Agentic AI: Self-Managing Workflows in Enterprises

With automation and next generation AI models getting advanced day by day are able to manage documents, extract data and use automation to reduce the processing time. However, GenAI is not able to make decisions on its own. This changed with Agentic AI.
Agentic AI refers to software programs that are specifically designed with a designated role to act autonomously on its own. It is rapidly changing the workflow from traditional decision making softwares to a more advanced approach towards decision making in the enterprise.

Agentic AI workflows combine reasoning capabilities, human error and cognitive into a more dynamic and independent task making it a self managing enterprise AI.

Features of Agentic AI that sets it apart

1. Use of LLM models for reasoning: Large Language Models (LLMs) that were initially used to generate data and then AI used to analyse that data. With Agentic AI, LLMs are being used for logical reasoning, pattern recognition and then independently performing decisions. For example, in logistics, Agentic AI can predict disruption in delivery, re-route deliveries and negotiate supplier without the involvement of humans and adhering to the compliances improving the whole workflow efficiently.
2. Varying Degree of Control: Every enterprise is not looking for full automation, Agentic allows the companies to chose the level at which they want to implement agentic AI into their workforce. Financial industries could set up Agentic AI to flag unusual expense claims, however later with analysis of such claims they can choose to adopt or reject such claims.
3. Cognitive System: Agentic AI is developed to match human cognitive skills. It could perceive, analyse, reason and then act like a human brain. It will learn with each interaction and can make additions to existing systems from CRM to ERP platforms which work on cloud-based management tools. Cognitive systems are essential as they can maintain coordination between various departments keeping projects moving without human oversight.

Trends in Agentic AI to look for in 2025

An average business operates across several software platforms regularly within different time-zones. However, with a global team working together with real-time coordination which becomes easier with Agentic AI. Business is evolving everyday and breaks in the supply chain have been proven to be costly. As per The Editorial Institute Static workflows are becoming obsolete everyday, agentic AI ensures that businesses thrive efficiently.

• Companies that are implementing Agentic AI not just to get work done but using it to design strategies too. For example, agentic AI can stimulate market scenarios and provide recommendations for investment based on data.

• Financial Institutions are making use of Agentic AI to predict loan frauds, defaults in payment, adjusting interest rates automatically and minimizing risk involved with bankruptcy. EY, powered by NVIDIA, has deployed 150 agentic tax agents that support the workforce of 80,000 employees in analysis of tax and funds.

• Agentic AI can track compliance requirements of the company, highlight security breaches and even auto generate security protocols.Microsoft integrated such programs into internal IT workflows reducing ticket resolution time by 40%.

• The shift from human-AI collaborative models are proving to be more effective with AI handling 80% of the work while humans focus on innovation, relationship and management. IBM has deployed agentic AI for cutting the contract to review cycle from weeks to hours.

Best Practices for implementing agentic AI

1. Look for high impact workflow: workflows that are repetitive operating through inter department and experience most delays like invoice approval, customer service can improve with the use of Agentic AI.
2. Build trust gradually: start with AI-assisted decision making to steadily increase automation. Starting with full automation can lead to trust issues with customers.
3. Ensure data security: Agentic AI can only be essential if the data they process is clean and integrated into secure pipelines otherwise could lead to serious security threats to the enterprise.
4. Involve stakeholders and measure ROI: implementation involving both executives and operational team to design the workflow can reduce resistance and ensure smooth workflow. Track metrics as to time saved error avoided and cost savings to quantify the impact of integration.

Why is the shift necessary?

Business today is growing at a much faster pace than a decade ago. The static process to manage the workforce is getting outpaced and not sustainable in today's economy. Walmart has deployed agentic AI super agents that work with customer service and coordinated with supply chain with minimizing delays and hence, optimization e-commerce.

CEOs and CTOs are making their organisation's future ready by reducing high operational efficiency while maintaining secure systems for their enterprise.
Big tech giants are adopting because they recognise the potential of Agentic AI and redefining how companies operate today.
Despite the benefits, agentic AI faces challenges with organizations that have difficulty connecting with AI making a shift from their traditional systems. AI agents need clean and accessible data, failure to provide such data can lead to inefficient and wrong decisions.

AI systems today that make important decisions specially in the finance sector raise concerns about their unbiasedness and fairness. Organizations need to implement strict guidelines of implementation for ethical and safe use of AI.

Agentic AI represents a turning point in the automation industry- it is the difference between reacting and shaping the business. The next wave of innovation would lead with Agentic AI which can combine human capabilities with AI autonomy creating a secure self sufficient environment which is not just smart but more resilient.

At the Editorial Institute, we help tech leaders to design and implement AI strategies that deliver results and not just promises.