There is a mental model that has served organisations well for decades when it comes to building and deploying business software.

Define the requirements. Design the solution. Build, test, and release. Hand over to a support team. Move on to the next project.

This model works because traditional software is predictable. Once deployed, it does what it was designed to do. It may need occasional fixes or enhancements, but it does not demand day-to-day attention. Most of the work is required upfront, but the rewards remain consistent over time.

The reason is simple. AI agents are not projects with an end date. They operate across a lifecycle that continues well beyond initial deployment.

An agent is not a static artefact. Once deployed, it cannot be assumed to remain stable in the same way as a traditional application or automated workflow.

Traditional software executes fixed logic. An AI agent reasons based on probability. It interprets inputs, draws on knowledge sources, and produces outputs shaped by context. That reasoning process is influenced by things that change over time, and that is where the operational challenge begins.

The difference goes deeper than behaviour. Traditional software is deterministic. Give it the same input and it produces the same output, every time. An AI agent is generative and probabilistic. Every response is produced in the moment, shaped by context, the knowledge available, and the patterns in the underlying model. Two identical questions can produce different but equally valid responses.

This is part of what makes agents useful. They can reason, adapt, and respond to nuance in ways that fixed-rule systems cannot. But it also means you cannot test an agent the way you test traditional software. There is no single correct output to check against. What you can do is test whether responses are accurate, safe, and useful across a range of real scenarios, and repeat that testing whenever something changes.

A more useful mental model is to stop thinking about AI agents as software at all. Think of them as a new kind of team member, not because they are human, but because they require the same kind of ongoing oversight and role clarity.

Most agents are grounded in organisational content. Documents, policies, procedures, product information. These are the knowledge sources an agent draws on to answer questions, complete tasks, and support decisions.

When an agent’s knowledge sources change, the agent’s outputs can change with them, sometimes subtly, sometimes significantly. Without someone actively monitoring those changes and validating that the agent continues to respond accurately, the agent gradually drifts away from being useful.

Organisations deploying AI agents need to be able to answer a few basic questions:

• Who owns this agent?

• What version is running in production?

• When were the knowledge sources last reviewed?

These are not technology questions. They are governance questions.

AI models change. Microsoft updates the language models that power Copilot and Copilot Studio agents on a regular basis. New models can improve reasoning and expand what an agent can do. But moving to a new model is not always a straightforward decision.

• Test the agent’s behaviour against real scenarios from the business

• Validate that outputs remain accurate, appropriate, and safe

• Confirm the update does not negatively affect tone, reliability, or outcomes for users

Working with Copilot Studio and other 1st party Microsoft AI business solutions (like Dynamics 365 Sales agents) introduces another operational consideration. Microsoft releases updates frequently. New features arrive, existing capabilities are extended, and new experiences are introduced, often as preview features first.

• What Microsoft is releasing

• Which updates are relevant to your deployed agents and Copilot configuration

• Which changes could affect user experience or business outcomes

   

Without that awareness, teams tend to run into two predictable issues: they miss improvements that would genuinely help their people, or they adopt changes before they have been assessed against their own environment and risk profile.

A recurring pattern with Microsoft AI business solutions is that valuable new capabilities often arrive as preview features first.

• How do we evaluate features that are in preview?

• Who is responsible for testing them against our environment and data?

• What criteria determine whether a preview feature is safe and valuable enough to deploy?

• Who signs off on moving a feature from evaluation to production use?

A Gartner report predicts that over 40% of agentic AI projects will be cancelled by end of 2027 due to escalating costs, unclear business value, and inadequate risk controls. In many cases, the failure mode will be operational: the agent works, but no one has been resourced to keep it working well.

AI agents are what Galileo’s research calls “living systems.” They require ongoing stewardship, not just initial deployment. That stewardship includes four things:

What it is:

The Copilot Studio Kit is a free toolkit from Microsoft’s Power Customer Advisory Team (Power CAT) that helps teams run agents with more consistency across environments.

What it enables:

It adds governance and operations support such as agent inventory, batch testing (including rubrics you define for grading generative answers), and a Compliance Hub to flag higher-risk configurations and track reviews and remediation. It also captures conversation KPIs in Dataverse and includes utilities such as SharePoint synchronisation to keep knowledge sources current.

What it is:

The shift described here is not primarily technical. Most organisations can build an agent. The harder question is whether they are set up to run one well over time.