Move beyond chat. To agents that get work done.
Most generative AI implementations end at chat. The next step, and the one our clients are now demanding, is agents: systems that can plan, choose tools, take actions, and verify their own work, all under human supervision.
Appoly designs agentic systems for Australian organisations that need real outcomes, not demos. We start with the workflow, not the model. Mapping the decisions, data, and approvals an agent has to navigate before a single 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. Not a black box.
- Governance + monitoring: token spend, latency, accuracy, drift. See AI Agent Governance & Security.