Part 1 of 7 in the Logic Apps Agent Loop series
The Azure Logic Apps agent loop introduces a fundamentally different way to design workflows on the platform. While conventional Logic Apps workflows follow a fixed sequence of steps defined at design time, the agent loop delegates reasoning to a large language model at runtime, looping through think, act, and observe cycles until a task is complete. This post opens a seven-part series on building agentic workflows in Logic Apps. It starts with the question that matters most: why does this change anything?
For years, Azure Logic Apps has been the platform of choice for integration architects who need to orchestrate business processes across cloud services and on-premises systems. You build a workflow, wire up triggers and actions, define your conditions, handle your errors, deploy, and move on. The flow is predictable (deterministic): given the same inputs, it does the same thing every time. That predictability is the point.
The agent loop breaks that contract, deliberately and usefully.
With the introduction of agentic workflows in Azure Logic Apps, Microsoft has extended the platform from a fixed automation engine into something that can reason, adapt, and decide. At its core, the agent loop drives this shift. It is a repeating process: the connected language model thinks through a problem, selects a tool, acts on the result, and decides whether the task is done.Unlike a conventional workflow, there is no hardcoded sequence of steps. Instead, the model determines the path based on the task.
This post is the opening of a seven-part series on building agentic workflows in Azure Logic Apps. Before going hands-on with triggers, connectors, and multi-agent patterns in later posts, this one makes the case for why the agent loop matters and what it fundamentally changes about how you think about workflow design.
How the Azure Logic Apps agent loop differs from nonagentic workflows
Nonagentic Logic Apps workflows are excellent at exactly the kind of work they were designed for: stable, predictable, repeatable processes. An approval workflow, an ETL pipeline, and a B2B message exchange are all scenarios where the path through the workflow is known in advance. The trigger fires, the conditions evaluate, the actions execute in sequence, and the run history tells you exactly what happened and why.
The challenge arises when the environment you are integrating with is unstable or unpredictable. When incoming data is unstructured. Or when the right action depends on context that cannot be captured in a condition expression. Or when you need to handle a customer query that could go a dozen different directions depending on what the customer actually says.
These are the cases where deterministic workflows buckle. You end up building sprawling switch-case structures, hardcoding edge cases as branches, and constantly patching the workflow every time a new variation appears. The workflow becomes a maintenance problem rather than a solution.
Agentic workflows excel in dynamic environments where unexpected events occur, the choice of the right tool relies on the input, and the system must manage unstructured data without specific instructions for each variant.
The agent loop: Think, Act, Learn
How the agent loop works: Think, Act, Learn
The Azure Logic Apps agent loop follows a three-step process.
Think. The agent collects available information: task instructions, prior inputs, and previous tool results. It then passes all of this to the connected language model.The model reasons over the context and decides what to do next: invoke a tool, ask a follow-up question, or return a final answer.
Act. In Logic Apps, tools are actions drawn from 400+ connectors. These include Azure OpenAI, Azure AI Search, Office 365, and custom APIs. Once the action runs, the result feeds back into the next cycle.
Optionally, the loop adapts. The agent can use feedback or external signals to adjust its behaviour over time, though this is the most advanced capability and not required for most workflows.
Iterations, not instructions
This loop continues think, act, observe, decide until the model determines the task is complete. You can change the number of iterations as needed. A simple query might resolve in one loop. A complex multi-step task might require five or ten.
The diagram below shows the difference between a conventional non-agentic workflow, which follows a linear sequence of predetermined steps, and the agent loop, which dynamically iterates until the model determines that the task is complete.
Agent versus nonagentic: a structural comparison
The difference is not just philosophical. It shows up in how you design, deploy, and maintain the workflow.
In a nonagentic workflow, the logic architect owns the decision tree. Every branch, every condition, every action path is explicitly modelled. This is powerful for known, bounded scenarios, but it places all the reasoning burden on the architect at design time.
In an agentic workflow, the reasoning is delegated to the model at runtime. The architect’s job shifts: instead of modelling every path, you define the agent’s instructions, give it the right tools, and trust the model to navigate the task. This is a different skill and a different mindset closer to prompt engineering and system design than to traditional workflow modelling.
The Microsoft documentation puts it plainly: agentic workflows can adapt to environments where unexpected events happen, choose which tools to use based on prompts and available data, and handle unstructured data at a level of flexibility that nonagentic workflows simply cannot match. Moreover, nonagentic workflows function best in stable environments with static, predictable, repetitive tasks.
Neither is universally better. They address different problems. But for integration architects, the arrival of the agent loop means Logic Apps can now cover territory that previously required a custom-coded application or a fully separate agent framework.
Standard versus Consumption: what you need to know now
Azure Logic Apps offers two hosting models: Standard (single-tenant, runs on Azure Functions runtime) and Consumption (multitenant, pay-per-execution). Agentic workflows are fully available in Standard. Consumption support is in public preview as of early 2026 and carries some restrictions.
For production agentic workloads, Standard is the right choice today. The rest of this series will use Standard throughout, with notes where the Consumption behaviour differs.
What this series covers
The seven posts in this series move from concept to production:
- Why the agent loop changes everything — this post
- Anatomy of an agent loop — instructions, the connected model, tool calls, and how the loop iterates
- Autonomous versus conversational workflows — choosing between unattended execution and human-in-the-loop patterns
- Building tools for the agent — connectors, custom connectors, and MCP servers as tool providers
- Multi-agent patterns — handoffs, orchestrators, and sequential agent loops
- Securing agentic workflows — authentication, the expanded caller surface, and Easy Auth
- Observability, pricing, and running in production — Application Insights, agent loop pricing, and DevOps deployment
The next post gets hands-on: we will look at the anatomy of a single agent loop in the Logic Apps designer, walk through the instructions pane, wire up Azure OpenAI as the model, and watch the run history to see how the iterations unfold.