AI Transformation
Agentic AI, engineered like architecture — not deployed like a chatbot.
Discovery workshops with senior stakeholders, mapping and simplifying business processes, selecting technology, building proofs of concept, and defining the target agentic architecture for the organisation — sequenced deliberately, with governance and guardrails built in from the start.
What we deliver
Four capabilities, one transformation
AI transformation advisory
Executive discovery workshops, AI use-case identification and prioritisation, business-case shaping, roadmap definition, guardrails and governance.
AI & GenAI solution architecture
LLM solution design, AI agents, MCP servers, RAG over corporate data, proof-of-concept delivery, technology and model selection.
Process assessment & redesign
Mapping and simplifying complex business processes, identifying where GenAI and agentic automation create value, redesigning operating models around them.
Stakeholder & delivery leadership
Facilitation from CIO/VP level to delivery teams, vendor management, change governance, multi-team programme delivery.
The stack
A layered agentic architecture, not a model API call
Each layer builds on the one above it: the foundation sets what the model can see and touch, the orchestration pattern sets how steps run, guardrails and isolation keep it safe, and the harness is the concrete runtime executing all of it.
| Layer | What it is | Why it matters |
|---|---|---|
| Foundation | The base model plus tools, MCP-served integrations, retrieval, and persistent memory. | Add only what the specific use case needs — every capability costs shared context budget and adds a failure surface. |
| Extensibility | In-process tools vs. out-of-process MCP servers vs. agent-to-agent delegation. | MCP is the right boundary wherever an agent crosses a system-ownership line — permissions and audit sit at the protocol boundary. |
| Orchestration pattern | From deterministic chaining/routing to evaluator-optimizer to open-ended autonomous agents. | Choose the least autonomous pattern that solves the use case — predictability drops sharply past evaluator-optimizer. |
| Context isolation | Subagents get a clean-slate context for review or critique steps; skills load only when relevant. | Without it, a "reviewer" step inherits the drafter's blind spots and rubber-stamps its own mistakes. |
| Guardrails & structured output | Schema-enforced outputs, allow / ask / command-only permissions, human-in-the-loop gates. | Every consequential action needs a named gate and a schema its output must satisfy. |
| Enterprise RAG & sovereignty | Retrieval as a governed, identity-filtered tool call; private network path; regional routing; contractual ZDR. | What makes the platform usable at all for client-confidential or IP-sensitive data. |
| Observability & evaluation | Full trajectory tracing plus automated evals, run the same way unit tests gate a code change. | Without this, "why did the agent do that" has no answer and error rates can't be tracked over time. |
| Harness | The runtime that executes it: declarative/markdown runtime, code-first framework, or managed platform. | The build-vs-buy decision — the right harness differs for a drafting assistant vs. a shop-floor monitoring agent. |
This stack is covered at engineering depth across our blog series — including two real, open-source systems we dissect line by line: TradingAgents (a provider-agnostic multi-agent trading pipeline, MIT-licensed) and AI Job Search (a fork-and-own, Claude-native framework, Apache-2.0 licensed).
Version control is a cross-cutting practice across every layer above, not a separate concern: agent definitions, skills, and prompts are treated as code, reviewed the same way a pull request is reviewed. Visual, no-code canvases store their logic as an opaque JSON blob that git can't meaningfully diff or blame; a declarative, markdown- or code-first harness produces a clean diff on every change. That difference alone is often the deciding factor in a harness or vendor choice once a system has more than one contributor.
Operating model
Someone has to own the agent, not just the model
A Target Operating Model names an accountable business owner for each agentic process, stands up a cross-functional review forum, and gives escalation paths a place to go — before the first pilot ships, not after an incident forces the question.
Governance structure
An accountable business owner per process, a standing cross-functional review forum (business, risk, engineering), a named platform/enablement function, and a defined escalation path when an agent's behaviour needs a human decision fast.
AI enablement, three tiers
General workforce literacy for everyone who reviews agent output; a power-user / citizen-builder track for people composing their own workflows; and a platform/engineering track that owns the harness, guardrails, and evals infrastructure everyone else builds on.
Human oversight
Autonomy is assigned per action, not per agent
An agent's autonomous ("allow") permission should never cover an action that is irreversible, financially consequential, or safety-adjacent — no matter how well it has performed so far.
Approval gate
Approves or rejects a proposed action before it executes.
Sending an email, executing a trade, deploying code.
Edit gate
Edits the agent's proposed content or parameters before resume.
Reviewing a drafted document before it's sent.
Review gate
Inspects output after the fact; can trigger a re-run.
Post-hoc QA on a batch of agent-generated reports.
Security
A genuinely new attack surface
Agentic systems attack the agent's judgment, not just its code. None of the countermeasures below are bolted on after the fact — each is a sharper application of the architecture already built above.
Agent goal hijack (indirect prompt injection)
Retrieved content is treated as untrusted data, never instructions; consequential actions stay behind an approval gate regardless of what the agent "decided."
Tool misuse & exploitation
Least-privilege permission modes — allow / ask / command-only — assigned per action, not per agent.
Identity & privilege abuse
Per-agent, per-MCP-server scoped permissions declared in configuration, not ambient application credentials.
Agentic supply-chain vulnerabilities
Every MCP server and skill is vetted and pinned the way a software dependency is vetted, with a named owner accountable for it.
Insecure inter-agent communication
A2A and other agent-to-agent channels are authenticated and scoped like any other service boundary — a Remote Agent is never trusted just because a Host Agent chose to delegate to it.
Cascading failures
One agent's bad output shouldn't become the next agent's unquestioned input — evals and schema checks sit between pipeline stages, not just at the very end.
Human-agent trust exploitation
Approval-gate prompts stay specific and inspectable, so "just approve it" fatigue never becomes the default response to a gate the reviewer stopped actually reading.
Rogue agents & runaway autonomy
A kill switch and a hard ceiling on autonomous ("allow") actions exist independently of the agent's own judgment — the system can be stopped even if the agent itself never asks to be.
EU AI Act & GDPR
Compliance is a documentation problem you already solved
The observability layer is the record-keeping Article 12 requires; the human-in-the-loop gates are the human-oversight design Article 14 requires; a LogicStream Agentic Process Artefact — one version-controlled diagram with the agent's permission mode labeled on every task — satisfies the "well-documented process" prerequisite and most of an auditor's data request in one artifact. What's usually missing is the paper trail connecting the two — not the controls themselves.
Roadmap
A phased path, not a big-bang rollout
Foundation audit
Technical-debt and data-access inventory against 2–3 candidate use cases; document target SOPs; stand up the AI governance group.
First pilot
One low-risk pilot per business unit, "ask"/"edit" gates on every consequential action, no autonomous "allow" actions yet.
Build the durable layer
MCP integration layer, governed enterprise RAG, observability and evals pipeline; move the highest-stakes pilot to a code-first framework.
Expand and harden
Extend autonomy only where evals and traceability are proven; formalize data sovereignty posture; consider a managed platform for scale.
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The complete framework above, plus the cost-efficiency evidence and error-elimination case for the business case you need to make internally.
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