TL;DR
AI agents change integration in two ways. They speed up the delivery work: discovery, data mapping, transformation code, testing and reconciliation. And they increasingly act across systems once integrations exist, using standards such as the Model Context Protocol (MCP). Agents do the repetitive work well; people still own architecture, security, approvals and cut-over decisions. On one Great Minds project, AI-driven orchestration cut a retailer's migration from 12 weeks to just over three. The risk is real too: Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027, so start with a defined problem and clear guardrails.
Integration has always been where platform projects slow down. Each system has its own data model, its own quirks and its own owner, and connecting them takes careful, repetitive work: mapping fields, writing transformations, testing edge cases and reconciling records. That's exactly the kind of work AI agents are now good at.
Two ways agents change integration
- Agents that build integrations. During a project, agents help analyse source systems, propose data mappings, generate transformation code and tests, and check migrated data. This is delivery acceleration: the same integration, built faster and checked more thoroughly.
- Agents that use integrations. After go-live, AI agents inside platforms such as Salesforce, Atlassian and Microsoft 365, or your own agents, act across connected systems: looking up an order in the ERP from a service case, or creating Jira work from a customer escalation. Here the integration is what the agent stands on.
Most organisations need both, and they share the same foundations: clean data, clear systems of record, and controlled access.
What agents do, and what people still own
| Phase | Agents do | People own |
|---|---|---|
| Discovery | Read schemas, API documentation and sample data; draft an inventory of fields, objects and dependencies | Business goals, scope and which system is the source of truth for each kind of data |
| Design | Propose field mappings and transformation rules, flagging low-confidence matches | Architecture, security design and sign-off on every mapping |
| Build | Generate transformation code, configuration and test cases | Code review, standards and anything unusual |
| Test and migrate | Run tests, reconcile record counts and values, summarise differences | Deciding what's acceptable, and the go/no-go for cut-over |
| Run | Watch for failures, draft incident summaries and suggested fixes | Approving fixes, and accountability for the service |
This split is how we run integration work at Great Minds: AI agents do the repetitive work, and our architects review and sign off every milestone.
A real result
Example from a Great Minds project
A global retailer integrating e-commerce, CRM and ERP reduced its migration from 12 weeks to just over three weeks using AI-driven orchestration, cutting project costs by 35%.
Results like this depend on how much of a project is repetitive, checkable work. Decisions about architecture, data ownership and cut-over stay with people on every project.
MCP: the standard that connects agents to systems
The Model Context Protocol is an open standard that lets AI agents use tools and data from other systems through a common interface. It's quickly becoming the way platforms expose themselves to agents:
- Atlassian's Rovo MCP Server, generally available since February 2026, lets AI clients search Jira and Confluence and create work items and pages.
- Akeneo's MCP server lets AI tools search and update product data in its PIM.
- OpenAI's Responses API can call remote MCP servers, with approval required by default.
MCP makes connections easier, but it doesn't decide which agent may read or change what. That's still a design decision, and it's where most of the governance work sits. Our MCP integration service designs and builds that layer.
Governing agents that act across systems
- Least privilege. Give each agent access only to the systems, objects and actions it needs, through service accounts rather than personal credentials.
- Approvals for changes. Let agents read freely where appropriate, but require human approval for writes to systems of record until you trust the results.
- Logging. Record what each agent did, with what data, so you can audit and explain outcomes.
- Data handling. Know where prompts and data are processed, especially for personal information. Our OpenAI for enterprise guide covers data residency in Australia.
- Automated decisions. If agents make decisions that significantly affect people, the Privacy Act's automated decision rules apply from 10 December 2026.
Why agentic AI projects fail
Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027, because of escalating costs, unclear business value or inadequate risk controls. It also warns that many use cases sold as agentic don't need agents at all. In integration, we see the same patterns:
- No clear problem: agents bought before anyone agreed which process to improve.
- Poor source data: agents map and move bad data faster.
- No systems of record: when two systems both claim to own customer data, no agent can resolve it.
- Guardrails added late: access and approvals designed after agents are already connected.
Where agents fit, and where they don't
Agents earn their keep on high-volume, repetitive and checkable work: large migrations, many similar mappings, extensive testing and reconciliation, and run-time tasks with clear rules and a person to review exceptions. They help less with one-off, judgement-heavy decisions, undefined requirements, or work that depends on negotiating between teams. Those still need experienced people.
How Great Minds helps
Great Minds is an integrator first. We connect Salesforce, Atlassian, Microsoft Dynamics 365, Adobe and the other platforms mid-market organisations run on, and use AI agents for the repetitive integration and migration work, with our architects accountable for every milestone. See our agentic AI systems integration and agentic AI for ERP implementations solutions.
Frequently asked questions
What is agentic AI in systems integration?
AI agents that carry out integration tasks, such as analysing systems, mapping data, generating code and tests, and reconciling records, and agents that act across systems once they're connected. People stay responsible for design, approvals and outcomes.
Does agentic AI replace integration platforms like MuleSoft or Boomi?
No. Integration platforms still run the connections reliably at scale. Agents help build and test those connections faster, and can use them at run time.
How much faster are integration projects with AI agents?
It depends on how much of the project is repetitive mapping, transformation and testing. On one Great Minds project, a retailer's migration went from 12 weeks to just over three. Projects dominated by design decisions see smaller gains.
Is it safe to let AI agents change data in our systems?
It can be, with least-privilege access, approvals for writes to systems of record, and full logging. Start with read-only tasks and expand as results prove reliable.
Where to start
Pick one integration or migration on your roadmap with a lot of repetitive mapping and testing. Measure how long similar work took last time. That baseline is how you'll know whether agents are earning their keep. We're happy to help you scope it.