AI Agent Design & Deployment
Agents that
Agents that
actually run
in production.
Most companies have seen the demos. We build the system that ends up in your ops stack — tested, monitored, and handed over running. No perpetual proof-of-concept phase.
What we actually do
34+
agents in production across Europe and North America
91%
of deployed agents still running without significant rework after 12 months
3 weeks
median time from scoping call to first live agent in your environment
Why companies come to us
What we solve — pick the one that fits.
We work on a narrow set of problems. If you're not in one of these buckets, we'll tell you early.
Situation 01
Stuck in demo limbo
Situation 02
Ready to scale ops
Situation 03
Legacy workflow bottleneck
Situation 04
Agent broke in prod
You've done the demos. Nothing made it to production.
Your team evaluated three LLM vendors. You have a Notion doc full of potential use cases. Nothing is running in your actual stack. The gap between "it works in the sandbox" and "it works on Monday morning" is exactly where we operate.
We scope what's actually deployable in your environment before writing a line of agent code
Every build includes integration testing against your real data and access patterns
Handover documentation written for ops teams, not AI researchers
Your operations team is hitting a ceiling on manual work.
The workflows exist, the volume has grown, and the team is spending more time on repetitive processing than on actual decisions. Agents are a fit here — but only if the underlying process is clean enough to automate safely.
We run a workflow audit before any agent design work starts
Automation is introduced incrementally — one decision node at a time
Fallback logic and human escalation paths are part of every specification
A core workflow runs on spreadsheets, inboxes, or manual handoffs.
Most companies have at least one critical process that was never properly systematised. Automating it directly with an AI agent is usually the wrong move. We spend the first phase mapping the process, removing the unnecessary steps, then building the agent against the cleaned version.
Process mapping before automation — always
Integration with existing tools (Slack, Notion, CRMs, ERPs) without forcing a full migration
Scope is fixed per engagement — no open-ended retainers
You have an agent running but it's started failing or drifting.
Models update. APIs change. Your data distribution shifts. An agent that worked in January can quietly degrade by April without anyone noticing until a big batch fails. We run monitoring and intervention for agents we build — and sometimes for agents we didn't.
Output monitoring with drift detection for every production deployment
Monthly evaluation runs against a held-out test set
Incident response included for agents on the maintenance plan
The short version
We don't start with the agent. We start with the process.
Most AI agent projects fail because the workflow underneath them isn't ready for automation. Before any code, we map what you're actually doing, strip out what shouldn't be automated, and define the exact decision points an agent can own reliably.
Full process walkthrough01
Workflow audit
We map the process as it actually runs — not how it's documented. We identify automation-ready decision nodes and anything that needs to stay human.
02
Agent specification
Every agent gets a written specification: inputs, outputs, tools, escalation triggers, and acceptable failure modes. Agreed in writing before build starts.
03
Build and integration testing
We build against your real environment — real data, real access patterns, real edge cases. Not a clean sandbox.
04
Deployment and handover
The agent goes live with monitoring in place. Ops documentation is written for the people who'll manage it, not the people who built it.
From the field
All field notes
What we've learned deploying agents at scale.
Deployment
What nobody tells you about shipping AI agents to production
The difference between a working prototype and a production-grade agent is mostly boring engineering decisions. Here's what they are.
Evaluation
How to evaluate an AI vendor without getting lost in the demo
A vendor's demo environment will always look better than your production environment. The questions that matter happen after the slide deck.
Automation
Before you automate: a practical guide to workflow readiness
Automation makes a bad process faster and more expensive. This is how we decide what's actually ready to hand to an agent.
Common questions
What we get asked before most projects start.
Yes, and it's often easier. Companies without existing agent infrastructure have fewer legacy assumptions to work around. What matters is whether you have a clear workflow and real data — not whether you've shipped AI before.
Workflow audit and scoping: one to two weeks. Build and integration: two to four weeks depending on complexity. First live agent is typically running within three to five weeks of the initial scoping call. We don't do open-ended engagements — every project has a defined end state.
We work with the major providers and design agents to be model-agnostic where the workflow allows it. Model selection is part of the specification phase — we choose based on cost, latency, and reliability requirements for your specific task, not on any vendor preference.
All deployments include a 30-day stabilisation period. After that, you can manage it internally with our documentation, or continue on a maintenance plan that includes drift monitoring, monthly evaluation runs, and incident response. The choice is yours.
Yes. An NDA is standard on every engagement. For projects involving personal data we provide a Data Processing Agreement that covers GDPR requirements. Both are available for review before you commit to anything.