Diagnose first. Build second.
The Bedrock AI journal on the AI architect role: what AI architects do, how they diagnose a business before building, and where the role is heading.
Latest articles
- The Permissions, Logging and Fallback Layer AI Needs
Every production multi-agent system needs three layers built in: permission scoping, audit logging and fallback handling. Here's what each one requires.
- FinOps for AI Agents: Why Cost Becomes Architecture
FinOps for AI agents means designing cost control into the system, not auditing the bill afterwards. Here's the framework and where it lives.
- Agent-to-Agent Coordination: What to Standardise First
A2A protocol supplies the wire format. Here's what an AI architect still has to standardise before agents can safely coordinate.
- Enterprise-Managed Authorization for MCP Explained
Enterprise-Managed Authorization lets an IdP centrally control MCP server access via ID-JAG tokens. Here's how it works and what architects must build first.
- MCP's Stateless Spec: What Changes for AI Architects
The 2026-07-28 MCP spec drops sessions and handshakes. Here's what that changes about how AI architects design tool access, auth and routing.
- How to Build an AI Architect Portfolio With No Experience
Without a full-time architect job, a portfolio has to prove judgement, not just code. Here is what to build, document and leave out.
- Fractional AI Architect vs AI Consultancy: What You Get
A fractional AI architect owns the outcome and stays; an AI consultancy owns a deliverable and exits. Here is what each structure actually gets a business.
- How Agentic AI Is Reshaping the Enterprise Architect Role
Agentic AI is turning enterprise architects from gatekeepers into orchestrators, and the shift is big enough that new job titles are already emerging.
- Solutions Architect to AI Architect: When to Upskill
The signals that tell a solutions architect it's time to upskill into AI architecture, what transfers, what's new, and how long it actually takes.
- AI Architect vs Enterprise Architect: Who Owns AI?
Neither owns the AI roadmap alone. Enterprise architects govern the map; AI architects diagnose and build the system that has to work inside it.
- Why Most ML Engineers Need an AI Architect
ML engineers build models that work in isolation. An AI architect designs the system around them, which is why most technically sound models never ship.
- AI Architect vs AI Engineer: The Three-Way Distinction
AI architect, AI engineer and data scientist are three different jobs. Here is what each one actually owns, and why businesses keep hiring the wrong one first.
- How AI Architects Run Discovery Workshops With Stakeholders
How AI architects structure discovery workshops with stakeholders: who to invite, what to ask, and how to translate findings into a build decision.
- When AI Systems Fail: The Architect's Incident Playbook
What to do when AI systems fail in production: how AI architects detect, contain and diagnose failures, and what to design in before the incident happens.
- Will AI Architects Be Automated Too? An Honest Answer
AI already automates the AI architect's build tasks, but not the diagnostic judgment that prevents most AI projects from failing.
- How AI Architects Keep Company Data Out of Model Training
AI architects keep company data out of model training through vendor contract terms, routing architecture, and data classification, not a settings toggle.
- The AI Architect's First 90 Days
An AI architect's first 90 days: diagnose in month one, pilot one workflow in month two, and prove it with governance and metrics by day 90.
- Build vs Buy: How AI Architects Make the Call
AI architects decide build vs buy by scoring differentiation and data advantage, not by comparing sticker prices. Here is the framework they use.
- AI Architecture Mistakes That Cost Six Figures
The AI architecture mistakes that cost companies six figures are rarely model failures: they are missing data pipelines, no evaluation layer, and no governance at launch.
- The Org Chart of 2030: Where the AI Architect Sits
By 2030 the AI architect sits at the hub of a hub-and-spoke structure, not inside IT: setting shared context for AI-augmented teams across the business.
- Evals: How AI Architects Measure If AI Works
How AI architects use evals to measure whether AI systems actually work in production, not just in demos, using layered metrics and grading.
- From Data Engineer to AI Architect: The Skills Bridge
Why data engineers cross into AI architecture faster than any other background, and the four gaps that still stand in the way.
- Agent Memory Design: How AI Architects Build It
Agent memory design is the discipline of deciding what an AI system remembers, forgets and retrieves. Here is the AI architect's framework for doing it well.
- What an AI Architect Does in Week One
A week one AI architect audits before they build: system inventory, stakeholder mapping, and a diagnosis. No models get chosen yet.
- From Developer to AI Architect: The Skills Bridge
The five gaps a software developer must close to become an AI architect, including the one most transition guides miss entirely.
- AI Architect Interview Questions: What Good Answers Reveal
The ten questions that separate AI architects who can build from those who can only describe. What to ask, what to listen for, and the red flags to watch.
- Human-in-the-Loop Design: Where AI Architects Draw the Line
AI architects decide which actions agents can take alone and which require human approval. Here is the framework for drawing that line correctly.
- AI Governance for SMEs: The Architect's Responsibility
What AI governance actually means for small businesses, and why the AI architect (not legal) owns the technical side of making AI governable by design.
- How AI Architects Choose Models: The Four-Axis Framework
AI architects select models across four axes: capability, cost, latency, and privacy. Here is the decision framework used in production systems.
- RAG Is Dead: The Rise of Context Engineering
Context engineering has replaced RAG as the core discipline in production AI. Here is what AI architects build instead, and why the difference matters.
- AI Tools vs AI Architecture: What's the Difference?
Buying AI tools and having an AI architecture are not the same thing. Here is what separates a coherent system from a collection of disconnected subscriptions.
- AI Architecture Diagrams: Mapping the Intelligent Business
How AI architects draw diagrams that map business processes, data flows and agent logic — before a single tool is chosen or a line of code is written.
- How an AI Architect Runs an AI Audit
The AI audit is the diagnostic phase before any build. Here is how an AI architect runs one: the process, what gets examined, and the deliverables you receive.
- Fractional AI Architect: Why SMEs Rent the Role
A fractional AI architect delivers senior AI strategy and system design part-time. Here is what SMEs get, what it costs, and when to hire one.
- AI Architect Salary: Employee vs Fractional vs Consultant
UK salary bands, day rates and fractional retainer costs for AI architects in 2026 — what each engagement model pays and when each one makes sense.
- How AI Architects Design Agentic Workflows
AI architects choose between three patterns: router, planner-executor, and crew. Learn how each works and when to use it.
- The AI Architect's Toolkit: Models, Frameworks and Stores
The four-layer stack every AI architect selects: foundation models, agent frameworks, vector stores, and orchestration, and how to choose between them.
- Why Mid-Sized Businesses Will Need an AI Architect
AI tool sprawl is the defining challenge of 2026. Here is why every mid-sized business will need an AI architect before 2030.
- AI Architect Skills: The 2026 Stack
The five technical domains every AI architect must command in 2026: agent orchestration, context engineering, data pipelines, evals, and governance.
- What Is AI Architecture? Systems, Agents and Data Explained
AI architecture is the blueprint for how models, agents, data pipelines and orchestration logic fit together. Here is what that means in practice.
- How to Become an AI Architect in 2026
A practical career roadmap for becoming an AI architect — including entry paths that don't require a CS degree or years of ML experience.
- AI Architect vs Solutions Architect: Which Do You Need?
AI architects design intelligent systems; solutions architects design integration and infrastructure. Here's how to tell which role your business actually needs.
- AI Architect vs Machine Learning Engineer
An AI architect designs enterprise-wide AI systems; an ML engineer trains and optimises the models inside them. Here is when you need each role.
- What Does an AI Architect Do All Day?
A realistic breakdown of how an AI architect divides their week: from diagnosis and system design to evals, governance, and stakeholder alignment.
- What Is an AI Architect? The Role Explained
An AI architect designs how AI systems, agents and data work together inside a business. Here's what the role involves and why it's growing fast.