.jpg)
The AI workflow is now going beyond specific instructions, and, in agentic AI, it is possible to create processes where the AI or agent makes decisions and plans the steps, applies the tools, analyzes results, and decides what to do next.
This is a significant change that provides more possibilities for automation, especially those that involve a complicated process or many pieces of unstructured data.
Nonetheless, the greater degree of autonomy raises an important question for business leaders: where is it still necessary for the human supervisors to control the processes?
The answer is not to involve people in every single step because it leads to the opposite effect. Enterprises have to decide which decisions can be made by the AI independently, which require approval, and which should be controlled by humans.
In this article, we will describe how agentic AI is changing AI workflow and whether supervision over the AI processes is necessary.
As enterprises explore AI workflow automation, the focus is increasingly on making it work within existing business processes. CloudTara takes a similar enterprise-focused approach to integrating AI into real-world workflows.
What is agentic AI workflow automation?
Agentic AI workflow automation refers to the usage of the AI agents that can realize a business process based on an aim of the process instead of applying a fixed sequence of instructions.
The conventional workflow is represented like this:
Trigger – Rule – Action – Next Step
The agentic AI workflow is different.
Goal – Understand – Plan – Act – Evaluate – Adapt
An agent is given a task, establishes required steps, utilizes accepted tools and systems, analyzes outcomes, and decides on what follows.
IBM notes that agentic workflows are AI-driven processes where autonomous AI agents conduct actions without human input.
Unlike typical automation, these workflows can react to real-time information as well as unexpected conditions.
For instance, let us look at the IT support workflow.
With the traditional automation, the operation is carried out where the system detects a problem with the Internet connection and performs a predetermined series of operations. In case none of them help, the problem is escalated.
In the agentic workflow, agents' actions are broader. The agent understands the issue and asks for further clarification, checks approved information from the system, chooses the exact instrument of diagnostics, analyzes test results, and possibly tries another way of troubleshooting if needed.
For enterprises, agentic workflows also need to fit into existing systems and processes. CloudTara approaches AI workflow automation with this broader enterprise integration in mind.
In this article, we'll discuss the difference between agentic workflows and traditional workflow automation.
When discussing systems, there is simply no comparison between them.
Let's define the difference between agentic workflow and traditional workflow automation.
When deciding on automation in any process, two terms often come up: agentic workflows and traditional workflow automation.
Now let's look at the differences between these two concepts further.
Agentic Workflow vs. Traditional Workflow Automation
Traditional workflow automation does a great job for processes that can be predicted.
Following are some of the specific processes for which traditional workflow automation works best:
- Transferring data from one system to another
- Sending notification emails as required
- Processing standard invoices as needed
- Updating records as required
- Executing scheduled reports
- Issuing pre-approved decisions
In this case, artificial intelligence may not be required.
But it's essential.
However, agentic workflows are much more beneficial in processes involving uncertainty, exceptions, unstructured data, or decisions, depending on the context.
Now let's look at the differences further.
However, with the use of agentic works, productivity level in the company increases.
That is not to say, though, that traditional automation should be replaced with it.
Using both variants of automation sometimes proves to be the best solution.
A business can leverage deterministic automation to execute predictable steps and rely on an AI agent to handle tasks involving interpretation, decision-making, and dealing with exceptions.
For example, in the invoice processing case:
- Automation can obtain standard invoice information;
- An AI agent can investigate an invoice that does not match the purchase order;
- A human can approve an exceptional case.
This combination makes it possible to create a more effective AI workflow automation approach than trying to make processes fully autonomous.
Where Do Humans Still Require Control?
This is the crucial factor to consider when implementing agentic AI workflow automation.
The goal must be not to eliminate human input.
But rather to use human input when it is needed most.
Financial decisions
AI agents can assist in paying bills, spotting inconsistencies, preparing payments, and executing routine financial tasks.
However, organizations may still require human consent to complete:
- Transactions with high value
- Payments that are just as unusual
- Any new suppliers
- Exceptions with reference to pricing
- Significant purchases
- Financial obligations that do not fit into the standard policy
An effective solution is to set thresholds.
An agent can undertake all regular transactions independently but seek approval when it exceeds the threshold.
Legal and contractual decisions
AI can deal with the contract analysis, looking for various conditions, atypical clauses, and suggest options, but still, the company may need an authorized person to approve:
- Non-standard terms of the contract
- Innovative obligations
- High-risk agreements
- Important correspondence
A program has the ability to perform much of the analysis without taking a final decision.
Customer-related decisions
Automated processes will be even more sensitive when an AI is taking a decision that has an impact on a client.
In this situation, we have:
- Refunds
- Account blocking
- Termination of services
- Claims
- Decisions about eligibility
- Credit decisions
It is manageable to automate low-risk and policy-compliant cases.
Cases connected with disputes or rare situations must have an efficient way of appealing to a human.
Security and infrastructure modifications
AI agents are advantageous in the realm of IT operations, as they can continually oversee systems, analyze alerts, and execute authorized remedial actions.
However, one should not assume that the agent should automatically have unrestricted access to production infrastructure.
Automating regular operations is invariably possible.
Dangerous or irreversible changes would, however, be subject to human validation.
Compliance-focused issues
The next thing that must be taken into account by businesses is accountability.
When an AI agent performs a specific action, the organization should be aware of:
- What information has been used
- What tools have been utilized
- What action has been taken
- What rules came into play
- Whether the action required approval
- Who has validated the action
- Why the current workflow was escalated
In the absence of such transparency, it will be difficult to track mistakes or prove compliance.
Enterprise Use Cases of AI Agent Workflows
The best cases can be found in areas of high information gathering, systems switching, and repetition of processes.
IT service management
The AI agent can read a ticket, gather necessary system information, run diagnostics, apply standard solutions, and escalate the issue if needed.
This can reduce the amount of manual investigations required by IT teams.
Finance and accounts payable
AI agent can analyze invoices, compare data with purchase orders, find discrepancies, get missing documents, and send the exceptions to relevant employees.
Customer service
The agent is capable to clarify customer inquiry, get information about accounts, verify terms and conditions, investigate the issue, and solve trivial problems.
More complicated or sensitive issues can be escalated while being already prepared.
Sales procedure
AI agents are capable of examining accounts, collecting appropriate company data, revising CRM records, identifying selling signals, and composing personalized messages for outreach.
Human checks play a vital role before communicating with high-priority accounts or taking part in regulated systems.
Agentic AI Process Automation Advantages
If agentic automation is used appropriately, it can offer a number of benefits.
Decrease in the required effort
Agents may help in completing redundant searches, documents' collection, systems updating, and everyday work that otherwise has to be performed by employees using several applications.
Shorter terms of decision-making
Instead of asking employees to gather information, the agent will collect the relevant data in the course of the process.
Improved Exception Management
The classic form of automation could fail to complete a task where there is no match to some intended condition.
Using the agentic approach, the workflows can analyze these exceptions and find out if another approach can be taken.
Better Scalability
If automating routine work is feasible, it would allow companies to process much bigger volumes without having to perform the same amount of manual work.
Risks To Be Considered By Companies
However, the increase in the level of autonomy runs some dangers for companies that must be addressed before implementing agentic workflows.
Access to Sensitive Data
The agents will need access to the important enterprise's data.
Proper design should include such aspects as least privilege access, authentication, data protection, and policies of data retention.
Unpredictable Results
In contrast to deterministic automation, an agent can make different decisions in the same situations as it reacts to a certain context and available information.
This makes testing and monitoring important.
Mistaken actions
An AI agent may not interpret data properly and choose inappropriate actions.
The level of risk and possible consequences depend on the type of permissions given to such an agent.
This is why it's better to strict controls on actions that carry a higher risk.
Lack of traceability
Organizations need to keep track of the actions done by the agent and reasons behind them.
Logs and data of approvals, workflow histories, and monitoring are necessary for solving issues and proving accountability.
How to Start Using Agentic AI Workflow Automation
Companies should not begin with applying the technology.
Begin with the workflow.
This workflow-first approach also helps enterprises scale automation without adding unnecessary technology complexity. CloudTara focuses on connecting AI automation with existing enterprise workflows and systems.
1. Find a process with high value
Search for the process which features:
- High manual labor
- Frequent bottlenecks
- Different systems
- Unstructured data
- Specific decision-making
- Measurable results
2. Analyze the process map
Find out where things stand now.
Find the persons involved, systems utilized, decision-making points, exceptions, and delays.
3. Make a clear distinction between automations and agentic decision-making
4. Define the limitations of self-governance
For every significant task, determine whether the agent must:
- Advise
- Prepare for
- Do
- Do it only in specific situations
- Always ask for permission
5. Determine escalation procedures
Indicate what happens when:
- There is no information
- Systems are at odds
- The agent is uncertain
- Transaction is above the threshold
- Policy deviation occurs
- Security problem arises
6. Examine the system thoroughly before increasing autonomy
Start with guidelines or low-risk activities.
Study normal cases and systemic failures, scenarios with missing information, and the system's attempts to act beyond its authority.
Only proceed with the further functioning if the system provides the relevant results.
7. Assess whether the system delivers business results
Monitor the conditions and performance of the system as well as its impact on the completion of a task.
Some of the most important indicators may include:
- Speed of process completion
- Cost per operation executed
- Error index
- Necessity to escalate issues to a human
- Failures in tool performance
- Quality of outputs
- Overall success with a given task
The Role of CloudTara in Agentic Workflow Automation
Implementing an agentic workflow is not just about connecting an AI model to an existing process in the organization.
The entire workflow requires the right integrations, the correct data, the needed permissions, the right automation logic, the adherence to organization-wide monitoring, and governance protocols.
CloudTara offers AI and Data Services of agentic AI-based automation. Including custom AI agents, complex workflow automation, integrations with existing processes and systems, and continuing AI support.
CloudTara views enterprise automation concerning broader technology modernization processes based on the idea of unifying workflow automation, AI-powered intelligence, and integration systems with cost-effective technology solutions.
This approach is important as the most efficient agentic AI deployment is rarely the one where an agent is added to the existing process and forgotten about.
The implementation of agentic AI is based on understanding the existing workflow, identifying the areas where it can bring tangible value, defining the areas where determinative automation of actions might be more effective, and determining the regulations necessary for safe operation.
Although the usage of AI should be responsible, it does not mean unnecessary power needs to be entrusted to AI.
Finding the balance is the key.
Let AIs deal with simple scenarios with low risks involved, yet enable human intervention for highly-sensitive decisions.
Companies need to make sure that no decision with a big financial, legal, security, regulatory, or customer influence is made without human involvement.
The future of automation will therefore combine all these elements:
AI, traditional automation, enterprise systems, and human reasoning.
For the companies, the challenge is not automating everything.
The challenge is automating key processes but retaining human control over important decisions.
CloudTara can help companies understand how AI workflow automation and agentic workflows can be integrated into their operations and make sure that the right things are done.
Ready to discover where agentic AI can make a difference in your business processes? Contact CloudTara and find out how you can implement AI automation in your company.
.jpg)
