In practice
At AIM Housing, the AI layer sits inside the operations platform. It helps staff prioritise and route work across field teams on mobile and the back office on the web.
Agents that get work done
Most generative AI implementations end at chat. The next step is agents: systems that can plan, choose tools, take actions, and verify their own work, under human supervision.
We design these for Australian organisations that need a real outcome. We start with the workflow, not the model. The decisions, data, and approvals an agent has to navigate get mapped before a line of code is written.
Where agents pay off
- Internal operations: triaging tickets, drafting customer responses, reconciling records, generating compliance reports.
- Knowledge work: research, summarisation, document drafting against your own data.
- Customer experience: guided self-service across complex product catalogues or policy documents.
- Engineering: code review, dependency upgrades, infrastructure-as-code generation.
How we build them
- Workflow mapping: we shadow the people doing the work today and identify the decision points an agent can safely own.
- Tool design: agents are only as capable as the tools they can call. We build secure, well-scoped tool interfaces (often via MCP servers).
- Evaluation harness: before an agent touches production, we build a regression suite of real tasks with measurable success criteria.
- Human-in-the-loop UX: clear escalation paths, approval gates, and audit trails.
- Governance + monitoring: token spend, latency, accuracy, drift. See AI Agent Governance & Security.
