AI Agent Development Services in Australia
JDML builds AI agents that do real work for Australian businesses: agents that search your knowledge base, call external APIs, reason across documents, trigger approvals, and complete multi-step tasks inside real products. Built with Claude, GPT, and open models. Deployed on Google Cloud with tracing, evaluations, guardrails, and human review flows.
AI agents for Australian businesses
The Australian market is early in adopting agent-based AI, which means the teams that move now build a durable operational advantage. We've built agents for document-heavy workflows, competitor monitoring, internal knowledge retrieval, customer-facing copilots, and ops automation. In every case, the agent is deployed with proper guardrails, cost controls, and human oversight, not as a demo.
How we build agent systems
We use Google ADK, LangGraph, and direct model APIs depending on what the problem actually needs. RAG pipelines are backed by vector search (pgvector, Vertex AI Vector Search) with hybrid retrieval for relevance. Tool use is built with structured function calling, tested against adversarial inputs, and instrumented with full tracing so you can see exactly what the agent did and why.
Testing and safety for production agents
Agent testing is different from standard software testing because the output is probabilistic. We build evaluation harnesses with expected-output datasets, adversarial prompt tests, regression suites for known failure modes, and human review queues for high-risk actions. We don't deploy agents that haven't been tested against the edge cases that would embarrass you in front of a client.
- →RAG architectures with vector and hybrid search
- →Google ADK and LangGraph agent systems
- →Tool use and function calling across APIs
- →MCP client and server integrations
- →Multi-step planning and multi-agent orchestration
- →Document extraction and structured outputs
- →Memory, retrieval, and context management
- →Guardrails, cost controls, tracing, and evals
- →Human-in-the-loop approval flows
- →Internal copilots and admin dashboards
Internal copilots, ops tooling, and document-heavy workflows
Teams that want agent capability without sacrificing control and oversight
Products that need tool use, approvals, and clear operational guardrails
Questions we get.
Which AI models do you use for agent development?
Claude (Anthropic) for most agent workloads because of its strong tool use and instruction-following. GPT-4o and open-weight models like Qwen for specific cases where cost, latency, or local deployment requirements make them a better fit.
How do you handle agent safety and guardrails?
Every agent we ship has explicit output validation, rate limiting, cost controls, human approval gates for high-risk actions, and full trace logging. We treat safety as a hard requirement, not a nice-to-have.
Can you integrate an agent with our existing tools and systems?
Yes. Tool use and function calling lets agents interact with CRMs, databases, internal APIs, email, Slack, Google Workspace, and most external services. We also build MCP server integrations where standardised tool access makes sense.
Ready to get started?
Tell us about your project. We reply within 24 hours, always from the engineers.
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