Overview
What agentic AI systems integration is, and who it's for
Traditional systems integration connects applications with fixed rules: when X happens in one system, do Y in another. Agentic AI adds software agents that interpret context, decide what to do next and act across several systems: looking up an order in the ERP from a service case, drafting a response from the knowledge base, or creating Jira work from a customer escalation. Agentic AI systems integration is the work of connecting those agents to your platforms safely, so they can read and change the right data and nothing else.
Great Minds Consulting is a systems integrator headquartered in Sydney, with offices in Singapore, Milan and Madrid. We're the integrator for the gaps between platforms, and agents live in those gaps: they're only as useful as the systems they can reach and the data those systems hold. We're an Atlassian Silver Solution Partner and a Klaviyo Gold Advisor, with delivery experience across Salesforce, Microsoft, Adobe, Google and ERP platforms such as NetSuite and SAP.
This service is for Australian mid-market organisations that want agents to do real work across systems, CIOs who need access, approvals and logging designed before agents connect, and teams whose AI pilots stalled because the data or integrations weren't ready.
Agents speed up integration delivery, and they act across systems once integrations exist. Our article on agentic AI in systems integration explains what agents do and what people still own.
Capabilities
Where agentic AI delivers value across your platforms
Agents earn their keep on high-volume, repetitive and checkable work that crosses systems. These are the areas we deliver most.
Platforms
Platforms we integrate agents with
We connect agents through each platform's APIs, its own agent features, or MCP servers where the vendor provides one. Our ERP implementation service using agentic AI and MCP integration service cover two of the most common starting points.
- CRM and customer platforms: Salesforce (Sales, Service, Marketing and Data Cloud), Klaviyo, Adobe Real-Time CDP
- Work and service management: Atlassian Jira, Jira Service Management, Confluence and Rovo
- ERP: Microsoft Dynamics 365, NetSuite, SAP
- Content and commerce: Adobe Experience Manager, Shopify Plus, Akeneo, Bynder
- Cloud and AI: Microsoft Azure, Google Cloud and Gemini, AWS, Claude and ChatGPT Enterprise
- Automation: n8n, UiPath
How we deliver
From first use case to agents in production
Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027, because of rising costs, unclear value or weak risk controls. Our delivery method is built to avoid those three failures: a defined problem first, guardrails before access, and measurement from day one.
We pick processes where autonomous decisions add the most value and the least risk: high volume, two or more systems, clear success measures. You get a shortlist, a business case and a price.
We confirm systems of record, fix the data agents depend on, and build or harden the integrations. Access, approval points and logging are designed and agreed before any agent connects.
Agents and workflows built on your platforms using secure APIs, connectors and MCP, tested against real cases. AI agents do much of the repetitive build work; our consultants own the design and sign off every change.
We monitor outcomes, cost and exceptions, refine agent behaviour, and extend to new processes once the first ones prove their value. We can run the service with you or hand over to your team.
Governance
How we keep agents under control
MCP makes connections easier, but it doesn't decide which agent may read or change what. We apply four rules on every project.
Each agent gets access only to the systems, objects and actions it needs, through service accounts and scoped permissions rather than personal credentials.
Agents read where appropriate, but writes to systems of record need human approval until results are trusted. High-impact actions keep an approval step permanently.
Every agent action is recorded with the data it used, so you can audit outcomes, explain decisions to customers and regulators, and find the cause when something goes wrong.
We document where prompts and data are processed for each model and tool, and keep personal and confidential information out of agents that don't need it.
Australian considerations
What Australian organisations need to check
From 10 December 2026, organisations covered by the Privacy Act must say in their privacy policy when a computer program makes, or does something substantially and directly related to making, decisions that could significantly affect people, and list the kinds of personal information used. The rule covers AI agents and simple rules engines alike, applies to systems already running, and isn't avoided by keeping a person in the loop if that person mostly acts on the agent's recommendation. Our guide to automated decisions and the Privacy Act has a checklist.
Each model provider and agent platform processes prompts in specific regions, and APP 8 applies when personal information goes overseas, so we map where every model call is processed.
For APRA-regulated entities, agents that touch critical operations or information assets fall under CPS 230 and CPS 234: AI and platform vendors may be material service providers, and agent access needs the same controls and testing as any privileged account. The federal Guidance for AI Adoption starts with deciding who is accountable, and we name an owner for every agent.
FAQ
Agentic AI systems integration questions
What does an agentic AI systems integrator do?
It connects AI agents to the platforms where your work and data live, and designs the controls around them. At Great Minds that means integrations and data foundations across Salesforce, Atlassian, Microsoft, Adobe, Google and ERP systems, MCP connections, permissions, approvals and logging, then building and running the agents themselves.
How is agentic AI different from RPA?
RPA follows predefined, rules-based steps through applications. Agentic AI interprets context and makes decisions, adapting when inputs change. The two work well together: agents decide, robots execute repetitive steps, and people approve what matters. Our UiPath and n8n pages cover the automation side.
What is MCP and why does it matter for integration?
The Model Context Protocol is an open standard that lets AI agents use tools and data in other systems through a common interface. Anthropic donated it to the Agentic AI Foundation under the Linux Foundation in December 2025. Vendors including Atlassian, Akeneo and Bynder now offer MCP servers, which makes connecting agents faster, but access still has to be designed.
Is agentic AI secure enough for enterprise systems?
It can be, with guardrails: least-privilege access, secure APIs, human approval for high-impact actions and full logging. We design agents to run inside your own environment and accounts.
Do the Privacy Act's automated decision rules apply to our agents?
They apply if an agent makes, or substantially helps make, decisions that could significantly affect people's rights or interests, using their personal information. A person approving the agent's recommendation doesn't take it out of scope. The obligation starts on 10 December 2026, so inventory your agents and automations before then.
Where should we start?
Start with a high-volume process that spans two or more systems and has clear success measures, such as data migration, lead routing or service triage.
Case studies
Integration and automation work behind our agentic AI practice
For a Dubai-based manufacturer, we migrated more than 2 million records to Microsoft Dynamics 365 and standardised more than 120 business processes.
Insights
Agentic AI and integration insights
Agentic AI in systems integration: what it does and how to govern itWhat AI agents do in integration projects and what people still own, MCP, governance, why projects fail, and a real result.
n8n vs UiPath vs Celigo: choosing an automation and integration layerCompare n8n, UiPath and Celigo on fit, AUD pricing, Australian data residency and AI agents, with a framework for deciding which layer owns which work.
Agentic AI for Australian mid-market: 40 use cases by department40 agentic AI use cases across finance, sales, service, IT, HR and operations, with a scorecard to rank them, AUD running costs and the Australian rules.