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A Guide to Adopting AI Agents Using the Cloud Adoption Framework (CAF)
Introduction 🧭
We are hearing more and more about AI agents. They go beyond simply answering questions in a chat; they understand goals, search for necessary information, call tools, and in some cases, take action within business systems. This form of software has become a highly realistic option.
On the other hand, when trying to introduce them as an organization, there is much more to consider than just "which tools to use." Which business processes should be applied? What data should they have access to? Who stops them if they fail? Are audit logs kept? And above all, at what stage do we call it "production use"?
When I think about these things, I find the perspective of the Azure Cloud Adoption Framework (CAF) to be quite helpful. While CAF is a framework for cloud adoption, it can also be used as a lens to organize "the order in which an organization should think about things" for AI agent adoption ☁️
In this article, for those familiar with CAF, I will think about AI agent adoption by mapping it to each phase of CAF. The focus is not just on introducing tools, but on what to decide first, how to expand in stages, and where to place governance.
How to perceive AI agents in the first place 🤖
In this article, I perceive AI agents with a granularity of "software entities that use LLMs (Large Language Models) to plan and execute tasks to achieve goals."
In Microsoft's AI agent adoption guidance, AI agents are described as flexible software programs that use generative AI models to interpret input, reason about problems, and decide on appropriate actions. Additionally, the components listed are generative AI models, instructions, retrieval, actions, and memory.
In other words, it is a slightly broader concept than just "a chatbot that searches company documents and answers questions." If it's just about searching and answering, a Retrieval-Augmented Generation (RAG) application might be sufficient. However, agents progress in the direction of deciding which knowledge to use, which tools to call, and what to do next depending on the situation.
This difference is a difference in convenience, but also a difference in risk. The necessary controls change between a system that only returns information and a system that makes changes to business systems. If you say "let's adopt AI agents" while leaving this ambiguous, both expectations and the scope of responsibility will expand.
In the AI agent adoption guidance, productivity agents, action agents, and automation agents are introduced as agent types. I find it easy to understand this classification if I also see it as the maturity of the adoption stage.
| Type | Main Role | Key Points for Adoption |
|---|---|---|
| 📚 Productivity agents | Information search, summarization, decision support | Which information sources to trust, how to show the basis for answers |
| 🛠️ Action agents | Execution of defined business actions | How to design permissions, approvals, revocations, and audit logs |
| 🔁 Automation agents | Autonomous multi-step processing | How to set stop conditions, exception handling, and human escalation |
If you aim for automation agents from the start, it is easy to get stuck in organizational design before technology. It is more realistic to start by learning about value and risk with productivity agents, solidify permissions and audits with action agents, and then begin with areas where automation agents can be sufficiently controlled.
AI agents are not just automation tools, but entities that can be involved in business with authority. That is why organizing them from the perspective of CAF, which can handle adoption, control, and operation together, makes it easier to find omissions in discussions.
The Relationship between CAF's 7 Methodologies and AI Adoption ☁️
In the CAF overview, it is explained that the Cloud Adoption Framework consists of seven methodologies: Strategy, Plan, Ready, Adopt, Govern, Secure, and Manage. Strategy, Plan, Ready, and Adopt are foundational methodologies, while Govern, Secure, and Manage are operational methodologies.
Note that in CAF's AI adoption scenarios, these are organized into six steps: AI Strategy, AI Plan, AI Ready, Govern AI, Secure AI, and Manage AI. In this article, I will use the seven methodologies of the entire CAF as a lens for AI agent adoption, incorporating Adopt as well.
For AI agent adoption, this flow applies quite naturally. However, with AI agents, it is necessary to consider not only "what the model returns" but also "what the agent is allowed to do." Therefore, treating Govern, Secure, and Manage not as subsequent phases but as things to run in parallel from the beginning seems appropriate.
The good thing about CAF is that it doesn't end with just "building" a technology adoption. The same applies to AI agents; to turn them into a system that continues to be used within an organization from a PoC, you need strategy, planning, environment, adoption, governance, protection, and management.
First, here is the overview mapping:
| CAF methodology | Considerations for AI agent adoption |
|---|---|
| 🎯 Strategy | What outcomes to improve, what judgments to delegate or not delegate |
| 📝 Plan | How to plan use cases, data, skills, PoC, and Responsible AI |
| 🏗️ Ready | How to prepare the environment, boundaries, connections, and landing zones to run agents safely |
| 🚀 Adopt | How to phase in permissions, from read-only to actions with approval |
| 🛡️ Govern | How to decide on use case registration, owners, exceptions, audits, and stop conditions |
| 🔒 Secure | How to implement least privilege, auditability, circuit breakers, and independent guardrails |
| ⚙️ Manage | How to continuously operate usage, quality, cost, data freshness, and behavior |
Strategy: Deciding What You Want to Achieve 🎯
The first thing to decide in Strategy is not "what to automate with AI agents," but "which business outcomes you want to improve."
Because AI agents are an attractive technology, there is a tendency to start with the means, saying things like "I want to make inquiries into an agent" or "I want to automate internal applications." However, if you align with CAF's Strategy thinking, you must first articulate the outcomes you aim for with AI agents, just as you would map business drivers to cloud outcomes.
For example, starting with questions like the following makes it easier to organize:
- 🧑💼 Employee Experience: Do you want to reduce the time spent on information retrieval or routine tasks?
- 🧾 Business Quality: Do you want to reduce errors caused by manual entry or transcription?
- 🧪 Development Productivity: Do you want to shorten the cycle of investigation, implementation, review, and operational response?
- 🧭 Decision Making: Do you want to consolidate scattered information to bring out decision-making material faster?
What is important here is to separate the "tasks you want to delegate" to the AI agent from the "judgments that must not be delegated." Even if you delegate primary responses to inquiries, humans make the final decision on contract terms. Even if you delegate investigations into incident responses, approvals are mandatory for changes to production environments. Such boundaries should be decided before implementation.
AI agents act like small entities within a business. Therefore, it is necessary to clearly define "what this entity exists for" at the very beginning of the adoption. The outcomes defined here become the criteria for subsequent use case selection, permission design, and operational design.
Plan: Incorporating Skills, Data, PoC, and Responsible AI into the Plan 📝
In Plan, you translate the outcomes defined in Strategy into an actionable roadmap from the perspectives of use cases, data, risks, and skills. Microsoft's Plan for AI adoption points out the perspectives of early incorporation of AI skill assessment, AI maturity, inventory of data assets, PoC, and Responsible AI.
What is particularly important in AI agent adoption is not to list use cases just in order of "high value." Even if the value is high, if the data is not organized, permission design is difficult, or the impact of failure is large, it may be too heavy as a first target for adoption.
For me, I prioritize based on axes such as whether the output is limited to summarization or candidate suggestions, whether it involves external transmission or production updates, and whether humans can confirm and execute it. Internal knowledge search, organizing meeting notes, and creating draft responses for support staff are areas where it is easier to learn the quality of retrieval and instructions, while suppressing direct impact on the outside.
It is also important not to postpone Responsible AI at the Plan stage. Trying to "add audit logs" or "add prohibitions" later often results in having to redesign the agent's entire architecture.
In Plan, you plan not only the technical roadmap but also skill acquisition, data preparation, responsibility boundaries, and evaluation methods. If this is ambiguous, the PoC might be fun, but you will get stuck at production deployment.
Next, you will prepare the environment and boundaries where that plan can be tested safely in Ready.
Ready: Preparing the Place and Boundaries for Agents 🏗️
In Ready, you prepare the environment to host AI workloads. In AI Ready, topics such as AI governance, networking, reliability, Azure landing zones, and the separation of internal/internet-facing workloads are covered.
Plan is the phase where you decide "what to test and in what order." Ready, on the other hand, is the phase where you prepare the location and boundaries to run that safely.
In AI agent adoption, this concept of "boundaries" is very important. Agents access knowledge, call tools, and in some cases, interact with external users. For an agent for employees referencing internal regulations versus an agent receiving inquiries from customers, the data, authentication, networking, and impact in case of failure all differ.
For organizations using Azure landing zones, the idea of hosting AI workloads in regular application landing zones is natural. However, because AI agents combine models, search, data, APIs, and monitoring, you must design boundaries based on the "scope the agent can touch" rather than just by resource.
Which subscription, which management group, which network boundary, and under which policy inheritance should the agent run? Deciding these in Ready makes the subsequent Adopt phase much easier to proceed with.
Adopt: Start Small and Phase in Permissions 🚀
In Adopt, you actually deploy the workload. In the case of AI agents, I believe it is more important to "gradually expand permissions" before "expanding the target business operations."
At first, keep the agent read-only. Answer user questions, find relevant documents, or suggest work procedures. Next, have it create drafts or proposals. These could be ticket descriptions, email drafts, or work plans. Then, within a sufficiently evaluated scope, move to actions with approval.
| Stage | What to allow the agent to do | Human involvement |
|---|---|---|
| 1 | 📖 Read, search, summarize | Human decides whether to use the response |
| 2 | 📝 Drafting, candidate creation, classification | Human confirms and sends/registers |
| 3 | ✅ Actions with approval | Executed after human approval |
| 4 | 🔁 Conditional automatic execution | Escalate to human in case of exceptions |
This stage design is also highly compatible with the ideas of productivity agents, action agents, and automation agents in the AI agent adoption guidance. Instead of aiming for autonomous execution immediately, start with productivity support, limit business actions, and finally expand the range of automation.
What I don't want to forget in Adopt is the evaluation perspective. You need to prepare expected questions, exception cases, cases of insufficient permissions, inappropriate inputs, and cases where outdated data is mixed in, and observe under what conditions it fails. Do not answer vaguely when you cannot provide an answer. Do not execute when you do not have permission. Hand over to a human when you are not confident. Reflect such behaviors in the design.
Adopt here covers the "principle of phased adoption." In the final section, "How I would implement phased adoption," I will translate this principle back into the CAF perspective and organize it as a roadmap.
Govern: Governance is the Design of Boundaries and Exceptions 🛡️
Govern is one of the areas that should be discussed earliest in AI agent adoption. This is because agents intrude into human tasks and serve as a link between data and actions.
Governance here does not mean "approving everything centrally." I think it means clarifying boundaries and exception handling so that the field can experiment safely.
- 🧭 Use Case Registration: Which department, for what purpose, and using which data?
- 🧑⚖️ Owner: Who is the owner of the agent's output, actions, and improvement decisions?
- 🗂️ Data Classification: What data is okay to use, and what data requires additional screening?
- 🔐 Permission Model: How do you combine the agent's own permissions with the user's permissions?
- 🧾 Audit: Who, when, what was input, and what was referenced/executed?
- 🚦 Stop Conditions: What anomalies should be detected to stop, and who decides on restarting?
Because AI agents are convenient, they easily become "rogue agents." If thinking in CAF terms, governance is not a brake but a guardrail. You create a lane for the field to run in safely and ensure you can detect when they deviate. This mindset is crucial.
However, you cannot protect everything just by deciding on rules. Next, let's look at the perspective of Secure, which technically enforces, detects, and stops actions.
Secure: Assume Agentic AI Safeguards 🔒
In Secure, we think about risks specific to AI agents. In Responsible AI in Azure workloads, perspectives such as auditability, RBAC, circuit breakers (mechanisms to stop processing in case of anomalies), robust data ingress/egress, data validation/integrity, and independent guardrails are indicated as agentic AI safeguards.
Security for AI agents is not sufficient if it only protects the model API. Everything—input, search targets, tool invocation, output, external transmission, and execution results—becomes a subject of protection.
What I want to place first in Secure are the following three:
- Least Privilege: Do not give the agent more permissions than a human.
- Auditability: Make it possible to track the agent's decision-making materials and actions.
- Stoppability: Be able to stop like a circuit breaker when there are dangerous signs.
Also, it is better not to close guardrails only within the agent's instructions. You need a design that protects with independent layers, such as access control, network boundaries, data validation, external monitoring, and approval flows.
Govern, Secure, and Manage might look like similar terms. I think of Govern as organizational rules and exception design, Secure as technical enforcement, detection, and stopping, and Manage as dealing with continuous operation after adoption.
Manage: Operate Behavior, Don't Just Build and Stop ⚙️
In Manage, you operate AI agents continuously. In traditional application operation, you look at availability, performance, cost, and error rates. For AI agents, you also need to keep watching their "behavior."
For example, you check which departments are using it, whether it is answering based on the expected rationale, and whether the executed processing resulted in the correct business outcome. At the same time, you check whether the cost is within expectations, whether erroneous answers or operations are occurring, and whether grounding data is becoming outdated.
AI agent quality changes due to business rules, internal documents, APIs, user expectations, and updates on the model or service side. Therefore, it is not stable once released; it requires an operation that continuously evaluates, improves, and returns permissions if necessary.
How I Would Implement Phased Adoption 🪜
Based on the above, I would proceed in the order of "Clarify Strategy → Learn with Read-Only PoC → Expand to Drafts and Proposals → Test Actions with Approval → Move to Limited Automation."
| Stage | Primary CAF Perspectives | Goal |
|---|---|---|
| 1. Clarify Strategy | Strategy / Govern | Decide outcomes, owners, and judgments not to delegate |
| 2. Read-Only PoC | Plan / Ready / Govern | Learn data, search quality, and user expectations |
| 3. Drafts/Proposals | Adopt / Manage | Incorporate into work within a scope humans can confirm |
| 4. Actions with Approval | Adopt / Secure / Manage | Verify permissions, auditing, approval, and cancellation |
| 5. Limited Automation | Govern / Secure / Manage | Expand within the range where stop conditions and operational structures exist |
The point is to prioritize the organization's learning speed over technical sophistication. Start from the range humans can confirm, create an observable state, possess mechanisms to stop, and then gradually expand the range of delegation. I believe this is a realistic way to proceed.
Conclusion 🌱
AI agents will likely enter many business processes from here on. That is why it is a waste to end adoption discussions solely on tool comparisons.
Seen from the CAF perspective, AI agent adoption involves not just Strategy, Plan, Ready, and Adopt, but also Govern, Secure, and Manage from the beginning. In particular, once agents begin to hold actions, governance and security cannot be retrofitted.
When I think about AI agents myself, I want to think first about "how safely I can delegate" rather than "how smart I can make it." I start small in the scope of delegation, observe, learn, and roll back if necessary. I believe that accumulation makes AI agent adoption as an organization a reality.
CAF is a framework for cloud adoption, but it is also a very good map when rooting new technologies like AI agents in an organization. By using CAF, it becomes easier to treat AI agent adoption not just as feature introduction, but as organizational transformation, including outcomes, environment, governance, and operation. With a map in hand, I'd like to take it one step at a time 🧭
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