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Beyond the hype: Why agentic AI is closer than you think
Accenture Center for Advanced AI
3-MINUTE READ
July 2, 2025
Introduction
We’re hearing a lot of talk about where we really stand with agentic AI. Is the hype outpacing reality? Or are we actually further along than most realize? In our view, the evolution from simple prompting to true agentic systems is happening faster than people expect—driven by real signals of standardization and interoperability. The question isn’t whether we’ll get there, but how quickly we can scale the value.
Businesses today feel increasing pressure to unlock real productivity gains, deeper reasoning, and fully integrated workflows. Meeting this challenge requires moving beyond simple Q&A to AI that can plan, act, and collaborate autonomously through AI agents. Understanding where we are on this journey helps organizations invest in the right capabilities now, so they don’t get left behind.
From prompts to agents: The stages of evolution
Figure 1: Performance Hierarchy from Successive Stages of AI Innovation
As figure 1 illustrates, the recent evolution of AI follows three key stages, each unlocking higher levels of performance. The release of ChatGPT in November 2022 was a pivotal inflection point—catalyzing a move from classical AI to generative AI. Since then, progression has moved through two major post-generative phases: Single Agent and Multi-Agent Systems.
The first stage, often referred to as “one-shot AI,” centers on users directly interacting with large language models (LLMs) through natural language prompts. While powerful, these models are typically trained on public data and lack enterprise-specific context. To bridge this gap, many organizations employ Retrieval-Augmented Generation (RAG), which supplements LLMs with enterprise data through a search mechanism to generate more relevant and accurate responses. 80% of the 2,000 projects Accenture has done so far involved this pattern. It’s a common way to start a proof of concept (POC) to experiment the initial power of AI.
These patterns function as general-purpose task processors—effective for retrieving information or summarizing content but are inherently reactive. They don’t plan, reflect, or act autonomously, making them suitable only for narrow, query-based/prompt-based tasks.
Why single agents aren’t enough
The second stage goes beyond prompting and introduces AI Agents—single agent working on top of LLMs that add planning capabilities, sequential reasoning, and external tools (e.g., calculator, search engine) to plan, reflect, act autonomously, and pursue goals. These are cognitive and action-oriented capabilities that do not exist in the first stage.
For example, a Research Agent is designed much like a human researcher: it tackles an ambiguously defined question, pulls information from diverse sources, reasons over that information, and synthesizes its own point of view. Each single agent like the Research Agent has a clear profile definition and specialty along with memory, planning, and action capabilities so that it can act like a trained apprentice focused on one task.
Most ecosystem agents (e.g., Agentforce) fall into this category. However, these agents are often confined to the ecosystem they’re built in and lack the ability to interact or coordinate with agents in other systems. Without orchestration frameworks to connect these specialized utility agents, organizations cannot tackle more complex, multi-step workflows that require dynamic collaboration. This is why single agents alone aren’t enough to drive true enterprise-scale transformation.
The multi-agent future
The third and most advanced stage mirrors the way human teams operate. This is what is known as Multi-Agent Systems, which are composed of multiple specialized agents that coordinate with each other and dynamically allocate subtasks within complex workflows. Compared to the second stage (single agents), multi-agent systems require orchestration frameworks, agent-to-agent communication protocols, more robust memory management, and deeper enterprise integration. This architectural leap unlocks significantly higher cognitive performance. For example, these systems are now capable of coordinating decisions across domains, such as marketing, finance, HR, and supply chain—not in silos, but as integrated intelligence.
Through Accenture's Trusted Agent Huddle, Accenture-built agents can work seamlessly alongside agents from other enterprise platforms to make this cross-domain integrated intelligence a reality. To achieve true reinvention and enterprise-scale impact, organizations must move toward this stage—up the performance hierarchy and into the agentic AI era.
Figure 2: Multi-agent example
Figure 2 illustrates how a multi-agent system works in practice. In this telecommunications industry example, a user’s goal, such as understanding AAA membership benefits and optimizing an iPhone purchase, is fulfilled by a coordinated team of specialized agents. This example represents the kind of complex workflow we often see in real-life enterprise scenarios.
Each agent has a defined profile: a Research Agent gathers information from trusted sources, an Analytics Agent processes and interprets the data, and a Validation Agent cross-checks and refines the results. These agents work together through an orchestration framework, using agent-to-agent and model context protocols. This collaborative approach enables dynamic planning, decomposition of tasks, robust memory mechanisms, and continuous self-reflection and learning. By integrating these capabilities, the system achieves higher cognitive performance than any single agent working alone, demonstrating the leap from isolated tasks to complex, cross-domain workflows that characterize the power of the agentic AI era.
Standards fuel acceleration
Figure 3: Where Agentic AI Sits on the Innovation Curve
There is a tendency to think multi-agent systems are far off, but early signals suggest otherwise. Building on the Abernathy and Utterback model that describes the evolution of product and process innovation through distinct phases1, figure 3 shows our view that agentic AI is already crossing the gap between early exploration and dominant design.
Why? The recent standardization of protocols (e.g., agent-to-agent communication standards, model context protocols) as a “dominant design” is accelerating adoption. Think of model context protocols (MCP) as giving AI agents a consistent, plug-and-play way to connect with tools, services, and data—no matter where they live or how they’re built. The agent-to-agent standard gives agents a common language to “talk” to each other. These protocols are a clear signal of maturity, enabling scalability and interoperability – critical prerequisites for widespread deployment.
Lessons from history: A familiar tipping point
There’s a historical precedent for this kind of tipping point. In the 1990s and early 2000s, mobile networks were fragmented across competing standards like CDMA and GSM, causing global incompatibility, higher costs, and slow adoption. The eventual convergence on LTE and 5G created a unified foundation that unlocked global interoperability and rapid innovation. Similarly, today’s agentic AI ecosystem employs standardized frameworks and communication protocols.
Moreover, the rate of change and adoption for multi-agent systems is much faster than anticipated, especially due to convergence around standards. For example, a useful parallel is the mobile app revolution: before standardization, developers had to build separately for each device/environment. Once Apple and Google introduced SDKs, standards, and app store guidelines, development scaled rapidly, fueling explosive growth – a global app economy and billions of mobile users. Agentic AI is now at a similar inflection point.
Shaping the curve
As with any platform shift, timing is everything. And because Accenture has been preparing for this wave, we’re not just keeping up, we’re shaping the curve. This is Accenture’s moment to lead. Just as app stores to Apple and Google; and CUDA to NVIDIA; and Pytorch to Meta, we are doing the same for enterprise agentic AI. With AI Refinery, the Trusted Agent Huddle, and the Distiller SDK, we’re building deep and indispensable differentiators that create sustainable customer impact over time. We are making it easier for clients to stay, scale, and grow with us.
This is also the moment for organizations to build on our advantage and turn it into real results. We believe that to get ready for the agentic AI era, organizations should start by identifying the business areas where AI agents can drive real impact; invest in preparing data and knowledge assets so agents can reason and act with confidence; and pilot the first trusted agents on priority workflows, then expand, orchestrate, and scale across functions. By starting in this way, organizations can build the agentic AI capabilities that can grow with every wave of innovation.
Source
1 Utterback, James M. 1994. Mastering the Dynamics of Innovation. Boston: Harvard Business School Press. Archived from the original on January 15, 2024. Retrieved January 14, 2024.