What is agentic AI?

 Agentic AI is artificial intelligence that can pursue a defined goal on its own, by planning the steps, using tools and systems, and adjusting course as conditions change.

Agentic AI moves beyond answering prompts or generating content to execute work. In enterprises, agentic AI will increase productivity, as people become AI supervisors rather than task executors. It will shift roles and redefine decision making by democratizing technical skills, increasing employee autonomy, and accelerating decision cycles. Agentic AI will require leaders to marry business and technology expertise, treating it not as a technology rollout but as a business transformation enabled by AI.

Agentic AI vs. generative AI

 Generative AI gave us copilots that assist human work by generating output. Agentic AI can reason, collaborate, and coordinate multistep work across systems.

Capability	Generative AI	Agentic AI
Primary role	Produces text, code, summaries, images, or analysis	Executes complex, multistep workflows by setting goals, planning, and learning on the fly, with minimal human input
Typical interaction	Serves as a smart assistant with a human in control, typically through conversational interfaces	A user assigns a goal; the agent works through steps, handles trial and error, and takes tactical action across tools, applications, and workflows
Workflow scope	Often task-based	Often complex, multistep, nondeterministic processes that span multiple business domains and systems
Tool use	Can be integrated into development platforms or enterprise tools	Uses tools, applications, APIs, and other agents to act
Enterprise value	Productivity, knowledge access, content creation; however, real ROI comes from business redesign rather than just deploying tools	Productivity, process reinvention, faster decisions, technical skill democratization, autonomous workflow execution
Governance need	Security, transparency, bias and privacy controls, data ownership and intellectual property rights, and human accountability and compliance	Runtime policy enforcement, monitoring, audit controls, failure protocols, and governed unstructured data foundations

How does agentic AI work in enterprises?

In enterprises, agentic AI entails deploying AI agents that can reason, coordinate with other agents and systems, and execute multistep workflows. Agentic AI moves work beyond single-task automation into end-to-end process execution.

That raises complexity well beyond what existing enterprise platforms were designed to handle. Agentic systems are connected, nondeterministic, and multi-agent—agents do not operate in isolation. Operationally, each agent may invoke APIs, execute code in sandboxed environments, query knowledge bases, use vector or graph indexes, and pass context to downstream agents within a single request.

Few companies are ready. Capturing the full value of agentic AI will require rethinking systems, data, and governance. For scalable, safe agent deployment across the enterprise, leaders must build in guardrails from the start, rather than bolting them on later. Agentic AI calls for a centralized policy that governs agent access, runtime guardrails like prompt-injection filtering and content safety controls, and end-to-end traceability.

To meet the new requirements of agentic AI, organizations are modernizing their platforms around a three-layer architecture:

1. Orchestration. The application and orchestration layer is the command center. It manages multistep workflows end to end with tool calls, context handoffs, and execution controls across agents within an application.
2. Observability. The analytics and insight layer provides real-time visibility with metrics, logs, and traces across agents, workflows, and infrastructure. Full reasoning-path traceability captures every step for human audit.
3. Governed data access. This is the data foundation for agentic systems. It gives agents consistent, governed access to structured and unstructured data across domains through standardized interfaces.

Finally, agentic AI is a business transformation, not just a technology rollout. Leaders will be strategic about where to deploy agents, clear on when to keep humans in the loop, and bold in placing bets that move the organization beyond incremental productivity gains.

What are the levels of agentic AI maturity?

The underlying progression of agentic AI maturity crystallizes into four levels.

Level	Description	Enterprise example
Level 1	Large language model (LLM)-powered information retrieval agents	Knowledge assistants and copilots
Level 2	Single-task agentic workflows	Task agents with self-contained action loops
Level 3	Cross-system agentic workflow orchestration	Supervised agents executing complex workflows across systems
Level 4	Multi-agent constellations	Loosely coupled collaborative agents

Tech-forward companies scaled Level 1 in 2023 and 2024, with varying degrees of success. When deployed in a diffuse way, these so-called agents delivered microproductivity. Real gains required deep embedding in functional workflows with heavy data cleaning and curation as well as continuous high-quality governance.

Levels 2 and 3 are where capital, innovation, and deployment velocity are converging. Level 4 remains earlier-stage, limited by practical barriers including:

• organizational silos;
• a lack of communication standards;
• compounding errors in multistep tasks;
• data readiness;
• concerns around privacy, security, and intellectual property;
• and vendor-fueled standards battles and walled gardens.

 

What makes agentic AI different from earlier AI?

Agentic AI differs from earlier AI in five ways:

1. It is action-oriented. Agentic AI can recommend or take actions autonomously within guardrails. Traditional analytics tools could only describe what happened, but couldn’t reason across fragmented data, adapt to changing context, or support decisions as work unfolded. And earlier automation only focused on parts of a process.
2. It is workflow aware. Early AI was contained and deterministic. We’re now shifting to connected, nondeterministic, multi-agent systems. Agentic AI systems support multistep orchestration workflows.
3. It depends on tools. Agents can discover tools dynamically via Model Context Protocol (MCP) servers and tool catalogs, invoke APIs, execute code, query knowledge bases, and pass context to downstream agents. That calls for governed tool access, including contextual, least-privilege permissions for every tool invocation.
4. It requires a dedicated orchestration layer. With agentic AI, enterprises need a way to coordinate control flow, retries, time-outs, parallel execution, and context handoffs.
5. It raises the governance bar. With agentic AI, governance must expand beyond model outputs to agents’ actions. It necessitates centralized control and observability to govern agent access, runtime guardrails, and end-to-end traceability.
   
   

Where is agentic AI used in enterprises?

Enterprises can use agentic AI wherever work is multistep and requires reasoning and coordination across systems. We’re still early in the journey, but agentic is already delivering value across industries.

Customer experience

Agentic AI is transforming customer experiences by reinventing and improving both the front stage and the backstage. The front stage is what the customer sees and feels. The backstage includes the systems and workflows that power the front.

The best companies are using agentic AI to rethink the backstage in parallel with the front-stage experience. They are autonomously routing service requests, generating content, summarizing customer history, and detecting issues before they become complaints. But they can only create repeatable, scalable outcomes by reinventing the customer-facing layer and the underlying machine simultaneously.

Retail and commerce

In retail, agentic AI could reshape the path between shoppers and products. AI agents can help consumers discover, research, compare, and purchase products, sometimes bypassing traditional websites or marketplaces. Bain estimates AI agents could fuel 15% to 25% of US e-commerce sales by 2030, representing a $300 billion to $500 billion agentic commerce market.

The shopper journey is already starting to change. Retailers now face a dual challenge: They need to serve human shoppers and the agents acting on their behalf. According to a Bain survey, consumers trust retail-owned agents three times more than third-party agents to complete transactions.

The strategic question is not simply whether to participate in agentic commerce. It is where to participate, what to protect, and how to remain visible to both humans and agents. Winning retailers will need to strengthen their on-site value proposition, partner where shoppers are, reinvent retail media, and protect control over data, fulfillment, and checkout where possible.

Marketing

In marketing, AI agents are becoming the new intermediaries between buyers and brands. Consumers and B2B buyers alike increasingly use AI tools for recommendations, comparisons, and research. AI-powered “zero-click” functionality compresses the discovery-to-decision journey, reducing opportunities that brands have to influence consumers directly and differentiate themselves. Leading marketers will adapt by optimizing content for large language models, investing in new performance metrics, and adapting their digital strategy to a world in which the AI agent may be the first audience.

Sales 

In sales, the potential of agentic AI is too great to ignore. Across the sales life cycle, there are good candidates for AI use cases, including:

• lead generation and prospecting;
• high-velocity guided selling;
• customization and brief preparation;
• data and artifacts automation;
• operational planning and visibility;
• and teaming, learning, and development.

To turn that potential into performance, teams will need to identify and prioritize high-value use cases, reimagine processes, and clean up their data. Done well at scale, they can dramatically improve life for frontline sellers and build a durable edge.

Banking and financial services

In financial services, agentic AI can reduce friction in service, payments, onboarding, compliance, financial planning, and client servicing. Banks’ conversational agents can help customers by resolving issues, moving money, providing personalized guidance, and more.

The financial services industry also shows why modernizing platforms and data matters. Modular, industry-standard frameworks, like the Banking Industry Architecture Network, can help accelerate the shift to modern technology foundations. Scalable access to structured and unstructured data will be essential for personalized customer engagement.

Insurance

In insurance, agentic AI can reimagine entire customer journeys. Take auto claims, for example. Insurers can use agents for preventive advice, claim submission, damage assessment, recovery and settlement, and fulfillment. Agentic AI can help redesign that journey by connecting the customer-facing experience to the operational machinery behind it.

ERP and enterprise operations

In enterprise resource planning (ERP) and enterprise operations, agentic AI can help turn passive platforms into dynamic decision-and-execution engines. Rather than work through a software user interface, employees can work directly with AI agents, overseeing “touchless platforms” that reroute workflows or initiate decisions. Core finance and planning processes—including procure to pay, record to report, and forecast to plan—will likely see early gains.

A Bain survey of nearly 500 IT leaders found that 78% expect at least some ERP functionality to be replaced or augmented by agentic AI by 2028. Nearly half expect AI to affect more than 10% of ERP functionality, and 16% expect it to affect more than 25%.

However, many companies remain in pilot mode. Common roadblocks include unclear operating models for human-agent interaction, limited internal skills, immature tooling, weak orchestration, data quality issues, vendor lock-in concerns, lack of executive sponsorship, and uncertain ROI.

Long-running enterprise work

Most AI agents are episodic, performing bounded tasks on demand. Now, long-running agents can maintain goals, preserve context and decisions, eliminate rework, and accumulate domain knowledge. This distinction matters in work that unfolds over days, weeks, or months rather than minutes.

Enterprise and industry-specific workflows are both likely to be early proving grounds for long-running agents. The biggest opportunities include procurement, customer issue resolution, healthcare coordination, claims, compliance, legal matters, and financial services.

Episodic agents are measured transactionally. Long-running agents should be measured developmentally. The value comes not only from faster responses or lower labor costs, but from compounding organizational assets that accumulate judgment, improve performance, and build institutional knowledge over time.

What business value can agentic AI create?

Agentic AI can create business value in several ways.

It creates new growth opportunities for certain industries. Bain estimates that the US agentic commerce market could reach $300 billion to $500 billion by 2030, making up 15% to 25% of total e-commerce sales.

By automating coordination work across systems, agentic AI also converts labor costs into software spending. Bain estimates the potential market of automating the coordination work among systems could be $100 billion in the US. More than 90% of that is still uncaptured.

Across industries, agentic AI can also accelerate decision making and improve operational efficiency. It can improve customer experience by reducing friction and making interactions more personalized.

The biggest gains will likely come from workflow reinvention. AI leaders that have moved from pilots to profits and scaled AI across core workflows have delivered 10% to 25% EBITDA gains. Agentic offers another round of gains for companies that turn their focus from automating tasks to redesigning entire workflows. Companies that successfully capture value will figure out how agents can coexist safely, discover each other, gather context and data, and collaborate productively.

What architecture does agentic AI require?

Agentic AI requires a new enterprise architecture built to support adaptive, multistep, end-to-end actions. Legacy technology stacks were designed largely for request-response interactions, not collaborative agents.

Agents may need to share memory, call tools, invoke APIs, query knowledge bases, execute code in controlled environments, pass context to other agents, and coordinate through multistep workflows. That requires an architecture that can manage orchestration, observability, and governance.

The gap in scaling AI is often architectural rather than aspirational. Moving from experimentation to business impact requires integrated platforms that can manage data and support the build, deployment, and operation of AI applications. These platforms enable dynamic coordination across agents, applications, and data.

Moving from isolated models to connected systems offers four benefits:

1. A unified platform reduces redundancy across systems, data pipelines, and siloed applications, eliminating rework and lowering the marginal cost of new use cases.
2. Shared data and context improve accuracy and timeliness of insights, enabling higher-value end-to-end use cases.
3. Centralized control and observability simplify governance, even in nondeterministic systems where steps and outcomes can vary.
4. Modular architecture enables scalable, reliable execution.

 

What are the three layers of an agentic AI platform?

A modern agentic AI platform has three layers built for orchestration, visibility, and governed data access.

The application and orchestration layer acts as the command center. It routes requests, manages workflow control and context handoffs, and coordinates agent-to-agent communication. It handles identity and policy enforcement, observability and audit tooling, and centralized registries and catalogs for agents, tools, and entitlements.

The analytics and insight layer makes agent activity visible and traceable. It tracks every step so that teams can audit and explain agent decisions.

The data and knowledge layer serves as the data foundation, giving agents access to the structured and unstructured data they need. It unifies data across domains so agents have consistent, governed access on which they can rely.

Security and governance cannot be added after deployment. They need to be designed into the platform from the start.

What data foundation does agentic AI need?

Agentic AI needs a data foundation that supports scalable access to structured and unstructured data. Agents don’t just analyze this data. They act on it, powering workflows, making decisions, and handling customer tasks autonomously. Without reliable, well-governed data, agentic AI risks acting on flawed inputs. That’s damaging to performance and trust.

Most enterprise data is unstructured and untapped. That’s a missed opportunity and a growing liability, as agentic AI relies on context-rich, high-quality input.

In agentic AI architecture, the data platform must make unstructured content usable and trustworthy at runtime. It turns human-centric content—documents, emails, transcripts, images, PDFs, and other raw materials of business knowledge—into agent-ready assets, so that agents can discover, retrieve, deduplicate, and reason over the right context for the task at hand. Without the right metadata and structure, AI models can fall prey to hallucinations, inaccuracies, and missed insights. The old rule still holds: flawed inputs create flawed outputs.

AI efforts often launch as standalone initiatives, only to discover that the data demands exceed what is required for traditional reporting. Common barriers include fragmented data, monolithic data lakes, unclear ownership, and governance that is too narrow for AI deployment.

A strong data foundation should include:

• unified enterprise data access across domains;
• governance for structured and unstructured data with masking, retention, and cross-domain access controls;
• real-time streaming pipelines to ensure agents operate on current, rather than stale, data;
• and automatically enriched metadata to tag, classify, and contextualize content.

How do CIOs think about agentic AI?

Leading CIOs are thinking about how to move quickly on agentic AI, but few have a roadmap in place. A March 2025 Bain survey of 200 B2B IT decision makers found that 48% of respondents with AI pilots were piloting agentic AI, compared with 79% piloting generative AI. Only 8% reported having no AI solutions.

Deployment plans vary sharply. Only 20% of B2B IT decision makers say they plan to expand their use of agentic AI rapidly, while 72% say they will take a more gradual adoption approach.

The business case is not limited to cost reduction. Among respondents, 35% say they are focused on cost savings, 21% on revenue growth, and 39% on achieving both.

CIOs also see agentic AI applying across a wide range of functions. The most cited opportunities were workflow automation, at 72%; customer support and sales automation, at 60%; data analysis, at 51%; IT operations and cybersecurity, at 48%; AI-powered productivity and personal assistants, at 39%; and software development and product design, at 36%.

What risks and challenges come with agentic AI?

Agentic AI brings several risks and challenges to companies, including:

• Legacy architecture. Agentic systems need shared context, orchestration, and runtime governance to support multi-turn, adaptive workflows. Legacy stacks were never built to provide these capabilities.
• Interoperability and integration. Consistent interoperability standards and frictionless integrations are critical to break down organizational silos and capture the full value of agentic AI.
• Data quality and access. Agents can’t act without governed access to structured and unstructured data.
• Governance and decision transparency. As agents take on more decision making, organizations need the ability to observe and explain agent behavior.
• Privacy, security, and intellectual property concerns. For agentic AI to scale safely across the enterprise, the guardrails to execute safely, securely, and cost-effectively must be built in from the start. This is essential to mitigate operational, customer, compliance, and reputational risk.
• Trust and judgment. Customers and employees need to feel confident as agents become more autonomous and cross-functional. It’s up to leaders to determine where to deploy agents and where to keep humans in the loop.

These challenges are not reasons to wait; they are reasons to design agentic AI strategically.

How will agentic AI change the workforce and operating model?

Agentic AI will transform the workforce and the operating model by shifting people from task executors to AI supervisors. Agents democratize technical skills, increasing the breadth of employee roles. They will redefine decision making, speeding up decision cycles and increasing employee autonomy. As agents expedite work, blurring boundaries between roles and organizing work around outcomes rather than functions, human collaboration is critical.

Agents increase the risk of unintended consequences, expanding the risk management role. Agentic AI makes judgment, not capacity, the scarce organizational resource. As agents shift the focus from who does work to who owns the work, org charts become “accountability charts.”

Long-running agents add another layer. Most agents today are episodic: They perform a bounded task, then lose context between sessions. Long-running agents maintain goals, preserve decisions, and accumulate knowledge across extended workflows. That could shift AI from a transactional productivity tool toward a more persistent operational capability.

But persistence raises new questions. Who owns the memory an agent accumulates? How should permissions change over time? How will companies keep memory clean, portable, secure, and governed? These questions will shape the next enterprise operating model.

How should leaders get started with agentic AI?

Leaders should get started with agentic AI by treating it as a business transformation, not a technology rollout.

Focus on the workflows where agentic AI can change performance

The best starting points are real, high-impact use cases that can pull the organization forward. The “let a thousand flowers bloom” approach is not always the best course. We’re still early in the agentic era, with limited adoption and results—for now. Leading companies will focus on graduating AI experiments into meaningful bets. Those leaders who understand how the business creates value will be best positioned to identify and allocate resources to pursue bigger agentic opportunities.

Redesign the workflow, not just the task

Rethinking the end-to-end process is what yields real productivity. This requires a top-down approach. Agentic AI’s value doesn’t lie in the incremental efficiency gain from improving one step of a process.

Build the foundation early

Leading organizations start with building the foundation. That includes:

• data governance and quality frameworks;
• centralized policy enforcement and compliance controls;
• an observability layer for metrics, logs, and tracing across agents and workflows;
• and a security baseline that includes runtime guardrails, identity management for nonhuman principals, and prompt-level protections.

Deploy the orchestration layer

Agents need multistep workflow engines, MCP-based tool abstractions, an agent registry for discovery and life cycle management, agent-to-agent (A2A) communication protocols for controlled collaboration, and memory management for both session-level and long-term context persistence.

Scale across the enterprise

Organizations can extend orchestration across applications and domains. They can enable autonomous multi-agent collaboration with broader decision authority, thanks to the governance built into their foundation. In the scaling phase, the platform can support cross-domain agentic operations, not just individual applications. This is where agentic AI starts delivering real value, by reducing effort, controlling costs, and ensuring outcomes.

The companies that win will move now, but pragmatically. With the right foundation in place, they will build a durable engine for intelligence, agility, and growth.