1. Service
  2. AI Agents

AI AGENTS SERVICES AUSTRALIA

AI Agents Consulting

Put AI to work, not just to chat. We design and build secure AI agents that take action across your systems, follow your rules and bring a person in when it matters.

Talk to an AI agents expert See use cases
AI Agents

Sound familiar?

  • Staff spend hours on repetitive, rules-based work across several systems.
  • Chatbots that answer questions but can't actually get anything done.
  • AI pilots that impress in a demo but never reach production.
  • Concerns about security, privacy and AI acting without oversight.

What you'll get

  • Agents that complete real tasks in your systems, within clear rules.
  • Human approval built in wherever decisions carry risk.
  • Secure deployment in your own Microsoft environment, with full audit trails.
  • Measurable time savings, tracked from the first pilot onwards.

AI agents are software that work towards a defined goal. They reason over your instructions and data, use tools to take actions in your systems, and hand over to a person when a decision needs one. Unlike traditional automation, which follows fixed rules, an agent can cope with variation in requests and documents. That makes agents a good fit for case management, service requests, data quality checks and process work, scaling what your people can do rather than replacing them.

Our Advantage

Our team brings over two decades of experience in data consulting, analytics and AI delivery. Our data engineers, scientists and AI specialists design AI agents that are:

AI consultants

Goal-oriented

Each agent is built for one clear business outcome, with the tasks, rules and limits that outcome needs.

Grounded in your data

Agents work from your approved documents and systems, and show where an answer came from, rather than relying on what a model happens to know.

Scalable and reliable

Designed to perform consistently under enterprise and government workloads, and to extend to new processes without starting again.

How we work

1

Identify

We find the tasks where an agent saves the most time, at a level of risk you're comfortable with.

Estimate the savings with our free calculator
2

Pilot

We build a working agent on your systems and test it with real users and real data.

3

Govern

We add guardrails, human approvals, security controls and monitoring before go-live.

4

Scale

We roll out, measure the time saved and extend agents to new processes.

Free tools

Plan your first agent

Two free tools to help you decide where an agent would pay off, and whether you're ready to build one.

Datum the turtle designing an AI agent workflow
AI Agent Savings Estimator Estimate the hours and dollars an AI agent could save your team. Pick a process, adjust the inputs and compare three scenarios. Estimate your savings
AI Readiness Checklist A 5-minute checklist that gives you an instant readiness score, so you know what to put in place before your first agent. Check your readiness

AI Agents Use Cases

AI agents don't just answer questions. They take action within clear rules, and bring a person in when it matters.

Customer service

An agent that resolves requests, not just answers them

The challenge
Routine requests like changing a booking or checking an order fill the queue and keep customers waiting.
What we build
An agent that verifies the customer, looks up and updates bookings or orders through your systems within agreed rules, and hands over to staff with full context.
The outcome
Routine requests resolved around the clock, and staff free to focus on complex cases.
  • Azure AI Foundry
  • Copilot Studio
  • API integration
  • Human handover
Mining & utilities

A maintenance planning agent

The challenge
Planners spend hours combining work orders, parts availability and crew rosters into a weekly plan.
What we build
An agent that drafts the plan from ERP work orders, inventory and rosters, flags clashes and shortages, and waits for the planner's approval.
The outcome
Plans prepared in a fraction of the time, with fewer clashes and parts surprises.
  • Multi-agent
  • ERP integration
  • Human approval
  • Azure OpenAI
Finance teams

A month-end close assistant

The challenge
Reconciliations and variance explanations are done manually, squeezing the team every month-end.
What we build
An agent that runs reconciliations, investigates variances against source data and drafts commentary for the finance team to review.
The outcome
A faster close, with analysts spending their time on judgement rather than gathering data.
  • Azure OpenAI
  • Finance systems
  • Audit trail
  • Human review
Sales teams

A sales research and follow-up agent

The challenge
Sales reps spend hours researching prospects, writing follow-ups and updating the CRM.
What we build
An agent that prepares account briefs before meetings, drafts follow-up emails and keeps Dynamics 365 up to date.
The outcome
More time selling, better-prepared meetings and a CRM people can trust.
  • Dynamics 365
  • Microsoft 365 Copilot
  • Copilot Studio
  • CRM automation

Facing something similar? Talk to us about your use case.

Not sure where AI agents fit?

Take our free 5-minute AI Readiness Checklist for an instant score across strategy, data, platform, people and governance.

Take the free AI readiness checklist Talk to an AI agents expert

Frequently Asked Questions

AI Agents are computer programs that can reason through a task and act on it, within limits you set, to complete specific work. They use artificial intelligence to understand information, make decisions, and perform actions much like a digital assistant that can analyse data, automate repetitive work, or interact with people and systems. In simple terms, AI Agents help businesses and individuals save time and effort by doing work that usually requires human intelligence.

Aspect Traditional AI Generative AI Agentic AI
Definition Uses predefined algorithms and statistical models to recognise patterns, classify data, or make predictions based on structured inputs. Uses deep learning models to generate new content such as text, images, code, or audio. Builds on generative AI with goal-driven reasoning, memory, and autonomy—planning, acting, and learning with minimal human intervention.
Core Capability Pattern recognition, rule-based decision-making, predictive analytics. Content creation and synthesis across multiple modalities. Autonomous decision-making, adaptive planning, multi-step task execution.
Data Dependency Structured and labelled datasets. Large volumes of unstructured data (text, images, audio). Structured and unstructured data, with real-time feedback and memory loops.
Examples Spam filters, credit-risk scoring, demand forecasting, recommendation systems. ChatGPT, DALL-E, Stable Diffusion, GitHub Copilot. AutoGPT, Devin, Microsoft Copilot Agents, LangGraph.
Human Involvement High – humans define rules and workflows. Moderate – humans provide prompts or guidance. Low – humans define goals; agent plans and executes autonomously.
Goal Optimise decisions or predictions within a fixed scope. Create new, contextually relevant content. Achieve defined objectives through self-directed reasoning and action.
Typical Use Cases Fraud detection, quality control, process automation, predictive maintenance. Content generation, design ideation, conversational assistants. Autonomous business processes, adaptive customer service, workflow automation.
Technology Stack
  • Python, R
  • scikit-learn, XGBoost, LightGBM
  • TensorFlow (classical ML)
  • SQL-based ML (BigQuery ML, Azure ML)
  • PyTorch, TensorFlow, JAX
  • Hugging Face Transformers
  • Azure OpenAI, Google Gemini and other LLM APIs
  • Stable Diffusion, Midjourney
  • LangChain for LLM orchestration
  • LangChain, LangGraph, LlamaIndex
  • AutoGPT, BabyAGI frameworks
  • Azure OpenAI, Gemini and other LLM APIs
  • Vector DBs: Pinecone, Weaviate, Chroma
  • Tool integrations (APIs, RPA, cloud connectors)
Level of Autonomy & Adaptability Low – reacts to inputs as programmed; needs human intervention to adjust behaviour. Medium – generates novel outputs based on prompts; limited self-correction via feedback. High – plans, acts, adapts to new info; learns from interactions to achieve goals.
Strengths & Limitations Strengths: Reliable, interpretable, fast on structured tasks.
Limitations: Cannot generate new content; limited flexibility. 		Strengths: Can create new content; versatile across tasks.
Limitations: May produce incorrect or biased outputs; limited autonomous reasoning. 		Strengths: Highly autonomous and adaptive; performs complex multi-step tasks.
Limitations: Complex setup; may need monitoring; resource-intensive.

AI Agents are delivering tangible outcomes across both private and public sectors:

  • Customer Support Automation: Intelligent virtual assistants that handle routine inquiries and escalate complex cases.
  • Predictive Maintenance: AI-driven monitoring that anticipates equipment issues before they occur.
  • Fraud Detection: Real-time analysis of transaction data to identify and prevent anomalies.
  • Personalised Marketing: AI-driven targeting based on customer behaviour and preferences.
  • Supply Chain Optimisation: Demand forecasting and inventory management for cost and delivery efficiency.

Speak to an AI Agents Expert

From the first idea to a secure agent in production, we work alongside your team so every agent is aligned to your goals, safe by design and delivering measurable value. Book a free consultation to explore where AI agents can make the biggest difference in your organisation.

Contact AI expert