Demo-grade, not production-grade
It dazzles on a scripted path, then breaks on messy data, edge cases, and the long tail of real requests. Impressive isn’t the same as dependable.
Expertise / AI Agents
AI agents that do real work. Not demos.
In 2026 most enterprise agents stall in the pilot — convincing in a demo, unreliable the moment they touch real data and take real actions. We design, build, and operate agents that work inside your systems, follow your policies, and earn the trust to operate.
Built by senior teams from the University of Edinburgh, Amazon, Stanford & the Alan Turing Institute
The gap
Why most AI agents never leave the pilot.
Across the enterprise, agents are everywhere in demos and almost nowhere in production. Around 88% of agent pilots never ship, and roughly 70% of enterprise leaders name non-deterministic outputs as the number-one barrier. The demo is the easy part.
Disconnected from your systems
A clever model with no safe, governed way to read your data or take action in your tools isn’t an agent — it’s a chat window. Integration is where most efforts stop.
No evals, no guardrails
Without a way to measure quality and enforce limits, no one can trust an agent to act unattended — so it never gets the keys, and it never ships.
Outputs you can’t predict, can’t measure
Non-deterministic answers across a thousand runs are the blocker most teams underestimate. Without an evaluation harness you can’t catch a wrong answer before it ships — and regression tests won’t either.
How we build agents
A constrained loop — not autonomy without guardrails.
A production agent is a control loop: it perceives context, reasons about a step, acts through governed tools, and is checked before and after every move. We engineer each part for reliability rather than betting on a single prompt.
- Tool & data access via the Model Context Protocol — now the enterprise standard for agent integration, under the Linux Foundation — and direct APIs where it makes sense. Least-privilege, fully audited.
- An evaluation harness built from your real workflows and failure modes.
- Evals become live guardrails: scoped permissions, action limits, human approval for high-stakes steps.
- Full traces, monitoring, and cost controls — operated like any production system.
Perceive → Reason → Act through governed tools → Observe — with a human on the high-stakes calls
What we build
Agent patterns we ship.
Three production agent patterns — each engineered for a different shape of work.
01
Task & workflow agents
Single-purpose agents that complete a defined job end to end — triage, research, reconciliation, drafting — inside the tools your team already uses.
02
Multi-agent systems
Specialized agents that coordinate — a planner delegating to retrieval, tool, and review agents — for work that’s too complex for one model in one pass.
03
Copilots & assistants
In-context copilots that draft, summarize, and act with a human in the loop, embedded where work already happens.
The production layer
What we engineer around the agent.
The agent is one piece. These are the layers that make it trustworthy in production.
01
Tool & MCP integration
Safe, governed access to your systems of record via APIs and Model Context Protocol — the difference between a clever model and an agent that gets things done.
02
Evaluation & guardrails
An eval harness that doubles as production guardrails: scoped permissions, action limits, grounding, and approval gates for high-stakes steps.
03
Observability & operations
Full traces of every decision, with monitoring, cost controls, and the runbooks to operate agents like any other production service.
Where they earn their keep
Agents that pay for themselves.
The strongest 2026 use cases share a shape: high-volume, multi-step work across several systems, where a human reviews the exceptions rather than every case.
✓
Customer operations
Resolve and act on customer requests across systems, escalating to a person only when it matters.
✓
Research & due diligence
Gather, cross-check, and synthesize internal and external sources into decision-ready briefs.
✓
Back-office automation
Reconciliation, claims, onboarding, and exceptions — agents that read documents and update systems.
✓
Engineering & data copilots
Agents that write, review, and ship code or pipelines under human review and CI guardrails.
✓
Knowledge & internal support
Grounded in your governed knowledge, agents that answer and act for employees.
✓
Compliance & regulatory monitoring
Scan filings, communications, and transactions against policy — flagging exceptions before they become incidents, with a full evidence trail.
FAQ
AI agents, answered.
How autonomous should an agent actually be?
Rarely fully. The reliable 2026 pattern is a constrained loop: the agent reasons and acts within explicit boundaries, with deterministic steps where they belong and human approval for high-stakes actions. Autonomy is dialed up only as evidence earns it.
Do we own the agent you build with us?
Yes — the code, the prompts, the evaluation harness, and the integrations are all yours. We design every engagement so your team can operate, extend, and improve the agent independently. Enablement is built into the project, not sold as an upsell.
What is MCP, and do we need it?
The Model Context Protocol is the open standard for connecting agents to tools, APIs, and data — now governed by the Linux Foundation’s Agentic AI Foundation and supported by every major model platform. By early 2026 most enterprise agents in production use it. We build on MCP (alongside direct APIs where it makes sense) so agents get safe, governed access to your systems without bespoke glue for every integration.
How do you keep an agent reliable over time?
An evaluation harness built from your real workflows and failure modes, run continuously. Those evals become live guardrails — scoped permissions, grounding, action limits, and escalation — so quality is enforced as the model, data, and usage change, not hoped for.
Can agents act on our real systems safely?
Yes — with role-based access, least-privilege tool scopes, approval gates for sensitive actions, and full audit trails. Agents get exactly the access they need and nothing more, and every action is logged.
Should we build agents or buy an agent platform?
Usually both. Buy the orchestration and tooling where it’s commoditized; build the agents, integrations, evals, and guardrails that encode your business and your risk posture. We help you draw that line — and you own the result.
Where to start
Where to start.
Bring us your agent project — we’ll find the path to production.
In design, in pilot, or stuck: we audit it against the four production criteria most teams skip — integration with systems of record, an evaluation harness, governance and controls, and operational ownership. You leave with a concrete path to production: scope, sequence, and the engineering decisions that need to be made.
What you get: a production-readiness assessment scored against twelve criteria; a target architecture for the agent in your environment; a staged delivery plan with timelines and effort estimates; and one workshop with your engineering and operations leads. Led by a senior consultant — fixed scope, fixed fee.
Book an Agent Readiness Review →Start the conversation
Ready to ship an agent your team can actually run?
A 30-minute conversation with a senior consultant. Bring an agent project you’re stuck on, or a workflow you think an agent could own. We’ll tell you whether it’s ready to ship, where the gaps are, and what an Agent Readiness Review would surface.
Book an Agent Readiness Review →