces total effort and maintains the quality of the business result.
Productivity and Measurable Value
Generative AI can shorten preparation and review tasks, while agentic workflows can reduce repetitive coordination and data entry. Assess the full process, including checking, corrections and exceptions. Useful measures include turnaround time, time spent per case, rework and the proportion of tasks completed correctly. A faster draft may offer little benefit if it creates more downstream correction work.
Data Quality and Accuracy
Consistent customer records, item descriptions, supplier details and transaction histories provide a stronger foundation for both approaches. Poor information can produce a misleading answer or direct an action towards the wrong record. Assign responsibility for maintaining master data and test with realistic cases, including incomplete or conflicting information. Forecasting, anomaly detection and calculations may also depend on specialist models or established software functions, rather than Generative AI alone.
Security and Appropriate Access
Use approved business applications and limit access to the information and actions required for each role. Review connected mailboxes and external tools as carefully as the core ERP or CRM system. Customer emails and supplier documents should be treated as business inputs, not as authority to change an agent’s operating rules. The design should prevent an external request from expanding the system’s permissions.
Governance and Human Oversight
Name a business owner for each workflow and define when people must review, approve or intervene. Controls should cover exceptions as well as routine work. Employees need enough context to assess a proposed action and a practical way to stop or correct it. Retain an appropriate record of activity so that issues can be investigated and responsibility remains clear.
Deployment Cost and Employee Adoption
The investment includes more than licences. Integration, process design, training, usage charges and ongoing supervision all affect the business case. Start with a bounded process, establish a baseline and evaluate the results before extending access or autonomy. Staff should understand both what the system can do and where their own judgement remains essential.
How Generative AI Is Changing ERP and CRM
Within ERP and CRM, Generative AI makes operational information easier to understand and use. The value comes from bringing assistance closer to the records and decisions employees already work with, reducing the need to assemble context manually across several screens.
In ERP
Finance teams can use generated explanations to prepare commentary on reports, provided that the figures and conclusions are checked against the underlying accounts. Operations teams can turn order notes or supplier correspondence into concise briefings. Employees can also use natural language assistance to find information or learn how to complete a process, where the application supports it.
Generative AI can help explain a forecast or an unusual transaction, but generating that explanation is different from calculating the forecast or detecting the anomaly. Keeping those functions distinct helps managers assess whether an output comes from a validated business calculation, a predictive model or an AI-generated interpretation.
In CRM
Sales teams can prepare account briefings, draft follow-up messages and organise meeting notes into a clearer record of customer needs. Customer service teams can use case summaries and suggested replies to reduce preparation time while maintaining continuity between colleagues.
The employee remains responsible for checking that a message reflects the customer’s circumstances and the organisation’s commitments. For example, a generated response about a delayed order should use a confirmed delivery position rather than invent a reassuring date. Access to relevant records improves context, but review remains necessary before making a promise.
How Agentic AI Is Changing ERP and CRM
Agentic AI adds the ability to progress work after information has been interpreted. The following examples describe possible workflow designs; what is available depends on the chosen application, integrations and configuration.
In ERP
In an appropriately configured environment, a replenishment workflow could review stock and existing purchase orders, identify a gap, prepare a proposed order using approved supplier terms and route it for human approval. It should not create a duplicate purchase simply because an earlier request has not yet been approved.
In finance, an agent could help collect invoice information, prepare a draft and request assistance when a vendor or account cannot be identified confidently. Separate business controls should determine approval, posting and payment. An invoice that has been prepared successfully has not necessarily been authorised for payment.
Agents may also help coordinate exception handling across purchasing, inventory and operations. For instance, a delivery change could prompt the system to gather affected order details and ask the responsible planner to review the next step. Predictable rule-based activities can remain in existing workflows, with agentic capabilities applied where interpretation or adjustment adds value.
In CRM
An agentic workflow could interpret an incoming enquiry, locate the customer record, gather relevant context and prepare a task for the appropriate team. Where authorised, it could also update specified fields or coordinate follow-up activity, reducing the manual effort needed to keep work moving.
For customer service, the design might allow routine information gathering while escalating complaints, disputed charges or uncertain commitments. For sales, it might prepare a revised proposal while leaving exceptional discounts and contractual terms to an authorised person. The important change is the ability to coordinate actions with context, while keeping commercial authority explicit.
Agentic and Generative AI in Microsoft Dynamics 365
Microsoft Dynamics 365 combines Copilot experiences, AI agents and other built-in AI capabilities across its ERP and CRM applications. Microsoft documents uses including summaries, assistance with business information and workflow-related tasks. Capabilities differ between products; a feature documented for one Dynamics 365 application should not be assumed to exist in every other application.
Copilot Assistance and Business Central AI
Microsoft Copilot is not a single category of AI capability. Generative experiences help users create and interpret content, while agent-enabled experiences can extend assistance into actions. The relevant question is what a particular Copilot feature or agent does, what information it uses and what authority it has.
In Business Central, documented examples include chat to find records and guidance, analysis assist to organise lists using natural language, and marketing text suggestions based on item attributes. These offer practical starting points for employees working with operational data. Microsoft also distinguishes other AI and machine learning features from Generative AI, so businesses should avoid treating every AI-powered function as the same technology.
Sales Order Agent
Sales Order Agent handles customer requests received by email. It can identify the customer, find requested items, check availability and prepare sales documents. Depending on configuration, the process can include clarification exchanges and progression from a quote to an order.
The agent operates within the permissions and profile assigned by an administrator. Its process includes user involvement, such as reviewing correspondence or resolving missing information. Businesses should configure that review around their order policies and commercial commitments, rather than assume that the agent can accept any request independently.
Payables Agent
Payables Agent monitors a designated mailbox for vendor invoices in PDF attachments, extracts invoice information and helps prepare purchase document drafts. It can seek assistance when it cannot confidently identify a vendor, and supervisors can review and adjust suggested invoice details.
Finalising a draft creates a purchase invoice; it is distinct from posting the invoice or making a payment. Microsoft’s overview lists approval flows among the agent’s limitations. Businesses should therefore retain their required financial approval arrangements and verify how these fit with the wider process, rather than present the agent as an automatic approval or payment system.
Current Availability and Responsible Deployment
As of 14 September 2026, Microsoft lists Sales Order Agent and Payables Agent as generally available. This status applies to those named agents and does not mean that every related AI feature or enhancement has the same release status.
Human supervision remains part of deployment. Microsoft documents ways to review agent work, inspect task history, provide instructions when an agent needs help and stop tasks. These capabilities support oversight, but the organisation still needs designated reviewers and clear operating procedures.
For Malaysian businesses, country availability should also be distinguished from language support. Microsoft’s availability table lists both agents across all supported Business Central countries and regions, with specified supported languages including English. Bahasa Malaysia is not listed for either agent. Confirm the environment, supported language, feature settings and licensing or usage requirements before planning a rollout.
Conclusion
The practical distinction between Agentic AI and Generative AI is how they contribute to work. Generative AI helps people create and interpret information; Agentic AI can use information to plan and carry out authorised actions. Together, they can make ERP and CRM processes more responsive while keeping people accountable for important decisions.
Businesses should begin with a clear operational need, dependable data and a suitable level of control. A focused deployment that reduces rework or shortens a process is more valuable than broad automation without a defined outcome.
For organisations considering this next step, ML IT Partners, a Microsoft Dynamics Partner in Malaysia, can help connect the discussion to practical ERP and CRM priorities. The starting point is identifying where Microsoft Dynamics 365 and Business Central capabilities fit existing processes, and what preparation is needed to use them responsibly.
Frequently Asked Questions
1. What is the difference between Agentic AI and Generative AI?
Generative AI creates and analyses information, producing outputs such as summaries, emails or explanations. Agentic AI plans and carries out actions towards an objective. Its level of autonomy depends on the permissions, rules, controls and approval requirements built into the solution. A business application can combine both capabilities within the same workflow.
2. Can Agentic AI use Generative AI?
Yes. An agentic system can use Generative AI to interpret a request, draft a message or summarise information while coordinating the steps needed to complete a task. For example, it could generate a proposed customer reply after retrieving relevant order information, then submit the reply for review before sending it.
3. Which is better: Agentic AI or Generative AI?
Neither is inherently better. Generative AI is useful when the main requirement is creating or understanding content. Agentic AI is useful when the requirement includes taking actions and coordinating work. Choose according to the process, expected benefit and available controls. Many businesses will benefit from a combination of the two.
4. Is Microsoft Copilot Generative AI or Agentic AI?
It can involve both, depending on the product and feature. Copilot experiences can use Generative AI for drafting, summaries and explanations, while agent-enabled experiences can perform defined tasks. In Business Central, Microsoft distinguishes collaborative Copilot assistance from agents that handle specific processes. Check the individual feature’s capabilities and controls rather than relying on the Copilot name alone.
5. What is the difference between Agentic AI and AI agents?
Agentic AI describes an approach or set of capabilities involving goal-directed planning and action. An AI agent is a software implementation that can use some degree of those capabilities. Agents may perform single tasks or multi-step workflows, and their autonomy varies. The terms are related, but an agent is not limited to a single-step task.
6. How are Agentic AI and Generative AI used in ERP and CRM?
Generative AI can help employees summarise records, draft communications and interpret business information. Agentic AI can progress authorised work, such as preparing sales documents, coordinating follow-ups or gathering invoice information for review. Effective use depends on connected data, suitable permissions and human involvement where the process requires approval or judgement.
References
Microsoft documentation checked on 14 September 2026. Feature availability and requirements may change.
- Microsoft Learn. Business Central AI
- Microsoft Learn. Agents, Copilot, and AI capabilities in Dynamics 365 apps
- Microsoft Learn. Sales Order Agent overview
- Microsoft Learn. Payables Agent overview
- Microsoft Learn. Business Central resources for partners – AI innovation and Copilot
- Microsoft Learn. Supervise agent activities
- Microsoft Learn. Copilot and agents country/region availability and supported languages
