Reducing waste in AI Agent projects starts with recognising that overspend can happen because teams invest in the wrong kind of “agent” work for the outcome they actually need. 

Think of this as simply managing AI spend wisely: you don’t just ask, “Can we build this?” You ask, “Is this the cheapest and most sensible way to get the outcome?” 

In the current wave of agents and multi-agent systems, a simple set of checks can help keep work aligned with outcomes and avoids unnecessary spend. Ask your team these four questions before approving your next AI Agent project: 

1. Are we choosing the right approach – automation, agent, or multi-agent? 
2. Are we asking a non-autonomous tool for autonomy? 
3. Are we trying to implement unsupported patterns? 
4. And is the architecture going to lower or raise our cost of change? 

Continue reading to delve into the detail of why asking the above questions is so important. 

 

Automation, single agent, or multi-agent – choose the right approach

The first leak in the budget is teams reaching for “agents” where a simple deterministic flow would do. 

Consider the following to help choose an approach: 

• If the task always follows the same steps and never needs human judgement, use automation. 

• If the process needs interpretation of messy input (emails, notes, documents) and then predictable steps, use a single agent with tools. 

• Reserve multi-agent designs for genuinely complex work – distinct domains, different data, or differing owners that must coordinate. 

If you don’t enforce this distinction, you will pay agent prices for automation outcomes. 

 

Intelligent, autonomous, agentic – know what you’re actually buying 

There’s a difference between an “agent” in the industry buzz and an agent in architecture. 

A true agent should be: 

• Intelligent: interprets open-ended natural language goals. 

• Agentic: can take actions through tools towards those goals. 

• Autonomous: runs in a loop, planning and replanning until it decides it’s done. 

Many low-code AI Agents (including Copilot Studio) are intelligent and agentic, but not truly autonomous. They don’t own their own long-running loop and plan – they are more “one‑shot” request–response systems, even when routed through tools or sub-agents. 

That matters for budget: don’t fund “autonomous agents” on a platform that can’t do autonomy. If you need genuine open-ended goal-seeking, you should be steering teams towards a different class of tooling, such as Microsoft’s Agent Framework or other code‑first tooling that supports genuine autonomy, not asking Copilot Studio to be something it isn’t. 

 

Multi-agent patterns – avoid paying for the impossible

Vendors publish design patterns for multi-agent systems: sequential, concurrent, group planning, and so on. They read well on a slide. Many are not yet achievable in low-code agent builders. 

If you’re sponsoring the work, start with whether the platform can support the pattern at all before deciding which one makes sense to apply. 

If the answer is “not yet”, then adjust the scope or move to a platform that can support it. This keeps investment focused on work that can actually deliver the outcomes you’ve approved. 

 

Sub-agents and connected agents – architecture is a cost line

Even when working within the scope of a tool like Copilot Studio, architecture still drives cost. 

Used well, connected / child agents give you separation of concerns, reuse and clearer ownership. Used badly, you end up with one giant, fragile “god-agent” that nobody wants to touch, so every change becomes expensive. This is simply applying the same separation‑of‑concerns and decoupling principles that keep any system maintainable over time. 

This shows up directly in the budget for you – good sub-agent architecture lowers change cost and reduces the likelihood of full rewrites in year two. 

 

Before you sign off 

As a reminder, a simple set of checks can help keep work aligned with outcomes and avoids unnecessary spend. Make a note of these four questions to ask before approving your next AI Agent project: 

1. Are we choosing the right approach – automation, agent, or multi-agent? 
2. Are we asking a non-autonomous tool for autonomy? 
3. Are we trying to implement unsupported patterns? 
4. And is the architecture going to lower or raise our cost of change? 

Clear answers protect you from wasted effort, rework, and spend – they ensure you fund work that moves the organisation forward rather than sideways. 

If you want to reduce waste in AI Agent projects, start by having your teams map one current initiative against these four decisions. It will show you immediately where cost, scope, or expectations need correction before more budget is committed. 

 

Let’s talk 

If you are finding it challenging to identify what you need, speak to us at Marra. We can help you assess where agents make sense, where they don’t, and how to structure the work so your investment lands well. If you’d like to know more about our views and experience with AI and Copilot Studio then visit our AI Hub. 

Written by Ryan Grey, Chief Technology Officer