Building AI Agents: 6 Critical Factors for Success in 2025
‘Agentic AI’ is disrupting every industry in ways we could not have imagined a couple of years ago. Yet, as with any technological innovation, Agentic AI also has to go through the hype cycle and ‘trough of disillusionment’ before it hits the ‘peak of productivity’. So, I was not surprised when Gartner made the following observation:
“Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls.”
While the outlook may appear discouraging, I view this statement as a valuable opportunity for every business and technology leader to reflect on a critical question:
What deliberate actions can I take to ensure my organization does not fall within that 40%?
I am confident that the six key factors outlined below can provide meaningful guidance in addressing this question and support your efforts in successfully implementing agentic solutions.
1. Start with the Why?
Why should agents or AI be employed to address a given problem? Often, I encounter situations involving data computations, deterministic process automation, or natural language processing bots, where agentic solutions are proposed. However, such use cases do not necessarily require agentic AI. Established technologies—such as Robotic Process Automation (RPA), traditional chatbots, and Business Intelligence (BI) tools—are highly effective and should not be disregarded if they adequately meet the business need.
2. Measure ROI:
Building on the previous point, if comparable automation capabilities and efficiency can be achieved through a more cost-effective solution—such as Power Automate—then that presents a stronger return on investment (ROI). While many organizations assess efficiency gains primarily from an operational perspective, they often overlook the range of available technological options and their associated costs. It is essential to evaluate ROI holistically, with careful attention not just to the benefits, but also to the cost—the denominator in the equation.
3. Is it really Agentic AI?
Once you have identified a well-defined problem that cannot be effectively addressed through traditional methods—and for which the return on investment is clearly justified—the next step is to evaluate whether a vendor is truly offering an agentic AI solution. Many products in the market are now repositioning themselves to include agents; however, in many cases, the fundamental capabilities of the product remain largely unchanged.
If you are incurring higher costs due to the inclusion of “agents,” it is crucial to assess whether core agentic attributes—such as autonomous decision-making, strategic planning, and self-learning—are genuinely integrated into the solution and if these capabilities are bringing you more business value. Without these, you may simply be falling victim to “agent washing.”
4. AI Operational Costs
There are two types of AI agent costs that need to be considered– Fixed Cost & Variable Cost
- Fixed Costs aka Project Cost: This includes the cost of AI environment setup, finetuning (temperature, prompt refinement, model fine tuning, etc.), integrations (MCP, tool use, etc.), and is typically the project cost for AI engagement.
- Variable Costs aka AI Operational Cost: This includes cloud cost, input/output token cost, cost to optimize agents, support cost, etc. By understanding the 3Us – Use case, User Count, and Usage Volume – this needs to be modeled out to understand the real value of an AI project.
5. Accuracy Matters!
A vendor may promise 90% accuracy for automating a business process using agents, and while that may initially seem satisfactory, it is important to pause and assess the broader picture. How many tasks or sub-processes comprise that overall business process? For instance, if the process involves four agentic sub-processes, each with 90% accuracy, the cumulative or effective accuracy drops to approximately 65%, which could significantly impact business outcomes.”
Clearly defining what success looks like—and gaining a thorough understanding of the sequence of tasks and sub-processes involved—is essential. This clarity helps ensure alignment of expectations and prevents unexpected outcomes after implementation.
6. Day Zero – Governance & Security
“AI is not just a technical shift; it’s a governance shift. That’s why Day Zero planning matters.”
It is important to drive transparency, accountability, and compliance while adopting AI solutions. Therefore, it’s key to establish the following early in the cycle:
- Setup AI Governance Council with cross-functional leadership
- Establish AI governance frameworks (e.g., NIST AI RMF) and conduct periodic security audits.
- Adopt best practices like explainable AI (XAI), human-in-the-loop, data leakage assessments, data access controls, and review AI security posture (against vulnerabilities and adversarial attacks).
This will enable organizations to set themselves up for long-term success and build confidence with AI-based solutions.
One might be tempted to say, “I will avoid being part of the 40% by simply not pursuing AI initiatives.” However, that would be a strategic misstep. Agentic AI represents the future of technology innovation, and increasingly, every application—whether aimed at enhancing customer experience, driving revenue, or achieving market differentiation—will be powered by intelligent agents.
It is imperative that organizations do not remain on the sidelines and risk losing competitive advantage. Instead, they should actively seek out AI-driven differentiators that can elevate their strategic position.
I hope these guidelines serve as a helpful foundation for making the agentic leap—deliberately and thoughtfully—because, as the saying goes, “failing to plan is planning to fail.”
Bottom Line
At iLink Digital, we don’t just talk about Agentic AI—we build it, implement it, and scale it across enterprises.
Our Agentic AI frameworks are engineered to go beyond simple automation. We design intelligent agents with autonomy, memory, reasoning, and collaborative decision-making capabilities—tailored to your industry and ecosystem.
From Planning to Production: Build AI Agents That Actually Deliver
The age of agentic AI demands more than experimentation as it demands execution. At iLink Digital, we’ve built a powerful suite of AI accelerators and orchestration platforms designed to help you move confidently from pilot to production.
Accelerate with eXLFlow
Our AI-powered SDLC automation framework, eXLFlow, streamlines coding, testing, debugging, and deployment with minimal human effort. By integrating tools like GitHub Copilot and AgileXL, we help tech-driven organizations improve developer productivity, reduce release cycles, and deliver higher-quality software, faster.
Orchestrate with iGENTIC
For enterprises looking to scale intelligent agents across workflows, iGENTIC is our platform-agnostic, low-code, multi-agent system. Built for business agility, it integrates seamlessly with AWS Bedrock, Microsoft Foundry, and your existing ecosystem—bridging the gap between innovation and operations.
Let’s help you build intelligent agents that deliver real business value.
Roshni Mohan
Associate Vice President
Author
Roshni Mohan is the Associate Vice President – Architecture & Technology Solutions with over 20 years of experience in deploying large-scale systems. At iLink, she leads AI projects and develops accelerators that drive efficiency and innovation. Her expertise spans enterprise architecture, scalable platforms, and AI-based solutions currently.
