How to Build an AI Agent Loop for Repeatable Tasks (+ Step-by-Step Guide for Australian SMEs)
An AI agent loop is built by defining a task, giving large language models (LLMs) access to approved tools, capturing its state, executing tool calls, checking the results, and repeating the cycle until a clear success condition is met. Start with one bounded workflow, add limits and human approval, then automate and monitor each run.
Recent data from National Australia Bank (NAB) Economics reveals that 42% of Australian small and medium-sized enterprises (SMEs) currently use AI tools to streamline day-to-day operations, while an additional 14% plan to adopt the technology soon.
Key Takeaways
AI agent loops automate repetitive SMB workflows by combining reasoning, memory, and tool calls.
Custom loops offer model flexibility and precise cost controls compared to managed platform APIs.
Start with a bounded single-goal workflow before adding automated triggers or complex state.
Implement human-in-the-loop safeguards for high-risk actions like pricing updates or outreach.
What is an AI Agent Loop?
An AI agent loop is the engine that makes an AI do things instead of just saying things. At its simplest, it's a loop that:
Perceives input (e.g., new lead, support ticket, content brief).
Reasons about what to do next using an LLM.
Acts by calling tools (APIs, databases, email, CRM).
Observes the result and decides whether to continue or stop.
This loop repeats until the task is complete or a stopping condition is hit (max iterations, budget, success criteria).
OpenAI Assistants API vs Custom Agent Loop: Side by Side
The OpenAI Assistants API is a managed service where OpenAI hosts the conversation state, manages history truncation, and orchestrates tool execution for you. A Custom Agent Loop is a self-hosted architecture where your code manages the logic, handling how and when the LLM calls tools, retains memory, and processes steps.
Feature | OpenAI Assistants API | Custom Agent Loop |
|---|---|---|
State and Memory | Managed automatically via hosted Threads. | Stored manually in your database. |
Token Truncation | Automatic when context limits are reached. | Custom logic required to manage the window. |
Tool Execution | Built-in (File Search, Code Interpreter). | Executed by your code via function calling. |
Model Flexibility | Locked into OpenAI models. | Multi-model (OpenAI, Anthropic, open-source). |
Human-in-the-Loop | Complex to pause and prompt user mid-run. | Easy to intercept, audit, and request approval. |
Latency and Cost | Hard to optimise; relies on opaque runs. | Highly optimisable via fine-grained control. |
How to Build an AI Agent Loop in 7 Steps
Step 1: Map the Task Manually
Write down the exact workflow you want to automate, step by step, as if a human were doing it. Include:
Trigger (e.g., new Typeform lead, new Xero invoice overdue by 7 days)
Inputs (fields, documents, prior context)
Actions (emails, CRM updates, Slack messages, report generation)
Success criteria (what completion looks like)
This becomes your functional spec and evaluation dataset later.
Step 2: Define the Minimum Viable Loop
Strip the workflow to the smallest loop that still delivers value. For example:
"For each new lead, send a personalised intro email and log the outcome in HubSpot."
"For each overdue invoice, send a reminder and update the notes in Xero."
Avoid multi-goal agents initially. One clear goal per loop guarantees easier debugging and lower API spend.
Step 3: Establish State Management
Decide how the agent will remember past actions and system states:
Where will conversation history live? (e.g., your database, a vector store, or managed state endpoints)
What needs to persist between runs? (Lead status, last message sent, retry count)
How will you truncate or summarise history to stay within context limits and control token costs?
For SMBs, a simple relational table with fields like conversation_id, message_role, message_content, created_at, and metadata is often enough.
Step 4: Refine and Test the Prompt
Your system prompt is the operating manual for the agent.
Role and scope (e.g., "You are a sales ops assistant for an Australian SMB...")
Allowed tools and exact parameter schemas
Guardrails (no sending emails without a verified address, no guessing invoice amounts)
Output format (e.g., JSON with action, parameters, reasoning)
Test this prompt manually in your API playground with real inputs from your mapped task.
Step 5: Assemble and Run Manually
Wire together your prompt, tool definitions, and execution code (Python or Node.js) to run a continuous loop:
Send the conversation history and tool definitions to the LLM.
Execute tool calls generated by the model.
Append tool execution results back to history.
Call the model again until it returns a final response or hits a max iteration limit.
Run this manually across 10–20 real test cases. Log inputs, tool execution outputs, token consumption, and cost to establish a baseline.
Step 6: Automate and Monitor the First Few Runs
Once the manual loop is stable:
Add a trigger (cron, webhook from your CRM, Zapier, Make, etc.)
Track key operational metrics:
Task success vs. failure rate
Average iterations per task completion
Total API cost per completed workflow
Escalation rates to human-in-the-loop review
Deploy to a small initial cohort (e.g., 10% of incoming leads) and scale as reliability is confirmed.
Step 7: Iterate Based on Real-World Use
Use execution logs to continuously refine system performance:
Tighten prompts where the agent hesitates, loops endlessly, or misinterprets tool schemas.
Add pre-check tools (e.g., validate lead quality as a step prior to outreach).
Adjust stopping criteria (max iteration limits, budget caps, or stall detection).
Maintain human-in-the-loop checkpoints for high-risk actions (e.g., custom pricing discounts, legal commitments).
Frequently Asked Questions
Is my business data secure when running an AI agent loop?
Yes, provided you handle deployment correctly. When using enterprise API tiers from major LLM providers, your data is generally not used for model training. Building a custom agent loop allows you to keep sensitive database access inside your own cloud infrastructure, sending only the necessary sanitised context to the LLM.
Should I roll my own agent loop?
If your workflow requires specific compliance controls, integration with niche internal software, or fine-grained control over costs, building a custom loop using lightweight Python code or frameworks like LangGraph is ideal. If you need a rapid prototype with minimal infrastructure management, the OpenAI Assistants API is faster to launch.
Build Your First AI Agent Loop to Automate Daily Tasks
If you’ve tried automating repetitive tasks like weekly reports, lead qualification, and content updates, an AI agent loop can give your business full control. Book a discovery call with BusinessAI to map your processes and deploy custom AI agent loops built for your business.
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