Building production-ready agents is the new AI battleground

Today in Australia, you would struggle to find an organisation that isn’t either using or looking to use AI. But the type and maturity of usage varies greatly.

Because of this, AI adoption across the country has emerged as a two-speed economy.

Financial institutions and digital natives are aggressively driving advanced implementation, fuelled by significant capital and competitive necessity.

In contrast, the SMB and government sectors have been slower to adopt, restricted by limited access to capital or less immediate pressure to drive productivity gains.

According to Moxie Research – AI Outlook: Australia 2026 – 93% of leadership teams in Australia are in favour of AI yet only 62% are deliberate in driving organisational change from the top.

When asked how effectively their leadership team supports AI initiatives, local businesses answered:

• Champions AI initiatives, actively drives AI strategy: 62%
• Interested in AI but provides limited support: 31%
• Little to no engagement with AI initiatives: 7%

Despite a surge in Generative AI (GenAI) experimentation, broader market maturity remains low. While enthusiasm is high, many Australian organisations are currently limited to simple knowledge retrieval use cases.

Their ability to unlock meaningful business value is currently stalled by lacking capability and experience of how to productionise high return on investment (ROI) use cases.

There are a few key areas of adoption which are seeing consistent high value return across the market including AI for software development, intelligent document automation, knowledge management and customer enquiries.

A significant indicator of the relative low maturity is that nearly two-thirds of organisations still lack formal AI frameworks and policies, suggesting that while usage is high, the standardisation, governance and guardrails required for enterprise-grade maturity are often missing.

In that context, the priority for Australian organisations over the next 12-24 months should be focused on a few key areas:

• AI enablement: The major barrier to entry isn’t technology, it’s people. To drive value from AI, organisations need a team that is enthusiastic and knowledgeable of the value that AI can bring to them and their business. This enablement of the people using AI needs to be the priority for anyone looking to achieve the most value possible from AI.
• AI production: Experimenting with AI is critical and Australian organisations should continue to push the boundaries and understand where potential business value can be obtained. However, production solutions need to be the primary focus as it can be easy to get caught up in the hype of possibilities without building the frameworks to ensure that you are in a position to productionise the highest ROI AI solutions.

While 2025 was labelled as the ‘year of the agent’, the priority for the next 12-24 months will be ‘productionising’ these agents – moving from proof-of-concept to consistent, cost-effective and efficient solutions that can handle the complexity of real-world business usage.

Moving from pilot to production

From a use case standpoint, we’re seeing strong traction in back-end process automation – specifically intelligent document processing such as claims automation and loan processing.

These internal processes are great candidates for AI where traditional automation techniques have failed to handle the ambiguity of the inputs across a large range of unstructured documents and therefore relied on lengthy human processes.

Simultaneously, there is a massive push into automated customer interactions via voice, email and chat, where the goal is to provide self-service human-like interactions which allow customers to interact with businesses via a range of modalities at any time without the potential wait times and high cost of operations.

Finally, we’re seeing the emergence of Generative UI (GenUI), where traditional user interfaces are transformed with natural language interfaces to enable users to perform complex tasks through a simple natural language prompt.

On balance, the technology priority is shifting from the models themselves to the ecosystem surrounding them – specifically tools, evaluations (evals), context and memory.

As we move toward multi-agent systems or ‘swarms’ that can collaborate to solve complex problems, the focus will shift from which model is used to how the model has the most context available using efficient tiered memory to improve accuracy.

This memory and context – combined with the ability to interact with other agents and third-party systems to gather real-time information or perform actions – will be the differentiator.

Prioritising proof of value

But challenges remain. The immediate priority of businesses is centred on shifting from hype-driven experimentation to demonstrable ROI.

The last 12 months has seen a lot of AI experimentation combined with the fastest rate of technology change in history, this has led to organisations being unable to identify the highest value use cases nor understand the true cost / effort to build production grade AI solutions.

The primary challenge isn’t technical capability, rather an ability to achieve the ‘reliability, speed and cost’ required for production.

Spinning up a proof of concept (PoC) is relatively easy but productionising an agent that performs reliably at scale is incredibly difficult.

Many organisations struggle to quantify the value of AI because they lack clear measurement frameworks, agreed success criteria and consistent baselines.

According to Moxie Research, the frequency of evaluating the impact and performance of AI initiatives is currently:

• We have a systematic process for measuring and analysing AI performance: 41%
• We assess AI initiatives periodically with a few key metrics: 39%
• We occasionally evaluate AI projects, but it’s inconsistent: 14%
• We do not track the performance or impact of AI initiatives: 6%

Furthermore, we see a prevalent issue where organisations fail to validate the feasibility and ROI of a use case before building, often choosing ‘happy path’ automation while ignoring the complex exception flows that drive actual business cost.

Building on this, the most significant risk is expecting 100% reliability – business stakeholders often expect AI to function with the 100% predictability of traditional software, failing to account for the probabilistic nature of large language models (LLMs).

To mitigate this, organisations need to implement robust guardrails and processes to verify outputs before they reach the user.

The secondary risk is getting caught in the AI agent hype – AI is extremely powerful and can provide significant value for businesses in many areas however it’s not a silver bullet.

Agents come with complexities that need to be considered when designing a solution, typically these fall into the non-functional bucket of cost, speed, security and reliability.

Whilst an agent can solve a problem, the risk to many organisations is assuming it’s the right answer to things which can be solved in a better way.

In response, AI is driving technology partners closer to the core business than ever before.

We’re no longer just receiving technical specifications; we’re now deeply involved in reimagining business processes. Because agentic AI requires a profound understanding of the ‘human’ process it is augmenting or replacing.

This approach is playing out in the data. According to Moxie Research, the most important characteristics that Australian organisations seek when working with an AI partner are:

• Ability to manage end-to-end AI projects (strategy to implementation): 56%
• Ability to provide end-to-end AI and tech solutions: 46%
• Deep AI skills in specific industry sector: 41%
• Collaborative approach / flexible contracts: 40%
• Deep skills in specific AI solutions: 35%

We’re helping CIOs and CTOs navigate the cultural shift of AI literacy, ensuring that ownership of AI doesn’t sit in a silo but is embedded across the organisation to drive genuine value.

Adam Durbin is CTO at Mantel Group. As part of Moxie Top Minds, Adam contributed to AI Outlook: Australia 2026 by Moxie Insights. Download the report here.