Azure Agent Loops for Real Business Work
Use Azure Functions and Logic Apps to give AI agents a controlled job, clear tools, and a human route when they are not sure.
An agent is useful when it can do more than answer a question. It needs to read the right source, take a bounded action, record what happened, and hand off when the answer is uncertain.
The Fabrik builds those loops around the systems you already use. Azure Functions handle the custom logic. Logic Apps coordinates the steps, approvals, alerts, and retry paths. The agent gets a useful job, not the keys to the building.
What This Fixes
Useful Until It Hits Reality
A chat demo works, but the moment it needs data, permissions, a decision, or an exception, nobody owns the next step.
No Safe Escalation Path
The agent can draft an answer, but there is no queue, approval, or clear route for the cases it should not decide alone.
Automation You Cannot Explain
A process runs in the background, then fails quietly or makes a decision nobody can trace back to a source.
What We Build
- A narrowly defined agent task, source data, and decision boundary.
- Azure Functions for custom orchestration, validation, or API work.
- Logic Apps workflows for triggers, approvals, retries, alerts, and hand-offs.
- Human review queues for low-confidence or high-impact cases.
- Logging, monitoring, cost guardrails, and a support runbook.
What Changes
- A repeatable loop around a real operational job, not a standalone chat demo.
- Clear ownership when the agent cannot proceed or a downstream service fails.
- A record of inputs, actions, approvals, and exceptions for troubleshooting.
- A foundation that can expand only after the first workflow has earned trust.
Trusted with complex, time-sensitive work
Darren was fantastic. He was so easy to work with, always finding a path through tricky issues. He consistently made technical matters feel accessible. Darren delivered a complex and multi-layered project to a very tight time frame. He has supported really important developments for our organisation, and we are very grateful for his work, expertise and great approach.
EHRC staff
How It Works
Choose One Job
We start with one high-volume, bounded task and define what the agent may do, what it may suggest, and what it must escalate.
Build the Loop
We connect the sources, functions, workflows, approvals, logging, and alerts around that task.
Test the Edges
We test bad input, missing data, uncertain answers, retries, and hand-offs before anyone relies on it.
Useful Answers Before We Talk
Is this just a chatbot?
No. A chat interface may be part of it, but the service is about the controlled workflow behind it: data access, actions, approvals, exception handling, and monitoring.
Can an agent make decisions without a person?
For low-risk, well-defined actions, sometimes. For financial, HR, legal, security, or customer-impacting decisions, we normally design a human review step and make the boundary explicit.
Do we need to move everything into Azure first?
Usually not. We assess the data, identity, APIs, and governance you already have, then choose the smallest reliable Azure and Microsoft 365 footprint for the first loop.
How do you stop it becoming expensive or unreliable?
We set scope, usage and retry limits; log every action; monitor downstream failures; and keep a human route for exceptions. If a use case cannot be made safe and supportable, we will say so.
Want to See What This Could Fix?
Bring one operational job that keeps bouncing between people and systems. We will tell you whether an agent loop is a sensible first move, or where plain automation is the better answer.
Book Your Free Discovery Call