Architecture
The CAE architecture, layer by layer
Seven layers, from where people meet the system down to where a change is real. Pick a platform to see what it covers, or a scenario to see which layers carry the risk and what you still have to build.
Click a layer for the detail. Arrow keys move between layers.
Platform names are illustrative: they show the category of service, not an endorsement or a procurement recommendation, and they change often. Last reviewed 2026-09-24. Coverage is a judgement about the platform as bought, not about what you could build on it, and it is the author's interpretation of public information: check current vendor documentation before relying on it. The scenarios are illustrative and do not describe any real organisation.
The same information as text
The seven layers
1. Experience and workflow
Where people meet the system, and where the work actually arrives.
A ticket, a chat, an inbox, an IDE, a line-of-business screen. This layer decides what a person sees before and after an agent acts, and it is where approval and escalation are felt. Design it badly and every other control becomes a rubber stamp.
Components. Assistant surfaces · Ticket and case queues · Approval screens · Escalation paths · Notifications
What CAE adds. CAE asks one question of this layer: when the agent needs a human, does that human see the proposal, the evidence and the policy in one screen, in time to decide?
| Platform | Cover | Typically |
|---|---|---|
| Anthropic Claude | Native | Claude apps, Claude Code, Claude in Chrome and Excel, Agent Skills |
| Microsoft Azure | Native | Microsoft 365 Copilot, Copilot Studio, Teams and Outlook surfaces, Power Apps |
| AWS | Partial | Amazon Q Business, Amazon Q Developer, your own front ends |
| OpenAI | Native | ChatGPT Enterprise, custom GPTs, your own app on the API |
2. Orchestration and agents
Plans the work, coordinates agents and tools, retries, and knows when to stop.
The loop: plan, act, observe, repeat, and the coordination of several agents over shared state. This is where runaway cost, endless loops and unclear ownership appear first.
Components. Agent runtime · Multi-agent coordination · Shared task state · Workflow engine · Loop budgets
What CAE adds. Loop engineering: a step and cost budget, a progress test and a stop condition checked on every pass, with partial work kept when it stops.
| Platform | Cover | Typically |
|---|---|---|
| Anthropic Claude | Native | Claude Agent SDK, subagents, tool use loop, background tasks |
| Microsoft Azure | Native | Azure AI Foundry Agent Service, Semantic Kernel, Durable Functions, Logic Apps |
| AWS | Native | Bedrock Agents and AgentCore, Strands Agents, Step Functions, EventBridge |
| OpenAI | Native | Agents SDK, Responses API, handoffs and guardrails in the SDK |
3. Reasoning core
The models, and how you choose between them.
Probabilistic by design, and the part everyone argues about. Model choice matters for quality and cost, but no model choice makes an action safe: that is decided two layers down.
Components. Model portfolio · Routing by task and cost · Prompt and skill libraries · Caching · Fine-tunes
What CAE adds. Treat the core as replaceable. Nothing in your control plane should assume a particular model, and swapping one should not require a governance review of everything else.
| Platform | Cover | Typically |
|---|---|---|
| Anthropic Claude | Native | Claude Opus, Sonnet and Haiku, prompt caching, batch processing |
| Microsoft Azure | Native | Azure OpenAI, Foundry model catalogue, provisioned throughput |
| AWS | Native | Amazon Bedrock model choice, Amazon Nova, provisioned throughput |
| OpenAI | Native | GPT model family, reasoning models, fine-tuning, batch API |
4. Context and knowledge
What the agent is allowed to know, assembled per request.
Retrieval, the semantic model, memory and freshness. The governance question here is disclosure: can the agent learn something the person it acts for is not entitled to see?
Components. Permission-aware retrieval · Vector and hybrid search · Enterprise semantic model · Memory · Freshness and provenance
What CAE adds. Context is assembled, not pasted. Retrieval carries the requester's entitlements, and every fact the agent relies on can be cited by reference later.
| Platform | Cover | Typically |
|---|---|---|
| Anthropic Claude | Partial | MCP retrieval servers, Files and Projects, memory tools |
| Microsoft Azure | Native | Azure AI Search, Microsoft Fabric and OneLake, Cosmos DB, Purview classification |
| AWS | Native | Bedrock Knowledge Bases, OpenSearch Serverless, Amazon Kendra, S3 data lake |
| OpenAI | Partial | File search and vector stores, connectors, embeddings API |
5. Action and tools
How the agent touches anything outside itself.
Tool definitions, protocol servers, adapters and the credentials they carry. The set of things an agent can do now grows with a line of configuration, which is exactly why it needs an inventory and a review.
Components. Tool protocol servers (MCP) · Typed action schemas · Integration adapters · Service credentials · Tool inventory
What CAE adds. Agents propose typed actions; they never call a system of record directly. Every path to a change crosses the commit boundary in the layer below.
| Platform | Cover | Typically |
|---|---|---|
| Anthropic Claude | Native | MCP servers and clients, tool use, computer use, connectors |
| Microsoft Azure | Native | Logic Apps connectors, API Management, Azure Functions, MCP support in Foundry |
| AWS | Native | Agent action groups, Lambda tools, API Gateway, MCP servers on AgentCore |
| OpenAI | Native | Function calling, MCP, computer use, code interpreter |
6. Control plane
What the agent may do, decided by code that the model cannot argue with.
Workload identity, scoped and expiring authority, policy as code, the commit boundary, evaluation and the evidence record. Platforms give you strong pieces of this and leave the joins to you: the boundary that checks identity, authority, policy and evidence together, in one place, on every action, is the part you build.
Components. Workload identity · Authority grants · Policy engine · Commit boundary · Evaluation suites · Observability · Evidence records
What CAE adds. This is the layer the book is about. A deterministic shell around a probabilistic core: permit, deny or escalate, and write the evidence on every path.
| Platform | Cover | Typically |
|---|---|---|
| Anthropic Claude | Partial | Workspaces and API keys, usage limits, audit logs, evaluation tooling |
| Microsoft Azure | Partial | Microsoft Entra (human and workload identity), Azure Policy, Purview, Defender for Cloud, Foundry evaluations, Azure Monitor |
| AWS | Partial | IAM and IAM Identity Center, Verified Permissions, Bedrock Guardrails, CloudTrail, CloudWatch, Audit Manager |
| OpenAI | Partial | Organisations and projects, roles and SSO, usage policies, evals, log export |
7. Systems of record
Where the truth lives, and the only place a change is real.
ERP, CRM, identity, ITSM, payments, the data platform. These systems have their own controls, their own auditors and their own idea of who may change what, and none of them were designed with an agent in mind.
Components. ERP and finance · CRM and service · Identity and access · ITSM · Payments · Data platform
What CAE adds. Preserve transaction authority where it already lives. An agent's action should be indistinguishable, to the system of record, from a properly authorised one, because it is.
| Platform | Cover | Typically |
|---|---|---|
| Anthropic Claude | Yours | Your systems, reached through MCP or your own services |
| Microsoft Azure | Partial | Dynamics 365, SAP and others hosted on Azure, your own systems |
| AWS | Yours | Your systems on AWS or elsewhere |
| OpenAI | Yours | Your systems, reached through connectors or your own services |
Scenarios
Service desk resets an MFA factor
An assistant that has summarised tickets for six months is asked to finish the job: reset factors and add people to groups. Its credential now reaches every identity you have.
Layers that carry the risk. Action and tools · Control plane · Systems of record
What the platforms give you. All four platforms give you a runtime, tool definitions and audit logs, and the cloud platforms give you an identity system for people and workloads.
What you still build. Authority scoped per action and per user class; call-back evidence required before a reset; a commit boundary that refuses an unevidenced request whatever the ticket says; an autonomy step per action type; automatic demotion after an incident.
CAE constructs. Commit boundary · Authority grants · Evidence record · Autonomy staircase
Invoice to payment, end to end
Read the invoice, match it to a purchase order, decide, and pay. Money moves, so the interesting part is what happens when the agent is wrong.
Layers that carry the risk. Orchestration and agents · Action and tools · Control plane · Systems of record
What the platforms give you. Durable workflow engines, retries and queues are mature on every cloud, and the finance system has its own approval rules.
What you still build. Value and counterparty limits expressed as policy, not prompt; a second human for bank detail changes; a compensating action rehearsed before go-live, because a payment cannot be rolled back; an evidence bundle captured with each action.
CAE constructs. Commit boundary · Policy as code · Compensation not rollback · Minimum action evidence bundle
Claims and underwriting triage
A model ranks and recommends, a person decides, and the customer is entitled to know why.
Layers that carry the risk. Reasoning core · Context and knowledge · Control plane
What the platforms give you. Retrieval, model choice, content filters and evaluation tooling are all available off the shelf.
What you still build. A named decider for every adverse outcome; decision records that keep the options, the evidence and the objections; drift and fairness monitoring on the model; an explanation a customer can read.
CAE constructs. Evidence-first decision records · Recommendation is not authority · Evaluation suites
Agents inside the developer platform
Agents that write code, open pull requests and touch pipelines. Cheap code, expensive consequences.
Layers that carry the risk. Orchestration and agents · Action and tools · Control plane
What the platforms give you. Repository and pipeline identity, environment protection rules and code scanning are standard.
What you still build. Tokens scoped to a repository and an action, not an organisation; blast-radius limits on what a pipeline agent may deploy; human review that is real rather than nominal; flow mining over agent runs to see the paths actually taken.
CAE constructs. Zero trust for agents · Agentic flow mining · Autonomy staircase
Customer-facing assistant
Anyone can type anything into it, including instructions meant for the agent rather than for you.
Layers that carry the risk. Experience and workflow · Context and knowledge · Control plane
What the platforms give you. Content filters, PII detection and moderation endpoints are available on every platform.
What you still build. Permission-aware retrieval so an answer can never exceed the asker's entitlement; data classification enforced at the retrieval layer; prompt injection treated as untrusted input rather than a model problem; a clear escalation path to a person.
CAE constructs. Context is not a prompt · Commit boundary · Escalation design
How to use this
Start with a scenario rather than a platform. The scenario tells you which layers carry the risk; the platform view then shows how much of that layer you are buying and how much you are building.
Three readings are worth having in mind:
Coverage is not control. A layer marked native means the platform sells you something that does the job. It does not mean the job is done: an identity system you have not scoped, a policy engine with no policies and an evaluation suite nobody runs are all native, and none of them is doing the job yet.
The control plane is where the work is. In my reading of the public documentation, the platforms are strongest on reasoning, tools and orchestration, and they largely leave the joins between identity, authority, policy and evidence to you. That join — one boundary, checked on every action, writing a record on every path — is the part this book is about.
Platforms move faster than layers. Product names here will age within months. The shape of the stack, and the question each layer answers, tend to change far more slowly.
The explorer is an educational view of one reference architecture, not a design for your organisation. The scenarios are illustrative, and the platform coverage is the author’s judgement as at the date shown. Application requires organisation-specific architecture, security, privacy, legal, regulatory, operational, safety, accessibility, commercial and governance review, together with appropriate approvals.
The book
Every layer here is a chapter or part of one, with the patterns and control objectives in full.
Start with the argument
The Agentic AI Playbook walks the same ground in prose, one idea at a time.