What Are AI Agents and Their Role in Modern Enterprises?

The workforce of 2025-26 will be hybrid - powered by AI agents under human oversight and supervision. These autonomous agents are miles ahead of traditional assistive tools that rely on human input for direction. They are intelligent enough to autonomously plan actions, take decisions, and learn from experience. They collaborate with each other and with human agents to execute tasks end to end, using reasoning, decision making and nuanced communication where needed.

Let us dive into a few use cases.

Imagine a travel portal manned by AI agents. One agent books tickets, another builds personalized itineraries, another coordinates payments and so on. What we get is a fully harmonized autonomous ecosystem of Agents that can help customers with complex requests that would earlier have needed an expert human agent to resolve.

This article discusses the concept, anatomy, and types of AI agents alongside their risks and challenges. Stay with us till the end.

What are AI agents?

AI agents are intelligent software systems capable of performing tasks and driving outcomes/goals autonomously using natural language inputs. Powered by AI technologies (like ML and Generative AI), these agents possess higher-order thinking skills like planning, reasoning, perception, decision making and nuanced communication, which enables task execution with minimal human input. From recommending diet plans to writing software codes, AI agents can handle a wide spectrum of specialized tasks, keeping humans in the loop (HITL) as an auxiliary tool.

Unlike standalone LLMs or rule-based systems, AI-powered agents can perform more complex multi-step tasks requiring specialized domain knowledge, system integration and compliance. Further, they can improve through self-learning and recalling past experiences, which is in stark contrast to traditional rule-based systems needing human input for rewiring.

Key features of AI agent:

• Reasoning: The ability to think through a problem to creatively stitch together a solution from atomic tasks in order to meet the stated objective or outcome.
• Omniscience: They have access to relevant data and knowledge and therefore are empowered to “think” and reason.
• Actionability: They have access to tools and systems which gives them the ability to not just think, but ACT.

The Evolution of AI Agents: From Rules to Reasoning

To truly understand AI agents, don’t just focus on their nuts and bolts — understand their story. How did AI become an Agent? Let's understand the evolution of AI-powered automation the way it looks today.

🚩L0: No Automation

Humans did all the work, using basic automation for information retrieval.

🚩 L1: Worker Assistance [via Robotic Process Automation or RPA]

Humans still did all the work but in predefined scenarios, AI stepped in with resolutions. AI was capable of executing precise sequences but stumbled when faced with variations.

🚩 L2: Partial Automation [via AI Assistant]

Humans must stay fully alert even while AI assumes basic tasks. In other words, AI only augments work.

🚩 L3: Conditional Automation [via AI Co-Pilot]

Generative AI enters the fray. AI could create, innovate and provide decision support, but humans still had to step in when AI failed to contribute meaningfully.

🚩 L4: High Automation [via AI Agent] - We are currently here

AI assumes the role of a human, collaborating with systems, tools, and other AI agents to accomplish tasks end to end. Humans step in when AI ends or fails. AI produces work.

🚩 L5: Full Automation [AI Enterprise]

Multi-agent ecosystems overtake not only routine but critical business functions, adding value in an organized way.

Source: Bain

Anatomy of an AI agent

An AI agent framework is composed of five broad elements that work together to achieve autonomous task completion. Take a look at them.

1. LLM — The brain

The Large Language Model (LLM) acts as the "brain" of the agent, offering the core intelligence and language comprehension abilities. It enables the agent to process and generate human-like text, understand context, and make decisions based on extensive training data.

2. Planning — Dividing objectives into tasks

A crucial capability of an AI agent is its ability to plan. It allows the agent to break down complex objectives into a series of manageable tasks and empowers the agent to analyze the given objective, identify the necessary steps to achieve the goal, prioritize these steps in a logical sequence, and adapt the plan as new information becomes available

3. Action — Utilization of tools and connectors

The action component is where the AI agent engages with its environment to complete tasks. It frequently involves employing various tools or APIs. Actions an AI agent might undertake include conducting web searches, running code, accessing owned and public databases, and interacting with other software systems.

4. Role — Specialization, tasks, skills

The role of an AI agent outlines its specific capabilities and behaviors. This component may encompass:

a. The agent’s role or specialization (e.g., customer service representative, data analyst)

b. Tone and communication style

c. Ethical guidelines and constraints (guardrails)

d. Specific knowledge domains or areas of expertise

5. Knowledge — Storing and recalling past interactions

The knowledge component enables an AI agent to retain and retrieve information from previous interactions since it’s essential for maintaining context in ongoing conversations, learning from past experiences, enhancing performance over time, and providing personalized responses based on user history. Memory can be short-term (lasting for a single conversation) or long-term (persisting across multiple interactions).

Why should enterprises use AI agents?

According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. Top benefits of AI agents for enterprises look like:

1. Enhanced efficiency AI agents can allow organizations to scale automation across departments and use cases. Large-scale automation will inevitably result in efficiency gains.

2. Personalization: AI agents can help retain high degrees of personalization despite operating at scale, resulting in higher customer satisfaction scores.

3. Scalability: AI agents, with the right Agentic framework, can effortlessly scale automation to unprecedented levels.

Challenges and risks with AI agents

AI agents promise efficiency and automation but challenge enterprises with data security risks, integration complexities, ethical concerns, and workforce adaptation. Let's discuss their top challenges and risks in detail.

• Bias In Decision Making

Agentic AI has the power to completely reshape CX, but it also carries the risk of amplifying hidden biases embedded in training data or workflows. These biases can lead to unfair outcomes — such as unequal resolutions for similar customer issues — undermining trust in a domain where fairness and consistency are non-negotiable.

To mitigate this, you should approach data with a critical lens, ensuring it reflects diverse, balanced perspectives. Regular audits of AI decisions are essential, not just as a checkpoint but as an exercise to improve it over time. More than a technical responsibility, tackling bias is a commitment to equity, accountability and the integrity of customer relationships.

• Regulatory and compliance complexity

In regulated industries, agentic AI must navigate stringent legal and compliance requirements to avoid costly fines or reputational harm.

Missteps — such as mishandling sensitive customer data or violating industry-specific protocols — can undermine trust and lead to significant setbacks. To ensure compliance, you will need to implement clear guardrails, such as audit trails and predefined decision boundaries. You might also want to look into consulting with legal and regulatory experts to align AI processes with applicable laws and standards...or choose an agentic AI partner that boasts of a powerful Trust Center.

• Productivity leakage

Saving time with agentic AI doesn’t always lead to tangible results if the extra capacity isn’t used effectively. When agentic AI takes over routine tasks, metrics like average resolution time improve, but the freed-up capacity can lead to idle moments if not purposefully redirected. Agents, now handling fewer but more advanced cases, might face gaps in focus or efficiency, leading to productivity leakage.

To counter this, you could redefine how time is utilized. Establish clear goals, such as dedicating extra time to thorough case reviews, collaborative troubleshooting or creating in-depth knowledge bases to aid future resolutions.

How to get started with AI agents in your business (+Best practices)

The promise of agentic AI is immense, but implementing it requires a thoughtful approach. Organizations need to balance ambition with practicality while addressing the challenges that come with integrating advanced AI systems into their operations.

1. Codify business processes and knowledge

Agentic AI thrives on well-defined workflows and subject matter expertise. Before you think of implementing one, document your processes, from customer service and call center scripts to escalation protocols, in a structured, codified format. This not only enables effective contact center training but also ensures consistency in AI decision-making.

2. Organize IT and data infrastructure

Smooth integration requires robust IT systems. As an enterprise, you need to ensure data accessibility, streamline APIs and build flexible architectures that allow agentic systems to interface with your present tools without causing disruptions. Centralizing feedback loops will also be critical for steady learning and adaptation.

3. Establish human-in-the-loop mechanisms

Autonomy must be balanced with oversight. Human experts should validate agent outputs for fairness, accuracy and contact center compliance, especially in customer-facing tasks. These control mechanisms are essential to mitigate risks like bias or incorrect decisions, ensuring AI agents operate within ethical and regulatory boundaries.

4. Re-evaluate agent roles and metrics

When agentic AI handles routine tasks, human agents focus on high-stakes cases that need deeper expertise and judgment. This shift means common customer experience metrics like the number of cases resolved or average handling time no longer reflect their true impact. Businesses need to redefine success by evaluating how well agents manage intricate issues. Metrics should emphasize quality over quantity, spotlighting their ability to solve problems and deliver value in ways AI cannot.

5. Adopt change management practices

Adopting agentic AI can understandably create anxiety among employees, who may worry about their roles becoming obsolete. To ease these concerns, you may want to position AI as a partner, not a replacement. Emphasize how it takes over repetitive tasks, allowing your talent to focus on more purposeful work that AI cannot replicate.

Ready to leverage AI agents for your enterprise?

As a leading Agentic AI provider, Sprinklr offers the full range of AI-powered technologies - from conversational AI bots to generative AI-powered co-pilots. Our unified AI acts like a central brain to train the AI models on all your business use cases, driving seamless user experience, efficiency and accuracy. Further, our built-in security and system integration drive trust and interoperability in agentic systems.

But don’t take our word for it. Talk to our AI experts today and explore your business use cases and needs.