Agentic AI: The next evolution in enterprise automation

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Executive summary

In an era where speed, operational excellence, and client-centricity drive competitive advantage, enterprise leaders leverage every resource at their disposal and embrace automation as a strategic enabler. What began in the 1990s as scripted automation—where simple scripts handled repetitive tasks with little adaptability and required manual updates—evolved into robotics process automation and AI assistants over the following decades. Today, Agentic AI represents a promising new frontier in enterprise automation, reshaping the way organizations streamline decision-making and manage complex processes through autonomous reasoning and dynamic orchestration.

Globally, the Agentic AI market—valued at $5.1 billion in 2024—is projected to reach $47.1 billion by 2030, accounting for a notable subset of overall Generative AI spending. Meanwhile, Gartner predicts that by 2028, at least 15% of daily business decisions will be made autonomously through Agentic AI but, also by that time, 25% of enterprise breaches will be tied to AI agent abuse.

Agentic AI autonomously plans and executes complex tasks through iterative cycles of reasoning and action. Unlike traditional AI systems—which only respond to queries or follow fixed rules, much like a passive assistant—Agentic AI introduces “agents” capable of taking initiative and adapting in real time by understanding objectives, interacting with tools, learning from outcomes, and dynamically adjusting their approach.

The experience is comparable to having a qualified team member to whom you can delegate high-level goals—someone who interprets instructions, takes initiative, and coordinates every step needed to meet objectives. This enables organizations across retail, financial services, manufacturing, and other sectors to automate sophisticated business processes while maintaining necessary oversight and compliance. For instance, in a customer service context, an AI agent can move beyond basic question-answering by checking a user’s outstanding balance, recommending which account to draw funds from, and completing the transaction when prompted. Throughout this process, the agent conducts natural conversation, adapts to unexpected questions, and seamlessly handles requests that arise in real time.

Core value propositions

Agentic AI offers several significant advantages for enterprises seeking to enhance their operations and customer experiences.

• For complex multi-step processes, traditional rule-based engines or machine learning systems often demand significant maintenance or retraining whenever the environment changes. However, Agentic AI handles multi-step processes using dynamic planning and orchestration through problem decomposition and adaptive task execution, continuously observing and learning from each outcome. Although this approach offers greater flexibility and resilience, it also introduces new challenges. AI agents that make decisions must be auditable and understandable as errors—such as hallucinations or incorrect decisions—can cascade, jeopardizing the entire process.
• When dealing with knowledge-intensive tasks, traditional approaches often require manually retrieving data from various sources—such as search engines, databases, or APIs—and then synthesizing that information before making decisions. Today, agentic knowledge assistants perform in-depth research, use multi-step reasoning, and integrate data from multiple sources across organizational silos. They analyze large volumes of information and determine subsequent steps—such as identifying additional data requirements or clarifying user intent. However, these systems face challenges similar to other generative AI solutions. Hallucinations can result in inaccurate assertions, and tracking the origin of information becomes essential, especially in high-stakes environments.
• In operational decision-making, traditionally rule-based systems and threshold-based alerts have been used to hand off situations to human operators for further resolution. Agentic AI offers more advanced autonomous capabilities—it can analyze problems, gather relevant information, and apply sophisticated reasoning to determine when human escalation is necessary or when an alternative solution might be more appropriate. This shift toward deeper automation brings its own challenges. As organizations rely more on AI-driven decisions, questions arise about how to keep humans engaged and maintain their expertise.
• For customer experience personalization, current customer-facing interactions largely rely on rule-based or ML-driven personalization that supports predefined journeys or offers broad recommendations based on crowd behavior. In contrast, Agentic AI has the potential to transform these experiences by providing a nuanced, real-time understanding of customer intent. By analyzing a customer’s history, session patterns, and broader context—much like a knowledgeable store associate—this technology can craft highly tailored recommendations and solutions. While deeper personalization promises to enhance digital engagement and drive business value, it also raises significant privacy concerns.

Maturity spectrum

Agentic AI exists along a maturity spectrum from established systems to emerging concepts.

• Information-retrieval agents currently in production function as “information search” engines. They use fixed data analyzed by multimodal large language models—capable of understanding graphs and charts—and techniques such as vector search and semantic matching to deliver a single point answer to the customer’s question. Platforms like Google Agentspace provide pre-built connectors for common enterprise applications, though enterprises with complex ecosystems need specialized partners.
• Multi-tool retrieval agents consolidate information from multiple sources and plan which ones to invoke based on customer requests. For example, if a user asks about quarterly sales, the agent might query CRM systems, databases, and even Google spreadsheets to gather context and synthesize a response. Sometimes, this process also involves invoking enterprise APIs. Although these agents typically deliver a “single-shot” answer, they employ a cascading approach by dynamically selecting and calling multiple tools to provide a comprehensive response.
• Deep research agents conduct multi-step investigations into complex inquiries. They make multiple round trips to various systems and engage in self-reflection—asking whether the current context is sufficient, if the user’s intent needs clarification, or if additional tools should be introduced. This iterative loop, which includes task decomposition, external data collection, and critical reasoning, aims to deliver not just a single answer but a comprehensive report with recommendations. Recently released Perplexity Deep Research can spend several minutes performing dozens of searches, reading hundreds of sources, and reasoning through material to deliver detailed reports.
• Task execution agents not only gather information but also have the ability to make direct changes in the environment. For example, they might update tickets or onboard a user across multiple systems—similar to how an airline manages passenger check-ins. If a system fails or becomes unavailable, the agent escalates the issue to a human operator to resolve the blocker. These agents operate on a “react” paradigm: they act, observe the results, and adjust their plan if the action is unsuccessful.
• Autonomous agents represent the latest frontier. At this stage, the agent no longer waits for explicit instructions when it encounters a blocker; instead, it continuously monitors its environment. Subscribed to relevant business events, the agent takes action as soon as an event occurs. It exercises a degree of independent judgment by planning and executing operations while coordinating with other agents through task delegation. Sometimes, one autonomous agent takes the lead and distributes tasks among others, enabling a coordinated response across the organization.

Key innovation drivers

Three major breakthroughs distinguish Agentic AI from traditional AI-driven automation.

• Inference-time reasoning enables AI to dynamically analyze complex problems and generate solutions without retraining. This capability stems from a two-system cognitive architecture that mimics human thought processes—combining fast pattern matching with deliberate step-by-step reasoning. High-end models from OpenAI, Google, and others now come pre-equipped with chain-of-thought reasoning, enabling them to handle new problems without constant retraining—much like humans adapt to new challenges.
• Multi-agent architectures unify AI, humans, and software agents within a common framework. In this architecture, human operators, software agents, and even databases act as agents within a common framework. This unified approach allows agents to communicate naturally, coordinate tasks, invoke APIs and business applications, adapt to real-time events, and escalate complex decisions to human experts when needed. When specific protocols are required—such as for invoking APIs—special guardrails ensure the agents follow a structured schema, creating a resilient enterprise automation ecosystem.
• Enterprise-grade tool integration allows AI agents to discover, select, and orchestrate software tools based on business context. By combining structured API access with natural language-driven function calls, these systems enable AI to act as an intelligent middleware layer, translating high-level business objectives into precise system interactions. Secure execution environments, role-based access control, and comprehensive audit logging ensure the automation process remains governed and compliant with enterprise standards.

Implementation considerations

To successfully integrate Agentic AI, enterprises should focus on several key areas. Technology and infrastructure readiness requires modernizing IT ecosystems to support event-driven, multi-agent architectures. This means adopting cloud-native environments, implementing AI orchestration frameworks, and integrating AI with enterprise systems for real-time decision-making. Data foundation and access patterns must ensure enterprise data readiness through structured documentation, real-time access patterns, and comprehensive governance frameworks to enable AI agents to effectively access and act upon enterprise knowledge.

AI governance and risk management demand structured governance models to maintain compliance, security, and ethical decision-making. Organizations should implement AI audit trails, bias detection tools, explainability frameworks, and human oversight to mitigate risks. Workforce and process evolution is essential for effective human-AI collaboration. Companies must reskill employees to work alongside AI, redesign workflows to maximize AI augmentation, and establish clear escalation paths for critical decisions.

Security, bias, and safety concerns

Organizations must balance innovation with appropriate safeguards to ensure reliable, ethical, and compliant deployments. Agentic AI’s ability to autonomously execute actions and interact with multiple systems creates risks of unintended consequences, data exposure, and regulatory violations. For execution boundaries and control mechanisms, AI agents should operate in sandboxed environments with explicit permissions and rate limits to prevent unauthorized actions. Data access restrictions ensure agents retrieve and process only necessary data, with role-based access control, encryption, and anonymization protecting sensitive information.

Regulatory compliance and auditability require detailed logs, explainability frameworks, and human oversight to meet standards like GDPR and CCPA. AI models inherit bias from training data, reinforcement learning, and optimization strategies, making fairness and decision transparency critical concerns. Bias mitigation strategies should include diverse training datasets, bias auditing tools, and ongoing model evaluations to minimize systemic discrimination. AI decision validation requires self-correction mechanisms, human verification checkpoints, and fallback protocols for ambiguous or high-stakes decisions. Explainability and trust mechanisms ensure AI provides clear reasoning behind decisions, allowing human operators to audit, adjust, and override outputs when necessary.

Strategic actions for adoption

To move from experimentation to enterprise-wide adoption, organizations should:

1. Identify high-impact use cases where AI-driven reasoning adds clear value
2. Run pilot programs in controlled environments
3. Implement AI governance and compliance controls
4. Develop an AI-augmented workforce strategy
5. Monitor, iterate, and scale AI initiatives

While many businesses explore AI self-service studios, the complexity of enterprise ecosystems may require a custom-developed Agentic AI platform that evolves with the business and manages all agents within the organization. With 8 years of experience delivering AI applications across industries, Grid Dynamics deeply understands the ground reality of your complex value chains. Partner with experts who can build a future-proof Agentic AI solution tailored to your unique business needs.