I work with CTOs, VPs of Engineering, and executive teams on the AI challenges that are too complex for a quick hire and too important to get wrong.
You've seen what AI agents can do. Maybe your team has built a few prototypes. But moving from demo to production - with proper tool orchestration, policy guardrails, observability, and governance - is a different problem entirely. You need someone who knows how these systems work from the inside.
I design agentic AI architectures that connect LLMs to your real-world systems safely and reliably. As a maintainer of the Model Context Protocol, I bring first-hand knowledge of the tooling layer that sits between AI models and enterprise infrastructure. I define the agent workflows, the policy boundaries, and the observability stack - then I help your team ship it.
A production-grade AI agent architecture with clear governance boundaries, integration patterns your team can extend, and measurable operational value - not a proof of concept that stalls.
You want to bring AI into your operations, but your current platform wasn't built for it. Your systems are tightly coupled, your data is locked in monolithic databases, and your APIs weren't designed for the kind of real-time, tool-orchestrated interactions that AI agents require. Before you can deploy AI effectively, you need a platform that AI can actually work with.
I define target-state architectures that make your enterprise systems AI-addressable. That means domain modelling, service boundaries, eventing patterns, API design, and data accessibility - all designed so AI agents can interact with your business processes safely and reliably. This is the same platform architecture work I've led for over two decades - for brands including Pandora, La Prairie, Birkenstock, Mizuno, Le Creuset, and Daily Mail - but with AI integration as a first-class architectural requirement, not an afterthought. I don't hand over a diagram and leave. I stay through the migration, aligning engineering, product, and leadership on sequencing and trade-offs.
A target-state architecture your teams understand and believe in, designed for AI integration from day one, with a phased migration plan and hands-on support through the cut-over.
Everyone in the organisation has opinions about AI. The board wants a strategy. Your engineering team wants to experiment. Your legal and compliance teams want guardrails. And you need to make decisions - what to build, what to buy, where to start, and how to govern it - before the pilots multiply into an ungovernable sprawl of disconnected AI experiments.
I work with your leadership team to cut through the noise and build an AI strategy grounded in your actual constraints - technical, organisational, and regulatory. That includes AI readiness assessments, governance frameworks, policy-as-code models, tool orchestration strategy, and build-versus-buy analysis. I help you decide where AI creates genuine operational value in your business, and where it doesn't - yet.
This can be a standalone advisory engagement or the discovery phase of a broader implementation programme. Either way, I'd rather give you an honest assessment that saves you six months than a polished proposal that tells you what you want to hear.
A clear-eyed AI strategy your leadership team can act on, a governance framework that satisfies compliance without paralysing delivery, and a prioritised roadmap that starts with the highest-value, lowest-risk opportunities.
You have an AI initiative that needs dedicated engineering capacity - and you can't afford to spend six months hiring, onboarding, and aligning new engineers with your architecture. AI delivery requires a specific kind of engineer: someone who understands both the AI tooling layer and your enterprise systems. That combination is hard to hire for on the open market.
I assemble dedicated senior engineering teams built around the AI architecture we define together. These aren't borrowed engineers with no context - they're experienced professionals who understand the systems they're building into, because the architecture and the team come from the same source. I handle the technical leadership, the architectural alignment, and the delivery governance.
Every engineer I place is senior. I don't do bench staffing, I don't backfill with juniors, and I don't hand you CVs and disappear. The teams I build operate as an extension of your engineering organisation - same standards, same rituals, same accountability. Most of my engineering teams have been with their clients for years - not because of contractual lock-in, but because they've become a genuine part of the delivery organisation.
A senior engineering team that's architecturally aligned from day one, integrated into your delivery rhythm, and focused on delivering production AI - without the six-month hiring cycle or the context gap between the architecture and the people building it.
I start by listening. Before I draw a single diagram, I need to understand the business context, the organisational dynamics, and the real constraints - not just the technical ones. The best architecture in the world fails if the team can't execute it or the business won't fund it.
Most of my engagements begin with a short discovery phase - typically a few focused sessions with your technical and business leadership. From there, the engagement takes one of three shapes: I embed with your team as a hands-on architect, I operate as an advisory architect, or I assemble a dedicated senior engineering team to deliver a defined programme of work alongside your existing organisation. Often it's a combination.
I work with your existing teams, not around them. The goal is always to leave your organisation stronger than I found it - with an AI capability they understand, own, and can evolve without me.
Agentic AI architecture is the design of AI systems where autonomous agents use tools to interact with enterprise infrastructure. It involves defining agent workflows, policy boundaries, tool orchestration via protocols like MCP (Model Context Protocol), observability stacks, and governance models to move AI from prototype to production safely.
The Model Context Protocol (MCP) is an open protocol that standardises how AI models connect to external tools and data sources. It provides the tooling layer between AI agents and enterprise systems, enabling safe, reliable interactions. Konstantin Konstantinov is a maintainer of the MCP TypeScript SDK.
A platform is AI-ready when its systems are loosely coupled, its data is accessible through well-defined APIs, and its business processes can be orchestrated programmatically. If your systems are tightly coupled, your data is locked in monolithic databases, or your APIs weren't designed for real-time, tool-orchestrated interactions, you likely need architectural work before AI agents can interact with your infrastructure safely and reliably.
AI governance in enterprise means defining clear boundaries for what AI agents can and cannot do: which tools they can access, what data they can read and write, when a human must approve an action, and how every decision is logged for audit. It includes policy-as-code models, tool whitelisting, human-in-the-loop checkpoints, and observability stacks that trace agent behaviour end-to-end.
Engagements begin with a short discovery phase — focused sessions with technical and business leadership. From there, the engagement takes one of three shapes: an embedded hands-on architect role, an advisory architect role, or assembling a dedicated senior engineering team to deliver a defined programme of work. Often it is a combination. The goal is always to leave the organisation stronger than before.
Most conversations start with a problem, not a service line. Tell me what you're facing and we'll figure out the right approach together.
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