AI Agents
Built right.
Built to operate.
Production Architecture
Memory Systems
MCP Integrations
Human-in-the-Loop
There is a significant gap between AI agents that demo well and AI agents that reliably deliver in production. We build production-grade agents with the architecture, memory systems, tool integrations, human-in-the-loop controls, and evaluation frameworks that production actually demands.
AI Agents
Production-ready AI agents with governance, monitoring, and human oversight controls.
Human-in-loop
The Reality
Demo-ready and production-ready are not the same thing.
Most AI agents are built to impress in a demo. They work with clean inputs, predictable tasks, and a forgiving environment. Production is different. Production means real users, edge cases, ambiguous instructions, tool failures, memory drift, and high stakes for getting it wrong.
The organisations that have discovered this difference after deploying are now dealing with agents that produce inconsistent outputs, that do not handle errors gracefully, that cannot be meaningfully monitored, and that provide no clear way to intervene when something goes wrong.
We build agents from the start with the architecture, controls, and evaluation infrastructure that production requires, so that what goes live is what was promised in the brief, not a polished prototype.
“Built right. Built to operate.”
The distinction between an impressive demo and a reliable production agent.
Demo agents have
Production agents have
What We Build
Six engineering capabilities. All production-standard.
Every production-grade agent engagement includes the full stack of capabilities that reliable deployment demands. These are not optional add-ons, they are the baseline for an agent that performs consistently in the real world.
01
Agent Harness
The foundational execution environment. We build the harness that controls how the agent reasons, what tools it can use, how it handles errors, and how it is monitored. Every other capability depends on this layer being built correctly from the start.
02
Memory Architectures
We design robust, multi-layered memory systems that enable AI agents to maintain coherent context over extended periods. Our approach combines short-term working memory, vector-based semantic recall, and structured long-term knowledge graphs to prevent context loss and memory drift per user.
03
MCP Integrations
Model Context Protocol integrations that connect the agent to the tools, data sources, and systems it needs to operate. MCP is the emerging standard for agent-to-system communication, we build integrations that are reliable, maintainable, and appropriately scoped.
04
Custom Skills
Modular, purpose-built capabilities that extend what the agent can do within its domain. Skills are designed to be composable, testable, and independently maintainable, so that the agent can be extended without refactoring the core architecture.
05
Multi-Agent Orchestration
For tasks that require multiple agents working in coordination, planning, specialisation, validation, we design and build orchestration layers that manage agent-to-agent communication, task delegation, and output synthesis reliably and at scale.
06
Human-in-the-Loop Controls
Structured intervention points that ensure human judgement is applied at the right moments, approvals, escalations, overrides. Not as a workaround for an unreliable agent, but as a designed, intentional layer of governance that makes the agent trustworthy in high-stakes contexts.
Our Standard
What production-grade actually means.
“Production-grade” is not a buzzword in our vocabulary. It is a specific set of engineering and architectural requirements that determine whether an agent will perform reliably after the demo is done and real users are depending on it.
Every agent we build is evaluated against these criteria before it goes live. If it does not meet them, it does not ship, because the cost of a production failure is always higher than the cost of building it right.
Graceful error handling – fails safely, recovers predictably, never silently.
Measurable performance – evaluation framework baked in from day one, not bolted on after deployment.
Maintainable – architecture that another engineer can understand, extend, and improve without starting over.
What You Gain
Agents that operate, not just impress.
The outcome is not a prototype. It is a production-grade agent that your organisation can depend on, monitor, govern, and extend as requirements evolve.
Reliability
Consistent performance on real, messy tasks, not just controlled demo conditions.
Continuity
Memory architectures that give the agent context across sessions, persistent, appropriate, and maintainable.
Control
Human-in-the-loop controls and intervention points that make the agent governable in high-stakes contexts.
Integration
MCP integrations and custom skills that connect the agent to the tools and systems it needs, reliably, not experimentally.
Foundation
Architecture that scales, composable, maintainable, and extensible as requirements evolve and new agents are added.
Govern What You Build
Our governance services complete the picture.
Production-grade AI agents work best within a governed AI environment. Our governance services ensure the data, compliance, and accountability layers are in place.
Ready to Build
Let's build an agent that actually works in production.
We begin with a consultation to understand the task, the context, the integration environment, and the performance criteria. From there, we scope an engagement that produces an agent built to operate, not to impress once.