Enterprise AI agents: Implementation roadmap

Agent deployment in Q4 2025 has declined to 26% from 42% in Q3. The reason is that businesses now have more realistic expectations of agentic AI, are beginning to scale their AI agents, and are more thoroughly preparing for agent implementation by establishing a data foundation, AI infrastructure, and governance procedures.

IBM’s CIO, Matt Lyteson, explains what modern businesses can do to succeed with agentic AI:

Our focus is, how do we scale agents across more and more use cases to bring value to the organization, and how do I really understand the outcomes, the data that I’m going to need to give the agents, and then how to manage and control them? If organizations can do that, we’re going to see a lot more adoption and a lot more success.

Over the next few years, the focus will be on building, adopting, and implementing AI agents that are scalable, controllable, and produce measurable results. This will come from a deep understanding of your company’s processes, data management practices, and long-term strategic goals.

In this guide, we’ll discuss how the enterprise agentic AI market has evolved and how to implement domain-specific agents to maximize business benefits. 

How to differentiate between genuine agentic AI and “agent washing”

Before we dive into the latest developments and implementation best practices for agentic AI, it’s important to understand what agentic AI is and how to avoid “agent washing”.

The concept of “agent washing” was introduced by Gartner and refers to offering standard chatbots, AI assistants, and robotic process automation (RPA) as agentic AI. In one of our articles, we show the clear difference between generative and agentic AI.

Vendors of such solutions provide false promises to enterprises, eventually eroding their trust in AI and even causing reputation and financial damage. The confusion over definitions makes it easier for AI vendors to engage in these underhanded tactics. 

A computer scientist and writer, Santiago Valdarrama, gives the following definition of AI agents:

Agents are systems capable of performing tasks dynamically and autonomously. They offer flexibility and model-driven decision-making at scale.

An AI model is an agent’s “brain”; consequently, the level and accuracy of decision-making depend on the model you choose.

Different types of AI systems commonly mistaken for AI agents

Key takeaway: If a vendor cannot explain what actions the agent performs, which systems it touches, and how it’s controlled and audited, you’re likely dealing with a copilot or automation product wearing an “agent” label.

Agentic AI platforms compared: Copilot Studio, Agentforce, Vertex AI & Bedrock

The BCG report revealed that agentic AI accounted for 17% of AI value in 2025 and is expected to reach 29% by 2028. Plus, the gap between AI leaders and laggards is widening due to the emergence of agentic AI capabilities.

Microsoft Copilot Studio

Microsoft Copilot Studio is the natural choice for organizations deeply invested in the Microsoft ecosystem.

They launched major innovations in Q4 2025, including integration with GPT-5 models and other third-party model providers (offering modern model choices for customers) and integrations with more than 1,400 services via a Model Context Protocol (MCP). The company also introduced Agent 365 for enterprise agent orchestration and control. 

Pricing starts at $30 per user per month for Microsoft 365 Copilot. Copilot Studio is included for customers with qualifying Microsoft 365 licenses, with consumption-based pricing for additional capacity.

Best for: Organizations with extensive Microsoft 365 deployments needing cross-functional automation across productivity, CRM, and collaboration tools.

AWS Bedrock AgentCore

AWS Bedrock AgentCore provides maximum model flexibility within a secure enterprise environment. Unlike platform-specific offerings, Bedrock offers access to Claude, Titan, Llama, and other models through a unified API, allowing teams to select the best model for each use case.

AgentCore, launched in late 2025, added policy creation features for granular control over agent actions, real-time performance evaluation, and simplified deployment through a standalone runtime. The platform also supports MCP server integration for standardized tool connections.

Bedrock pricing is based on model inference tokens, with additional charges for agent runtime and knowledge base queries.

Best for: AWS-native organizations requiring multi-model flexibility, strong security controls, and the ability to switch between foundation models without platform lock-in.

Google Vertex AI Agent Builder

Google Vertex AI Agent Builder was enhanced with new observability and deployment tools. Google now allows developers to deploy AI agents with a single command via the Agent Development Kit (ADK), which now also supports the Go programming language, in addition to Python and Java. Simplified deployment and improved observability help enterprises decrease time-to-production and maximize ROI. 

In January 2026, Google also introduced the new agentic commerce protocol, the Universal Commerce Protocol (UCP), to simplify automated commerce by enabling easy connections between customers, retailers, and payment services. 

Vertex AI pricing is consumption-based, with charges for model inference, agent runtime, and data processing.

Best for: Organizations with extensive Google Cloud data infrastructure needing advanced analytics, multimodal capabilities, and BigQuery integration.

Vendors are focusing on customization capabilities, model flexibility, and governance to give enterprises more confidence in their agentic systems and cultivate trusted relationships. Check out our comprehensive guide comparing Google Vertex, Azure AI, and Amazon Bedrock.

When selecting the right agentic AI development and deployment platform for your enterprise, you can follow these recommendations from a Head of Global AI Enablement at MetLife, James Barney:

Look for a system that optimizes the following:

1.uses or supports open source connectors,
2.exposes APIs for invoking agents or collecting information, and
3.works easily within your existing system.

Platform selection framework

Observability, orchestration, governance, and security are non-optional

A survey conducted by Harvard Business Review (HBR) found that multi-agentic systems would be most effective in enterprises, as engaging multiple applications, systems, and steps enables agents to substitute for entire enterprise workflows. 

But for these workflows to function consistently, enterprises need:

• observability (traceable actions and audit logs)
• orchestration (routing, retries, and escalation paths)
• governance (ownership, standards, and data lifecycle control)
• security (authorized access, least privilege, and protection against misuse)

The LinkedIn community is increasingly supporting the claim that only enterprises with proper AI guardrails will succeed with agentic AI. As Patrick Hogan, a Product Owner at  Digital Health Institute for Transformation (DHIT), claims in his post: 

Agentic AI value will be determined as much by control systems as by model capability. Enterprises don’t care if your agent can “think autonomously.” They care if it operates predictably, escalates appropriately, and leaves an audit trail.

As an additional safeguard, companies implement a human-in-the-loop workflow, in which human workers step in to validate, approve, or cancel the agent’s decision. This is particularly important in regulated industries. However, Deloitte forecasts a shift from human-in-the-loop to human-on-the-loop, where humans are involved only as supervisors of the entire agentic AI system, rather than interfering during the task execution.

So what’s changed in the enterprise AI market so far? AI vendors and in-house enterprise teams aim to increase the efficiency and trustworthiness of AI agents. As only 6% of enterprises currently trust these systems, we’ll see many production-ready agentic systems in the near future, with an emphasis on preserving business continuity and secure access to sensitive enterprise data.

Model Context Protocol: The integration standard connecting AI agents to enterprise systems

The most sophisticated AI model is useless if it cannot access your business systems. Model Context Protocol (MCP) has emerged as the universal standard for connecting AI agents to enterprise tools, and its adoption trajectory signals a fundamental shift in how agents will integrate with corporate infrastructure.

Why MCP matters for enterprise agents

Before MCP, connecting an AI agent to enterprise systems required custom integration work for every combination of model and tool. If an organization used five AI models and needed connections to twenty business systems, engineering teams faced 100 potential integration paths, each requiring separate development and maintenance.

MCP solves this N×M problem by establishing a common protocol. Tools expose capabilities through MCP servers; AI models connect through MCP clients. Add a new tool once, and every MCP-compatible model can use it. Add a new model, and it immediately accesses every existing tool connection.

Organizations using standardized integration approaches spend 60% less engineering effort on connectivity compared to those building point-to-point integrations.

Adoption has reached critical mass

One year after Anthropic introduced MCP in November 2024, adoption metrics demonstrate industry-wide acceptance. The protocol has achieved 97 million monthly SDK downloads, with over 5,800 MCP servers and 300 clients in production.

The competitive landscape shifted in early 2025. OpenAI adopted MCP in March 2025, followed by Google DeepMind, Microsoft, and AWS. In December 2025, Anthropic donated MCP governance to the Linux Foundation’s new Agentic AI Foundation (AAIF), cementing its status as an open industry standard rather than a proprietary advantage.

For enterprise teams, this means MCP integration is no longer optional. If your AI agents cannot communicate via MCP, they will be increasingly isolated from the broader ecosystem of tools, models, and orchestration frameworks.

What MCP enables in practice

MCP standardizes three core capabilities that enterprise agents require.

Tool access. Agents can invoke business applications (CRM updates, ticket creation, database queries) through a consistent interface. A procurement agent can check inventory in SAP, create purchase orders in Oracle, and update status in Salesforce using the same protocol patterns.

Context retrieval. Agents can pull relevant information from knowledge bases, document stores, and data warehouses without custom RAG implementations for each source. MCP’s resource primitives standardize how agents request and receive contextual data.

Action orchestration. Multi-agent systems can coordinate via MCP, with agents delegating tasks and sharing results via predefined message formats. This enables complex workflows in which a customer service agent escalates to a technical support agent, which then triggers a logistics agent to place a parts order.

Security considerations

MCP adoption introduces new security surfaces that enterprise teams must address. Agent permissions, tool authentication, and prompt injection vulnerabilities all require explicit governance.

The protocol itself does not enforce security policies. Organizations must implement authorization layers that control which agents can access which tools, audit logging for all MCP transactions, and input validation to prevent prompt injection through tool responses.

For implementation guidance on MCP security patterns, IBM provides a comprehensive technical overview covering enterprise deployment considerations.

Complementary protocols

MCP is not the only standard in the agentic ecosystem. Google’s Agent2Agent (A2A) protocol addresses multi-agent orchestration, defining how agents discover, communicate with, and delegate tasks to other agents. While MCP connects agents to tools, A2A connects agents to each other.

For organizations building multi-agent systems, both protocols will likely be necessary. MCP handles the integration layer; A2A handles the orchestration layer.

How Fortune 500 companies achieve ROI with agentic AI

Fortune 500 companies often run complex multi-step workflows and work with many mission-critical systems, which require robust safeguards to avoid data breaches or cyberattacks. 

Such workflows are the best way to show that if AI agents can provide value without disrupting anything for large organizations, they can be valuable on a smaller scale as well.

Capital One enhanced the car-buying process with the multi-agent system

Capital One has developed an internal multi-agentic AI assistant, Chat Concierge. They built this system using Meta’s open-source Llama model and enriched it with proprietary data. In addition to answering customer queries and providing car information, the agent also performs actions on the customer’s behalf. For instance, it can schedule appointments with the sales representatives

Even though the company, at its core, used an open-source model, they prioritized maintaining a high level of control and adherence to company policies. 

Here’s what Sanjiv Yajnik, President of Financial Services at Capital One, said regarding the results of this agentic AI initiative:

By leveraging our own internally-developed AI tools, we are able to provide personalized, efficient, and transparent interactions which ultimately help us to reimagine car buying and set a new standard for customer experience in the automotive industry.

Walmart builds “super agents” to improve employee, partner, and customer experience

Walmart developed four multi-agent systems, responsible for different aspects of e-commerce (customer shopping, supplier management, employee onboarding, and software development) and called them “super agents”. The retail giant consolidated dozens of AI tools into four separate company-wide frameworks to better orchestrate their use and achieve unified results. 

By scaling agentic AI across many business functions and investing in other AI breakthroughs, Walmart plans on increasing online sales by 50% within the next five years. This is an example of long-term strategic AI planning, where AI technologies are expected to augment existing processes.

Suresh Kumar, a Global Chief Technology Officer at Walmart, wrote:

I believe in the power of agentic AI to transform industries. At Walmart, it’s enhancing the way our customers shop and engage, how we run the business, and how our partners work with us. We’ve been building agents—fast- for every aspect of the business.

Disney invested in AI ad agents to speed up media planning for advertisers

Disney is investing heavily in developing its custom Disney Ads Agent to simplify media planning for advertisers. An agent can automatically search for inventory, identify the target audience, and track media campaign performance and success.

The company wants to combine generative and agentic AI, with generative AI responsible for creating customized ads and agentic AI for helping in running those ads. That’s the level of end-to-end advertising services they want to achieve. 

By entering into an agreement with OpenAI and its project Sora, Disney has taken another confident step towards an AI-ready future, and more is yet to come.

Agentic AI implementation best practices: From pilot to production

According to the Google AI ROI report, 74% of executives report measurable AI ROI within the first year. We’ve analyzed what differentiates companies that gain benefits from agentic AI from those that don’t and composed a list of best practices that might help you better plan your next agentic AI initiative. 

• Differentiate between generative and agentic AI projects. To truly benefit from agentic AI, this technology requires a separate roadmap. There is no one-size-fits-all deployment approach for all AI technologies. For instance, multi-agentic systems require a unique software architecture with communication protocols, such as agent-to-agent and model context protocols.

• Prepare the data. AI agents can work with legacy systems or fragmented data across multiple systems, but you still need to make it accessible to them, ensure it’s high-quality and well-cleaned. That’s where comprehensive data engineering consulting can come in handy.

• Start with expected business outcomes and measure them along the way. Define specific use cases for agentic AI; this could include automating overwhelming HR processes or internal software development projects. And then define the outcomes you expect from using agentic AI, like increased operational efficiency and employee productivity.

• Assign leaders responsible for implementing agentic AI. Such a person could be a Product Owner or, as it’s getting common to assign them now, a Chief AI Officer. A leader is necessary to supervise, manage, and organize the process and ensure it’s aligned with the long-term business strategy.

• Prioritize change management and AI literacy. Training, upskilling, and reskilling your teams to use agentic AI are also among the success factors that differentiate AI ROI leaders from AI laggards. This could be specific training programs that AI vendors can develop for you, workshops with AI engineers, or custom courses on your corporate learning management system (LMS).

• Think big and scale faster. Following Walmart’s example, scale delivers higher ROI and builds your trust in AI as you see cross-company improvements faster.

Rather than fearing failure, Ramanujam Theekshidar, Chief Digital Officer at U.S. Electrical Services suggest completely the opposite:

Have the mindset that there are going to be failures. But mitigate the risk so that if you fail, you learn fast and still deliver business outcomes.

Timeline and cost expectations

These estimates assume dedicated project resources. Organizations attempting agent deployment as a side project for existing teams typically see timelines extend by 50-100%.

Bottom line

The key takeaway is that enterprise AI agents are becoming more popular, and over time, their business value and adoption will only increase. The more enterprises crack the code of their successful adoption, which involves ensuring AI agents fit unique business workflows and establishing rigid guardrails, the more valuable the market will be.

But amid all the hype, it’s important to remain reasonable and adopt agentic AI only when you have a supporting team, a reliable vendor, a solid data foundation, and a clear plan with milestones that help keep a pulse on KPIs. 

Xenoss is one of the few companies that provides all of the above. We support, build, prepare data, and strategize your agentic AI adoption to deliver the fastest ROI possible.