The buzz around Agentic AI is reaching its peak this year. Finally, companies can have access to autonomous systems that are not simply limited to being advisors, but are able to act. They can orchestrate complex workflows, make decisions, and drive tangible business outcomes, capable of catching the attention of your stakeholders. But before you jump on the bandwagon, a critical question must be asked: Is your organization truly ready?
Adopting Agentic AI isn't like deploying a basic new program or a tool. It’s a fundamental shift in how work is executed, requiring a strong operational and technological foundation. This guide will help you perform a self-assessment, using frameworks from industry leaders like Xebia and AWS, to determine if your company is prepared to harness the power of autonomous agents.
Beyond the Hype: What is Agentic AI Really?
First, let's start by clarifying what we're assessing readiness for. Unlike traditional rule-based automation or generative AI copilots that assist with tasks, Agentic AI is a new step: creating autonomous software agents. These agents are given a business goal, for example, they can be to onboard a new customer or resolve a supply chain disruption. These agents are empowered to independently plan, reason, and execute across multiple systems (CRMs, ERPs, databases) to achieve it. They are able to operate within strict policy guardrails, while providing full audit trails, and can escalate to a human only when it is truly needed.
The agentic AI potential is staggering. McKinsey reports that early adopters are seeing 30–50% improvements in process efficiency and a 20% uplift in customer satisfaction. But these results are only possible with the right foundation in place.
The Xebia readiness framework: A 5-Level maturity model
In order to check if your company is ready to work with a completely new level of technology, that is also “a shift in enterprise accountability”, according to Mayank Verma, Xebia’s global head of Data & AI, there are a few things to check and questions to ask.
Xebia’s framework helps you diagnose your current state across five key levels:
Self-Assessment Check: Where does your organization primarily operate? If you’re at L2 or below, there is important work to be carried out. Reaching L3 is often the prerequisite to deploy efficient and successful Agentic AI pilots.
The four pillars of Agentic AI readiness
Based on this framework, your self-assessment should focus on four core pillars:
1. Defining outcomes & processes in a clear way
- Question: Can you identify a high-friction process in your organization with a well-defined, measurable goal (e.g., "reduce customer onboarding time from three days to two hours")?
- Why it matters: AI agents need a clear objective, vague and generic goals will only lead to failed experiments. Start with processes that involve workflows across multiple departments and systems.
2. System orchestration & API accessibility
- Question: Are your backend systems (CRM, ERP, data lakes) modernized with accessible APIs? Can they be orchestrated to work together?
- Why it matters: An agent’s power comes from its ability to act across your systems and tech stack. If those systems are isolated, siloed or lack APIs, even the best agent would be incapable of working in an effective manner.
3. Governance, guardrails, and audit trails
- Question: Do you have well-defined and clear scoped access policies and escalation paths? Can you track and explain every decision a system makes?
- Why it matters: This is non-negotiable, especially in regulated industries such as banking and healthcare. Autonomous action must be responsible and traceable, and an audit trail should be easy to read and identify at all times. Without governance, Agentic AI can easily turn into a black box of unforeseeable risk.
4. Observability and a Culture of Trust
- Question: Do you have the tools to monitor, review, and roll back automated decisions? Is your organization culturally prepared to trust assisted outcomes?
- Why it matters: Technical systems need constant and mature monitoring. More importantly, people need to understand and trust the agent's work. Change management is essential, along with how tech culture can assist workers in trusting AI.
How AWS Provides the governance bedrock for trusted agents
Readiness isn't simply about identifying your internal state; it's about choosing the right platform. AWS offers a suite of tools designed specifically to address the governance and security concerns of Agentic AI, making it safer to adopt.
- Amazon Bedrock Guardrails: This is a critical differentiator. Guardrails allow you to set strict policies that define the type and number of topics an agent can address, what actions it can take, and what content it is allowed to generate. It also filters harmful content and prevents off-topic conversations, ensuring agents operate within your compliance requirements.
- AWS CloudTrail & CloudWatch: These services provide a non-negotiable audit trail. Every action an agent takes, be that an API call or a policy decision, is logged via CloudTrail and can also be monitored in real-time by using CloudWatch. This fulfills the need for full transparency and observability.
- IAM Roles and Security: Agents built on AWS operate inside your existing cloud tenancy under strict AWS Identity and Access Management (IAM) roles. This means they only have the permissions you explicitly grant them, adhering to the principle of least privilege.
Assessing readiness: case studies
These companies assessed their readiness and found ideal Agentic AI use cases:
- Formula 1 shifting gears with RCA: F1 managed to reduce race weekend technical issue resolution from three weeks to a few days. They leveraged Amazon Bedrock Agents to create an intelligent Root Cause Analysis (RCA) agent. The agent connects multiple systems, enables natural language troubleshooting, and supports engineers across experience levels. The result? Transformative. An 86% reduction in resolution time, moving from reactive firefighting to proactive problem-solving.
- Rocket Mortgage (Financial Services): Their goal was to simplify complex home financing decisions and deliver hyper-personalized lending at scale. Using a massive data foundation and Amazon Bedrock, they built an agent that provides real-time, personalized mortgage recommendations. The outcome was measurable, with a 40% faster query resolution and a 65% improvement in personalization accuracy.
These cases exemplify how Agentic AI can improve decision-making journeys in high-stakes, data-intensive scenarios. These agents will improve both customer satisfaction and operational efficiency across different scenarios and use cases.
Your Agentic AI Readiness Checklist
Use this list to start a conversation in your company and check whether you are ready to implement Agentic AI:
- We have identified a high-impact, cross-system process with a clear goal.
- We have leadership buy-in and are preparing for a cultural shift towards assisted decision-making.
- Our key backend systems (CRM, ERP, etc.) have modern, accessible APIs.
- We have defined access control policies and human escalation paths.
- We have tools (or can implement them) for logging and monitoring automated decisions (e.g., CloudTrail).
- We operate at least at a Level L3 (Policy-Bound Execution) maturity.
Game, set, match... and execute
Agentic AI is not a distant future concept; it’s a present-day opportunity for enterprises that have laid the groundwork. Readiness is less about having the most advanced AI team and the best people working on it, and more about having defined processes, well-planned systems, and a commitment to governed, responsible deployment.
If your self-assessment reveals gaps, now is the best time to address them. If you see alignment, the next step is to partner with experts who can guide your pilot. With the combined power of Xebia’s implementation experience and AWS’s governed, secure infrastructure, you can move from being ready to being a leader in the autonomous enterprise revolution.
The question is no longer if Agentic AI will transform your industry, but whether you will be among the first to execute it correctly.
Written by
Ronnie Bagan
Ronnie Bagan-Global AWS growth lead at Xebia with over 20 years of experience with B2B sales and sales training, specializing in cloud services. A vast experience in building business strategies focusing on digital transformation, agility, and innovation, particularly with AWS. Her passion lies in supporting my customers with their cloud journey, focusing on their technical requirements, while aligning with a full cloud strategy that also incorporates FinOps and GreenOps as well as data focus and security, to achieve their goals.
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