Agentic AI is redefining what automation can achieve. While traditional automation focused on executing predefined rules, Agentic AI introduces a more human-like approach to decision-making, learning and collaboration. The market is responding and rapidly growing from less than $8 billion in 2025 to upwards of $52 billion by 2030, with some estimates projecting a leap to $196 billion by 2034.

This is not just a technological shift. It’s a fundamental change in how enterprises manage workflow, customer engagement, innovation cycles and decision-making. The real advantage focuses on how Agentic AI drives autonomation, embedding autonomy within automation to deliver intelligent, adaptive and scalable outcomes.

The future lies in ‘autonomation’

Most business processes still struggle with rigid structures, manual oversight and fragmented data. These limitations lead to inefficiencies, inconsistencies and subpar experiences for internal teams and end users.

Let’s look at how Agentic AI helps eliminate these challenges with capabilities that go beyond task execution:

• Dynamic workflow optimization: Unlike rule-based bots, agentic systems continuously learn and optimize. For example, an agentic bot in banking can detect anomalies or inconsistencies in documents, trigger a fraud detection workflow and update future decision models autonomously.
• Adaptive exception handling: When faced with complex or novel situations, Agentic AI adapts in real time. In service delivery, it can escalate complex queries to human agents while resolving simpler issues and reducing handoffs, improving resolution speed.
• Stringent compliance monitoring: Agentic AI monitors regulatory shifts and autonomously adjusts business rules. In the BFSI sector, it ensures alignment with evolving AML regulations, reducing the risk of non-compliance.
• Humanizing customer interaction: One of the most exciting impacts of Agentic AI is its ability to humanize customer engagement. Consider a healthcare AI Agent that scans through lab reports, prescriptions and past patient medical history and recommends moving an appointment up in the priority list based on critical indicators, potentially saving lives.

From general-purpose agents for document classification to domain-specific telecom, banking or insurance applications. Agentic AI creates business impact.

Key considerations to get Agentic AI right

While Agentic AI is a significant step forward, it is still not immune to familiar enterprise AI risks. Taking a proactive stance is imperative to achieve the desired results. The most common considerations are:

• Bias and fairness: AI models are known to develop bias while learning from training data. The risk of bias is higher in the case of Agentic AI as it learns from real-time data. If unchecked, the bias may become more impactful, necessitating a strategy to ensure fairness from the onset.
• Data privacy and security: As data fuels AI models, the importance of privacy and security protocols cannot be emphasized enough. The stakes are even higher in the case of Agentic AI as it learns from real-time data.
• Accountability and transparency: Understand and tracing AI decisions is crucial for reliability and gaining users’ trust. The accountability and transparency required from Agentic AI are much higher, as it makes decisions and takes actions autonomously.
• Change management: Like any major technological disruption, full-scale implementation of Agentic AI can significantly alter workforce dynamics. This makes advance upskilling/reskilling/cross-skilling of employees critical for its successful implementation.
• Chaos or A/B testing technique: Considering the agents behave in an autonomously, non-linear testing model is important to ensure agent viability and avoid misadventures or cascading impact due to data deluge or toxicity.
• Control and governance: Codified guardrails, guidelines and control for the appropriate use of agents and infusion of human-in-the-loop (as necessary) are critical to orchestrating and avoiding any irrational activation of the agents that can have a cascading impact and disrupt the business with its outcomes.

Depending on an organization’s unique scenario and the required Agentic AI use cases, there could be more challenges.

Creating predictability in an agent-driven ecosystem

A predictable framework is essential to guide the autonomy of AI agents without limiting their impact. Enterprise can use a structured orchestration model to manage scale and complexity effectively.

1. Predictability— Define bounds of actions while allowing agents to make decisions within agreed parameters.
2. Auditability— Ensure that every action by an agent is traceable, especially in an environment subject to compliance checks.
3. Orchestration— With potentially hundreds of agents operating across processes, central orchestration ensures consistency, reduces duplication and enables shared learning.

This framework do more than protect against risk, they enable Agentic AI to scale meaningfully while delivering value at every layer of the entire enterprise.

Our vision for Agentic AI

Together, HCLTech and Pega empowers enterprises to accelerate their shift toward autonomous, experience-led operations. By integrating Pega’s AI-driven platform and HCLTech’s advanced case management capabilities businesses can rapidly design, deploy and scale intelligent workflows, laying the foundation for the Agentic Enterprise.

The way forward: Make autonomation your strategic edge

Agentic AI shifts the conversation from “How can we automate this task?” to “How can we embed intelligence across our enterprise?” The transition from Software-as-a-Service to Service-as-Software is underway, where intelligent agents deliver outcomes autonomously, safely and at scale.

This evolution requires more than just new tech, it demands a rethink of operating model, seeking smarter, innovative ways to engage customers or modernizing your operational core, our combined expertise can help you lead this transformation.

Explore our joint capabilities and take the next step toward building an agentic enterprise.