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
- Agent SSO Explained: AI Agents as First-Class Identities
Okta's Agent SSO puts AI agents in the Universal Directory instead of treating them as service accounts. What changes, and what an architect checks first.
- The 91/10 AI Agent Governance Gap, Explained
91% of organisations run AI agents; only 10% have a non-human identity roadmap. What the gap means for scoping your first agent identity project.
- Orphaned AI Agent Credentials: The Offboarding Gap
What happens to an AI agent's credentials when its creator leaves, and why standard offboarding checklists miss non-human identities entirely.
- Identity Dark Matter: Why Application Discovery Wins
Why discovering non-human identities directly from applications, not IAM configuration exports, is the step that makes every later governance number honest.
- Agent Fabric vs Identity Fabric: What's the Difference?
An agent fabric binds each AI agent's scope, purpose, owner, TTL and revocation state: a narrower control plane layered on a general identity fabric.
- Identity Fabric for AI Agents: The Four-Step Approach
An identity fabric unifies AI agent identity visibility through discover, prioritise, measure and select. Here's how it differs from a governance register.
- AI Agent Credential Sprawl: How to Retrofit Identity
How architects consolidate inconsistent, team-built agent credentials into one identity layer without breaking the workflows already running on them.
- Why AI Architects Need Behavioural Science Skills
Gartner says 75% of EA activity will need behavioural science to manage AI bias and governance. Here's what that actually requires an architect to learn.
- Hub-and-Spoke AI Operating Model: Why Roles Blur
The hub-and-spoke AI operating model fails less from the wrong split and more from an undocumented one. Here is where overreach and under-ownership creep in.
- Citizen AI Development: The Guardrails IT Must Set First
What guardrails should a central IT function set before devolving AI build authority? A risk-tiered framework for citizen AI development governance.
- AI Champion Network: The Translator Role Explained
What an AI champion network is, what champions actually do between business units and the platform team, and what breaks when the role sits empty.
- Shared vs Federated AI Capabilities Explained
IAM, guardrails and MLOps pipelines stay centrally owned; domain teams federate implementation on top. Where AI architects draw that line, and why.
- AI GRC Platform Buyer's Guide: What to Check First
Most AI GRC buyer's guides list features. This one shows the single test that reveals whether a platform enforces governance or just tags rows the same colour.
- AI Model Inventory: The Foundation of AI Governance
An AI model inventory is the complete, current catalogue of every model a business runs. Without it, risk tiers, owners and coverage metrics are guesses.
- Control Catalogue vs Risk Register vs Compliance Matrix
Why AI governance needs a control catalogue, a risk register and a compliance matrix as three linked documents, not one combined spreadsheet.
- Capability-Linked AI Governance Registers Explained
A capability-linked AI governance register connects every AI system to the business capability it supports, its impact and risk tier, and a lifecycle stage.
- Business Capability Mapping for AI: A Scoring Framework
Business capability mapping for AI diagnosis scores every workflow on volume, structure and cost of delay, not which department shouted loudest for a pilot.
- Strategic Enterprise Architects: What AI Architects Absorb
2026 trend lists keep naming enterprise architects 'strategic AI advisors.' Here is what that shift actually hands the AI architect role, and what it does not.
- AI Agent Sprawl: How to Regain Control at 12+ Agents
A practical retrofit playbook for regaining control of AI agent sprawl: audit, risk-tier, default-deny, decommission, and repeat on a fixed cadence.
- LangGraph vs CrewAI vs LlamaIndex: How to Choose
It isn't really a three-way choice. Here's how AI architects split the retrieval decision from the orchestration decision before picking a framework.
- Digital Twins: How AI Architects Simulate Before Build
How AI architects use a digital twin of the organisation to model AI initiatives and test trade-offs before committing budget to a build.
- Architecture Decision Records for AI Systems: A Template
A practical ADR template for AI systems, with the extra fields standard software ADRs miss: drift, non-determinism and re-evaluation triggers.
- AI Architect Total Compensation: Salary, Bonus, Equity
What UK AI architects should expect beyond base salary in 2026, how bonus and equity are structured, and the levers worth negotiating before you sign.
- Fractional AI Architect vs Full-Time Hire: Board Case
The fully loaded cost, UK day rates and breakeven maths for choosing a fractional AI architect over a full-time hire, built for a board paper.
- AI Architect IR35 Guide: Structuring Day-Rate Contracts
How AI architects structure day-rate contracts to sit genuinely outside IR35 in 2026: the clauses that matter, the 2026 threshold changes, and CEST's limits.
- Why Internal AI Builds Fail: The MIT NANDA Findings
MIT's NANDA report found internally built AI tools reach production at roughly a third the rate of vendor-built ones. Here's what the data shows and why.
- The Boost Pattern: Extending Your Vendor AI Platform
How AI architects extend a vendor AI platform with custom retrieval and evaluation instead of building from scratch, and where the pattern stops working.
- The AI Vendor Proof of Concept: A Three-Week Framework
How AI architects validate a vendor's AI use case in three weeks before committing budget to a full build, and what each week must prove.
- AI Architect Job Postings Are Up 196%: What It Means
AI/ML architect vacancies grew 196.5% year on year, the fastest of any AI role. What that growth actually signals for hiring managers, explained.
- The Agentic Governance Champion Role, Explained
What Forrester's agentic governance champion role involves, how it differs from the other three emerging EA specialisations, and when a business needs one.
- The Consulting Staffing Pyramid Problem, Explained
Why the senior partner in the pitch meeting rarely does the diagnostic work, and what AI is doing to the pyramid model that made this normal.
- AI Consultancy to Fractional Architect: When to Hand Off
The handoff from AI consultancy to fractional architect should happen when the question shifts from what to do to who owns it. Here is how to sequence it.
- Fractional AI CTO vs AI Architect: What's the Difference?
A fractional AI CTO owns the whole AI function and is judged on outcomes; a fractional AI architect owns the system design. Here is where the titles split.
- Sovereign AI: When Architects Recommend Self-Hosting
When does sovereign, self-hosted AI beat a cloud API? Here's the regulatory, IP and token-volume test architects use before recommending off-cloud.
- Non-Human Identity for AI Agents: The Architect's Guide
AI agents need their own identity model, not a borrowed service account. Here's how architects design non-human identity and access management.
- MCP Server Cards: What Agent Auto-Discovery Changes
MCP Server Cards let agents discover a server's capabilities from a well-known URL, no handshake required. Here's what that changes for architects.
- Shared Context in Multi-Agent AI: What to Build
MCP and A2A move messages between agents. Neither one gives agents a shared, governed memory. Here's what an architect has to build instead.
- Gartner's 40% AI Agent Forecast: What Architects Scope Now
Gartner expects 40% of enterprise apps to embed AI agents by 2026. Here's what that timeline changes about how architects scope projects.
- AI-First Enterprise Architecture: What Actually Changes
AI-first architecture designs data, control logic and cost into the system from day one, instead of adding a model to a system never built for it.
- 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.