Agentic workflow automation
We build agents that 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.
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
We monitor cost, latency, error rates and override patterns in production 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
- Finance operations
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.
Human-in-the-loop AI agents: design patterns for production
The patterns we use to run AI agents safely in production: approval checkpoints, confidence thresholds, tool permissions, audit trails and fallbacks.
4 min read
Document parsing with LLMs: extracting data from invoices and forms
How to turn invoices and forms into reliable structured data with LLMs, schemas, validation and a review queue for the cases that need a person.
4 min read
LLM evals: how to test AI features before every release
A practical approach to LLM evals: build a test set from real cases, combine code checks with model grading, and block releases that regress.
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.