Service
AI workflow automation
Most automation breaks at the first exception. A rule-based flow handles the invoice that looks like every other invoice, then stops dead on the one that does not. Agentic workflow automation is different in one specific way: the system reasons about the input instead of pattern-matching it, so the awkward cases stay inside the automation rather than landing back in somebody inbox.
You might recognise
- A person copies data between two systems that will never be integrated.
- Your existing automation covers the easy 70% and a human mops up the rest.
- The process is documented, followed inconsistently, and nobody can say why.
How it runs
Four steps, in this order.
- 01
Trace the process
We follow one real workflow end to end, including the exceptions, and write down where the judgement calls actually happen. This is the step that decides whether an agent is the right tool at all.
- 02
Define the boundary
What the agent may do on its own, what needs a human to approve, and what it must refuse. Written as explicit rules before any code exists, because an agent with unclear authority is the thing that makes automation unsafe.
- 03
Build and instrument
The agent, the tool integrations it needs, and the logging that lets you read back every decision it made and why. Observability is part of the build, not a later addition.
- 04
Verify against real work
Run it in shadow mode against live inputs while a human still does the job, and compare. Only once the outputs agree does it take over, starting with the narrowest slice.
What you get
Deliverables
- The deployed agent, running on your infrastructure or ours
- A written decision boundary: what it does alone, what it escalates
- Structured logs for every run, so any output can be traced back to its inputs
- A shadow-mode comparison against real work, before anything is switched on
- Handover documentation and the source, owned by you
FAQ
About workflow automation
How is this different from an n8n or Zapier workflow?
Those tools execute a fixed graph you define. That is the right choice when the steps never vary. An agentic system decides what to do next based on the input it is looking at, which is what you need when the inputs are messy and the exceptions are the expensive part. Plenty of processes genuinely do not need an agent, and we will say so during the trace step rather than sell you one.
What happens when the agent gets something wrong?
It is designed to get things wrong safely. Every action runs inside a defined boundary, anything outside it escalates to a person, and every run is logged well enough to reconstruct the reasoning afterwards. Shadow mode exists so you find the failure modes before the agent has authority over anything.
Do you need access to our production systems?
Eventually the agent needs to act somewhere, but not on day one. The trace and boundary steps need conversations and read-only access at most. Write access is scoped to the specific actions in the agreed boundary, nothing wider.
Which models and platforms do you build on?
The model is an implementation detail chosen per task and swapped as better options ship. The integration layer is built so a model change is a configuration change, not a rewrite. Deployment is typically on Cloudflare Workers or your existing cloud.
Related reading
- Agentic AI vs AI agents: what the difference actually meansThe difference between agentic AI and AI agents, why it matters when you are buying, and the questions that reveal which one you are being sold.
- What is an MCP server?A plain-English explanation of MCP servers: what they do, the three things they expose, how they connect to AI apps, and when you actually need one.
Tell us what the work looks like
Describe the process you want off your team plate. You will get a straight answer on whether agents are the right tool for it, including when they are not.