Agentic AI refers to a class of AI systems engineered to function autonomously — making decisions and executing actions with minimal human oversight. Unlike traditional AI, which often relies on predefined rules or reactive prompts, agentic systems can learn, adapt, and initiate complex decision-making processes independently. They not only determine what needs to be done but also act on those decisions, including by interfacing with other enterprise applications to carry out tasks across systems. Although still an emerging technology, agentic AI has the potential to dramatically impact many industries by automating complex processes and workflows. However, significant obstacles remain that currently make implementing agentic AI systems within enterprise production environments a difficult undertaking.

Beyond Generative AI

Agentic AI represents the natural evolution of generative AI (GenAI). Whereas the latter primarily functions by generating output (text, images, video, code, music) in response to user prompts, agentic AI systems provide significantly more complex capabilities, including: the ability to set goals, plan multistep strategies, take action in both digital or physical environments, monitor outcomes, and adapt — all without (or with minimal) direct human interaction. Whereas GenAI models primarily operate as sophisticated responders, agentic AI systems are designed to function with a high degree of autonomy; they are capable of selecting tools, invoking services (or other agents), and completing workflows on behalf of a user or entire enterprise.

How Agentic AI Works

Agentic AI systems combine large language models (LLMs) with various orchestration layers, including tool integrations and APIs, in conjunction with feedback mechanisms designed to close the loop between decision and effect. Typical runtime behavior proceeds as follows: the agent first ingests context and constraints, decomposes the objective into subtasks, selects the appropriate tools and APIs necessary for executing the subtasks, issues actions, observes the results, and updates its plan iteratively until the goal is met. This cycle (i.e., perceive, plan, act, learn) is what differentiates agentic AI systems from standard GenAI systems.

Key Components

Agentic AI systems utilize various key technical components, including:

• Perception layer — sensors and connectors that feed the agent structured and unstructured data (which is facilitated by APIs, databases, documents, etc.)

• Cognitive model — one or more LLMs that provide planning, reasoning (chain-of-thought), and natural language capabilities

• Planner/policy module — decomposes goals into a sequence of actionable steps, selects among alternative strategies, and handles orchestration in multi-agent settings

• Tooling/action interfaces — secure adapters for apps, browsers, enterprise systems, cloud services, and custom interfaces that enable the agent to perform various actions (e.g., book an order, update records, initiate backup plans)

• Memory and context store — persistent state and short-term context that enable long-running tasks, continuity across sessions, and provenance for decisions

• Monitoring, safety, and governance — runtime policy enforcement, human-in-the-loop facilities, audit logs, and telemetry for managing risk and compliance

Architectures

Agentic AI systems can be implemented as single-agent or multi-agent architectures. In single-agent architectures, one AI agent handles all tasks. Single-agent systems are preferable for domain-specific tasks consisting of well-defined problems or processes. Multi-agent architectures are for large, complex, distributed workflows. They employ multiple AI agents to break down complex workflows into smaller segments. This approach tends to be more scalable than single-agent systems and is more flexible for solving complex scenarios.

Use Cases

Agentic AI is touted for various use cases and applications, including:

• Customer service. Agentic AI systems go beyond basic chatbots and traditional, scripted customer support applications. By executing multistep, end-to-end workflows (e.g., managing customer inquiries, starting trouble tickets, automating problem resolution), they help minimize customer service representative (CSR) intervention, freeing up human agents to handle more complex support problems.

• Sales and marketing. By integrating with Salesforce automation and CRM applications, agentic AI systems can automate lead qualification, schedule calls, and coordinate CRM updates with minimal human intervention.

• Supply chain management and procurement. By monitoring and analyzing data from diverse sources, such as sales, inventory, and shipping, agentic AI systems can help optimize supply chains (e.g., predict demand, place orders, automate logistics, track weather conditions, respond to disruptions, and reroute shipments) through automated, real-time responses.

• Financial services. Agents can monitor financial applications to automate fraud detection, prepare audit trails, conduct risk assessment, and generate draft reports (subject to human review).

• IT development and IT/ops. Agents can automate code generation, debugging, and testing, which helps speed up and improve code quality, as well as optimize software development cycles. In IT/ops, agents can monitor key IT infrastructure and automate incident response triage.

Technical & Organizational Challenges

A number of obstacles and issues currently make deploying agentic AI systems in enterprises a substantial undertaking.

New & Emerging Technology

Many organizations currently lack personnel experienced with implementing agentic AI systems in enterprise settings. This requires teams to take on new roles (agent designers, orchestrators, safety engineers) to be able to integrate agentic systems into existing and new enterprise workflows.

Integration Complexity

Developing reliable connectors for practically and securely integrating agentic AI systems with enterprise tools, legacy, and other applications is not a trivial undertaking. However, the industry is responding to this problem with new developments, including the Model Context Protocol (MCP).

MCP is an open integration standard that defines how LLMs and agentic AI systems can discover, request, and consume contextual data and executable tools from host applications in a standardized manner. It uses a client-server model that separates the host application, an MCP client, and an MCP server, while standardizing schemas for tool descriptions, data slices, and long-running context so models can call actions, read files, and stream results without requiring custom-built, point-to-point integrations. MCP matters for organizations building agentic systems because it reduces integration risk and enables reuse of agentic workflows across teams and environments — basically, facilitating easier and safer deployments of autonomous agents into enterprise apps and processes.

Data & Model Alignment

The kind of hallucinations experienced with LLMs in consumer-facing GenAI systems (e.g., ChatGPT) is simply unacceptable for enterprise applications. Consequently, implementing agentic AI systems for domain-specific applications typically requires fine-tuning and the use of retrieval-augmented approaches (e.g., retrieval-augmented generation) or hybrid architectures to avoid hallucinations and ensure more reliable output. Such undertakings require experts experienced in agentic AI development who also possess a strong knowledge of the industry and application domain for the intended company/application.

Safety & Governance

Agentic AI systems can take unauthorized actions. Consequently, organizations must implement tools to monitor and audit agent decisions, intents, and outcomes — along with accompanying policies and explainability capabilities — to effectively manage risk and meet compliance requirements.

Status in the Enterprise

Enterprise interest and experimentation with agentic AI is accelerating due to industry developments, which are starting to reduce the headaches associated with implementation. For instance, consulting firms now offer agentic AI advisory and consulting services, and cloud providers like Google and Amazon offer agent frameworks for common enterprise tasks. However, we believe that the majority of enterprise agentic AI developments currently consist of pilot or experimental applications (primarily for customer service, finance, and IT/ops) due to the integration, skills gap, safety, security, and governance issues discussed above.

Conclusion

Agentic AI marks a pivotal advancement in the trajectory of GenAI — moving past the limitations of prompt-based interaction. By integrating decision-making with execution, it effectively bridges the gap between cognition and action. In effect, these systems aren’t just responding to queries; they’re orchestrating tasks, managing workflows, and pursuing objectives with minimal human intervention. However, despite its promise, agentic AI remains in its infancy. Deploying it at scale within enterprise environments presents considerable challenges, from technical integration to governance and reliability.