Agentic workflow automation
We build AI workflow automation where agents carry a process from the first email or document through to the update in your ERP, CRM or ticketing system. Each step is logged, the risky ones wait for a person, and the whole flow is tested before and after every release.
What it is and when it fits
An agentic workflow is a process where a model plans and carries out several steps on its own: reading an incoming request, looking up data in two or three systems, applying your rules, drafting an action and executing it once approved. We build these as explicit state machines, so every step, tool call and decision can be traced, retried and reviewed.
This is the right fit when a process has clear inputs, a known set of systems and outcomes that can be checked, such as invoice approvals, shipment exceptions or order changes. It is a poor fit when the rules change weekly and nobody owns them, or when the volume is so low that a well-designed form would do the job. We will tell you which case you are in during discovery.
What we build
Document-to-action agents
Agents that parse invoices, contracts or orders, match them against your records and propose the next action with the source attached.
Exception handling agents
Agents that watch for delays, mismatches or failed payments, work out the cause and escalate with a drafted resolution.
Approval queues
A review screen where your team approves, edits or rejects agent actions, with every decision fed back into the eval set.
Tool and system connectors
Typed, permission-scoped tools for your ERP, CRM, inbox and databases, so the agent can only do what you allow.
Audit trail and tracing
A full record of inputs, tool calls, model outputs and approvals for every run, searchable by case or customer.
Agentic workflow or classic workflow automation.
Classic workflow automation follows steps someone wrote down in advance: a trigger fires and the flow runs the same path every time. In an agentic workflow, a model decides the next step within the tools and rules you give it, which suits cases that arrive in different shapes. Most processes mix both.
| Question | Classic workflow automation | Agentic workflow |
|---|---|---|
| Who decides the next step? | The rules in the flow | The model, within the tools and rules you set |
| Varied input, such as emails and PDFs | Breaks or needs a person | Handled, with checks on what the model reads |
| Predictability | Same input, same result | Results can vary, so every release runs against a test set |
| Running cost per case | Very little | Several model and tool calls |
| Best for | Stable steps that follow a condition | Work that needs reading, lookups and judgement across systems |
We build the fixed steps as plain automation and give the agent only the steps that need judgement. For the difference in more detail, read what an AI agent is and how to automate business processes.
How it works
- 01
Map the process
We sit with the people who run it today, write down the steps, rules and edge cases, and agree which decisions need a human.
- 02
Build the evals first
We turn real historical cases into a test set, so we know what good looks like before the agent handles anything live.
- 03
Build and release in stages
The agent starts by drafting actions for review, then takes on more steps as the eval results and approval rates support it.
- 04
Run and improve
Once the agent is live, we watch cost, latency, error rates and overrides, and adjust prompts, tools and rules as the process changes.
Related work
- Logistics
How AI agents saved a freight forwarder €400k a year on shipment exceptions
€400ksaved a year- AI agents
- LangGraph
- Workflow automation
- Reference build
AI invoice approval agent that cites the policy clause behind every decision
100%of automatic decisions cite a policy clause- AI agents
- Human in the loop
- RAG
Built with
All technologiesA guide to start with
Further reading
What is an AI agent? Examples for businesses
An AI agent is software that carries out a task in several steps across your systems, within limits you set. What that means in practice, how it differs from a chatbot, three examples and when to build one yourself.
11 min read
Human-in-the-loop AI agents: design patterns for real operations
How we keep a human in the loop when AI agents run on live systems: approval checkpoints, confidence thresholds, tool permissions, audit trails and fallbacks.
5 min read
PDF data extraction with LLMs: invoices and forms
How to use LLMs for PDF data extraction: turning invoices and forms into reliable structured data with schemas, validation and a review queue for the cases that need a person.
5 min read
Common questions
Mainly the number of systems the agent has to work with, the quality of their APIs, how many edge cases the process has and how much of it needs a review screen. Model usage is usually a smaller line item. We agree scope and timeline after discovery.
Tools are scoped so the agent can only call what you permit, high-impact actions wait for human approval, and outputs are validated against schemas and business rules before anything is written. Every release has to pass the eval set first.
Usually, yes. If a system has an API we use it; if it only has a database, file export or email interface, we build a connector around that. We check this early because it shapes the timeline more than anything else.
You do. The code, prompts, eval datasets and infrastructure configuration live in your repositories and cloud accounts, and we document them so your team or another supplier can take over.