AI Agents & Automation in Australia

What an AI agent means here

An AI agent at Bedstone is software that uses a language model as a reasoning layer to decide what to do next within a defined toolset. It reads inputs (structured or unstructured), plans steps, calls tools (CRM lookups, API requests, database queries, document retrieval, third-party integrations), evaluates results, retries or escalates. Not a chatbot. Not a wrapper around ChatGPT. A real production system with accountability.

What we do not do: prompt-engineering-as-a-service, single-shot LLM calls dressed up as agents, generic chatbots with no actions, or "AI strategy decks" without an engineering deliverable. If the build does not result in a system operating against real data and real users, it is not an agent engagement.

Where this fits

AI agents are the right call when most of the following are true. If fewer than half apply, deterministic automation or off-the-shelf software is usually a better starting point.

You have a workflow with unstructured input (emails, documents, customer queries, scanned forms, chat) that traditional rules cannot enumerate cleanly.
The workflow has variable steps that depend on context. Sometimes you look up a customer, sometimes you escalate, sometimes you fetch supporting data from a different system.
The work currently consumes meaningful headcount hours per week. Support, sales operations, document processing, reconciliation, internal helpdesk, content production.
You have API access to the systems involved, or the willingness to build it.
You have budget allocated for the engagement and a team that can adopt the system after handover.
The annual cost of the manual workflow is at least 5-10x the likely cost of an engagement.

What we build

Concrete agent patterns we have shipped to production. Most engagements combine several.

Bedstone OS: your internal AI workspace. Your own private domain (e.g. ai.yourcompany.com.au) that connects every business system you run (CRM, ERP, accounting, document store, ticketing, project management, internal docs) into one chat-style interface backed by an LLM. Your team asks questions in plain English; the system answers from your actual data, not from public training data. AU-region hosted, your data stays yours. The fastest way to get an AI capability live inside the business that staff actually use.
Internal IT support agents. Replace tier-1 IT support with agents that handle password resets, access requests, software install help, and the long tail of repetitive helpdesk queries. Sized for companies with 100+ employees burning hours on repeat tickets.
Customer service and support automation. Agents that resolve, not just triage. Read across CRM, knowledge base, billing, ticket history, then take action. Draft replies, issue refunds, raise warranty claims, escalate when uncertain. Built for SaaS, e-commerce, and service businesses with weekly ticket volume in the thousands.
Sales operations and lead processing. Inbound lead qualification, ICP scoring, personalised outbound drafting, follow-up cadence, CRM hygiene. For B2B sales teams where the bottleneck is response speed and inbox volume.
Quote and proposal generation. Auto-generated quotes, proposals, and statements of work from spec, RFQ, or call notes. Integrated with pricing engines, CRM, and document-signing systems. For agencies, professional services, manufacturers, and trades businesses.
Document and contract intelligence. Extract structured data from contracts, invoices, supplier forms, compliance documents, scanned PDFs. Reduces hours of manual data entry per week. For legal, financial services, procurement, healthcare, government.
Reconciliation and operational reporting. Daily three-way reconciliation across accounting, billing, payment processor, ledger. Automated anomaly detection and management reports. For finance teams and operations leaders.
Knowledge base agents. "Ask anything" agents over company docs, runbooks, training material, past tickets. For organisations with growing documentation that nobody can navigate.
Recruitment and onboarding automation. Resume screening, candidate qualification, reference check coordination, new-hire onboarding flows, paperwork generation. For HR teams and recruitment agencies.
Compliance automation. AUSTRAC reporting, AML and KYC workflows, R&D documentation, healthcare reporting, mining safety compliance, food safety, workplace incident reporting. We build the agent and the audit trail.
Real-time content production pipelines. Stream processing, automated editing, generative captioning, multi-platform distribution. Built for media businesses, content agencies, and software companies running creator-facing products.

How we work

Five-step engagement from intake to rollout. We work on both a project basis and an ongoing basis. A fixed-scope project takes the agent to production, then transitions to ongoing support and maintenance via monthly retainer, fractional engagement, or scheduled improvement sprints. One-off audits available for teams not yet ready to build.

Audit. Department-by-department workflow review. Score each candidate problem for agent-suitability, dollar impact, and difficulty. The audit deliverable is yours regardless of whether you proceed.
Scope sprint. Concrete plan with milestones, integration list, success metrics, eval plan, ranges for cost and timeline. Signed off before anything is built.
Proof of concept. Working agent in your environment, against real data and real users. Not a demo. Not a sandbox. The agent does the work end-to-end.
Verification. Eval set runs, adversarial testing, prompt-injection testing, load testing, security review, honest go-or-no-go on rollout.
Rollout. Staged deploy, monitoring, rollback paths, documentation. Handover training. Optional retainer for ongoing iteration as models evolve.

Working with your IT team

Most agent builds touch systems your IT department already runs. We partner with them rather than route around them. We speak their language because we came up as software engineers and infrastructure operators ourselves, so the conversation about API keys, service accounts, network access, identity provider integration, and change-management approvals is direct and technical. No translation layer required.

In practice that means your IT team gets a clean access request with scopes spelled out, security implications stated up front, and integration documentation they can audit. We do not ask for blanket admin credentials. We work to least-privilege scopes, document what we use, and hand the access back at engagement end. For organisations with strict change-management or IRAP-aware environments, we adapt to your process.

What week one looks like

The audit phase is where most AI engagements drift. We compress it into a single sprint week so you have a concrete plan before the second invoice. Day-by-day shape of a typical first week:

Day 1. Kickoff workshop. Two to three hours on-site for Brisbane and SEQ clients, video for remote. We meet the team who will operate the agent, walk the workflows where the work happens, capture data flows live.
Day 2. Department interviews. 30 to 45 minute conversations with each function in scope. Support, sales, finance, ops, IT. We ask "what would the perfect version of this workflow look like." High-leverage problems surface here.
Day 3. System and data mapping. Hands-on with your stack. CRM, ticketing, ERP, billing, document store, knowledge base. We assess API access, data shape, integration paths, and what blocks an agent from doing the work today.
Day 4. Scoring and prioritisation. Every candidate workflow scored on four axes: dollar impact, headcount hours reclaimed, agent-suitability, rollout risk. Ranked shortlist with conservative ROI math.
Day 5. Scope draft and review. Concrete plan, timeline, success metrics, integration list, eval approach, R&D eligibility note. Sent before end of week, reviewed live the following Monday.

By end of week one you have an audit report, a ranked shortlist, a concrete scope document, and an honest read on whether to proceed. If we decide an engagement is not the right fit, you keep the audit and use it to brief whoever you engage next.

What you get at the end

By the end of a typical engagement, you hold more than a running agent. You hold everything required to operate, monitor, evolve, and defend the system in production.

The working agent. Source code, infrastructure-as-code, deployment pipeline, model configuration, integration code. All yours.
Eval set and scoring framework. A test suite that runs against the agent on every change. Catches regressions before they hit production.
Observability and audit trail. Logs, traces, prompt history, decision rationale, cost dashboard. Every action the agent takes is recorded and queryable.
Operational runbook. What to do when the agent fails, how to roll back, how to onboard a new tool, how to handle a model upgrade. Written for your team.
Handover training. Working sessions so your team operates the agent without us. On-site by default for Brisbane and SEQ.
An R&D readiness pack. Sprint documentation aligned with the Australian R&D Tax Incentive so your tax specialist can file with confidence.

AI agents vs alternatives

Most operators considering AI for a workflow land on one of four paths. Each has trade-offs that matter at decision time.

Off-the-shelf chatbot or AI SaaS. Fast to deploy, fixed subscription, vendor maintains it. Right when your use case is generic enough that the SaaS template fits. Less right when the workflow needs deep integration with your own data, or the SaaS vendor's roadmap and pricing are outside your control.

Traditional automation (RPA, scripts, deterministic). Right when inputs are structured, steps are fixed, and the rules can be written down in advance. Faster, cheaper, more reliable than agents for the workflows that fit this shape. We use deterministic code wherever it earns its place; we do not force agents onto workflows that do not need them.

Offshore development shop. Cheaper hourly rates, but rarely the right call once you factor in the 43.5% R&D Tax Incentive available for engaging Australian operators. AU-specific considerations like ATO workflows, AUSTRAC reporting, AU privacy law, IRAP-aware deployment, and operational context are usually unfamiliar. The hourly saving disappears once you absorb rework and the lost tax offset.

Australian AI agency. Senior engineers do the work end-to-end. The team covers agents, software, integrations, security, infrastructure as one capability. Right when production-grade reliability matters and you want a partner who treats the agent as a live system with safety and observability built in.

How to evaluate a partner

Six checks worth running before signing with any AI agent agency. They surface the difference between a senior shop that ships and a marketing layer over juniors.

Ask who actually writes the code. The named partner who sells, or someone you have never met. Get specific. Get LinkedIn profiles.
Ask for a production reference. "What is the most recent agent you shipped that is operating in production right now, against real users?" An honest answer takes thirty seconds.
Ask how they handle AI-specific risks. Hallucination, drift, prompt injection, model-update breakage, cost blowouts. The answer should be a process with concrete tooling, not a hand-wave.
Ask about evals. A real agent agency has an eval set, runs it on every change, and can describe what is in it. No evals means no production confidence.
Ask about handover and documentation. A real agency has runbooks, training plans, and documentation as deliverables, not afterthoughts.
Ask about AU regulatory context. ATO, AUSTRAC, ASIC, IRAP, AU privacy law, R&D Tax Incentive documentation. An offshore shop or US-centric firm will struggle here.

Engagement patterns

Anonymised examples of the shape AI agent engagements typically take.

Real-time content production pipeline for a software company

An Australian software company processing more than 100 hours of live video per day was bottlenecked by manual highlight editing. The team was spending around 30 minutes per clip on cuts, captions, and uploads. We built an AI-agent pipeline bespoke to their stack. It watches streams in real time, detects engagement moments via combined audio transcript and visual analysis, automatically cuts vertical clips with FFmpeg, generates platform-tuned captions, and delivers ready-to-post content into the content management system. Daily manual editing dropped from four hours to under ten minutes of review. The pipeline runs at effectively zero marginal cost because it sits inside their existing infrastructure. Output: more than 200 clips per day from continuous live streams.

AI-augmented operations dashboard for a mid-market operator

A mid-market Australian operator with content distribution running across multiple channels needed a unified dashboard for tracking, scheduling, and reviewing AI-generated artefacts before publication. We built an internal Next.js + Postgres platform with role-based access, audit trails, and AI agent integration for automated quality scoring. Four prior workflows (separate spreadsheets, separate review threads, manual logging) collapsed into one tool with a complete audit trail. Reviewer time per artefact halved.

Operational platform with embedded AI for a professional services group

An Australian professional services group running several businesses needed a single place to operate from. We built the platform that holds it together. Compliance work, projects, custom calculators, documents, client portals, email threading. AI agents embedded where the work was unstructured. Logins work across the group via Google Workspace and Microsoft. Each business has its own data and configuration. All hosted in Australia. Workflows that used to live in five places now live in one.

Common stack and models

We are model-agnostic and integrate against the actual stack you run. The default toolkit:

Models. OpenAI, Anthropic Claude, Google Gemini, plus open-weight models (Llama, Qwen, DeepSeek) via Ollama, vLLM, or AWS Bedrock for on-premise or sovereignty-critical workloads. Model choice driven by latency, cost, accuracy, and data-residency requirements per workflow.
Orchestration. LangGraph, Pydantic AI, custom orchestration where the framework would add more weight than value. We pick the framework that fits the agent, not the other way around.
Vector stores and retrieval. pgvector, Pinecone, Weaviate, Qdrant. Hybrid retrieval where semantic and keyword search both pull weight.
Evals and observability. Langfuse, Braintrust, custom eval rigs. Every agent ships with a test suite that runs on every change.
Integrations. Salesforce, HubSpot, Microsoft Dynamics, Xero, MYOB, NetSuite, M365, Google Workspace, Slack, Teams, Zendesk, Intercom, Shopify, Stripe, and most major SaaS platforms with documented APIs.
Infrastructure. AWS, Azure, GCP. AU-region by default. On-premise deployments via Ollama / vLLM for workloads with data sovereignty or air-gap requirements.

How we handle production risks

An agent in production is a live system that can drift, hallucinate, fail silently, or escalate cost. Every engagement designs for these failure modes before the agent goes live.

Hallucination. Mitigated through retrieval-augmented generation against your actual data, strict tool use, citation requirements, and human-in-the-loop on consequential actions. We do not let the agent invent facts about your customers.
Drift after model updates. Eval suite runs against the agent on every model change. Regressions are caught before they reach production.
Prompt injection. Defense in depth: input sanitisation, output validation, scope limits on what the agent can do, separation of trusted and untrusted context.
Cost blowouts. Per-workflow cost budgets, real-time spend monitoring, alerts at threshold, automatic shutoff at hard limits.
Silent failure. Structured logging, distributed tracing, alerting on unexpected agent paths or extended retry chains. If the agent stops being useful, you find out before your customers do.
Audit and accountability. Every agent decision is logged with input, reasoning, tool calls, and output. Reviewable on demand for compliance or incident response.

How we structure engagements

Every engagement is scoped to the specific work. We do not publish fixed pricing because every business is different. Cost ranges and timelines are agreed up front in the scope document, before any build starts. Four common structures:

Fixed-scope project. Defined deliverable, defined timeline, defined acceptance criteria. Useful when you know exactly what you want built.
Monthly retainer. Senior engineering capacity, embedded technical lead, ongoing iteration. Useful when you have an evolving roadmap.
Fractional engagement. Senior advisory and oversight, delivery handled by your team or a separate vendor. Useful when you have engineering capacity but no AI seniority.
One-off audit or strategy work. Useful before you commit to a build path. The audit output is yours regardless.

Most mid-market engagements sit in a range that the R&D Tax Incentive can offset a meaningful portion of. Reach out and we will reply within 24 hours with the shape and pricing that fits your situation.

R&D Tax Incentive

Agentic AI work is textbook eligible R&D activity under the Australian 43.5% R&D Tax Incentive, jointly administered by AusIndustry and the Australian Taxation Office. For Australian mid-market operators this often funds a meaningful portion of the engagement. We document sprints properly so your tax specialist can file with confidence. We hand over the readiness pack. We do not lodge the claim. See our guide to the R&D Tax Incentive for AI in Australia for the detail.

Common questions

What is the difference between an AI agent and a chatbot?

A chatbot generates text in response to user input. An agent generates text and takes action. An agent reads your data, plans steps, calls tools (APIs, databases, third-party services), evaluates results, and either completes the task or escalates. The difference is whether the system actually does the work, or just describes what should be done.

How do you stop agents from hallucinating?

Retrieval-augmented generation against your actual data, strict tool-use boundaries, citation requirements, and human-in-the-loop checkpoints on consequential actions. The agent answers from real sources, with the source recorded, and high-stakes decisions are reviewed before they execute. We do not eliminate hallucination entirely; we design the system so when it happens, it is caught and bounded.

How long does an AI agent engagement take?

Audits run one to two weeks. Proof of concept typically four to six weeks. Full builds vary by scope but most ship in eight to sixteen weeks from audit to production. Ongoing iteration runs as a monthly retainer once the agent is live.

Can the agent integrate with our existing CRM, ERP, or other systems?

Almost always yes. We integrate against documented APIs from Salesforce, HubSpot, Microsoft Dynamics, Xero, MYOB, NetSuite, M365, Google Workspace, and most major SaaS platforms. For systems with weaker APIs we use middleware, direct database access, or RPA layers where needed. Integration approach is scoped in the audit phase.

Which AI models do you use?

Model-agnostic. OpenAI, Anthropic Claude, Google Gemini, and open-weight models (Llama, Qwen, DeepSeek) where workloads require on-premise hosting or strict data sovereignty. Model choice per workflow is driven by latency, cost, accuracy, and data-residency requirements, not vendor preference.

Is our data safe with an AI agent?

Yes. We sign NDAs, use AU-region infrastructure where required, architect for data residency, and do not train models on your data. Sensitive workloads are designed to IRAP-aware standards when required. We default to providers whose terms of service prohibit training on customer data, and we use on-premise inference for the most sensitive workloads.

What happens after the agent is live?

You own it. Source code, infrastructure, eval suite, runbooks, documentation. Most clients continue on a monthly support retainer for ongoing iteration and model upgrades. Some operate fully independently after handover. Both are fine. We do not sell dependency.

Can we host the agent on-premise?

Yes. We deploy open-weight models via Ollama, vLLM, or AWS Bedrock for workloads with data sovereignty, air-gap, or regulatory requirements. Hybrid architectures (sensitive workflows on-premise, non-sensitive in cloud) are common in government, healthcare, financial services, and mining.