Meet Your New (AI) Employee

AI agents are entering the workforce. Not as human replacements, but as digital collaborators that observe, reason, and act within defined parameters. Yet too often, organisations deploy these systems without properly defining their role, expectations, or oversight – treating them more like magic than a member of the team. For organisations to realise meaningful value, leaders must begin treating AI agents like they would a new employee: with defined roles, observable responsibilities, and a structured path to growth.

Over the past year we’ve worked with organisations experimenting with agents in various forms. And one of the simplest, yet most powerful mental models we use is this: think of your AI agent like a graduate. Much like a junior team member, an AI agent needs guidance, supervision, and time to build trust. Without these foundations, even the most capable agent risks becoming either underutilised or misaligned.

Rethink AI as Part of the Workforce

In many businesses, AI is deployed with high expectations but minimal structure. This creates a gap between what the system could do and what it should be doing. Drawing a parallel to human workforce onboarding helps bridge that gap. It clarifies how agents contribute, what they need to succeed, and how we govern their work.

This framing creates a shared understanding between business and technology leaders. It also reinforces the importance of gradual responsibility. AI, like any team member, needs to earn autonomy.

The Role of an AI Agent in Modern Architecture

At a technical level, an AI agent is a system that can observe events, reason based on logic or learned models, and take meaningful action. Their power lies in their ability to operate autonomously within a defined scope. 

As this capability grows, we see a shift from monolithic AI implementations to distributed, collaborative ecosystems of agents – each playing a discrete role within a broader enterprise solution. This is the foundation of agent-oriented architecture. Each agent is responsible for a discrete role, and communicates through events within the environment. This design pattern, combined with loose coupling, supports scalability, change resilience, and better governance.

The Graduate Analogy: Framing AI Maturity

When a graduate joins an organisation, they are bright, ambitious, and full of potential – but they lack context. We don’t give them the keys to critical systems on day one. We assign a mentor, we set expectations, and we monitor their work closely.

AI agents should follow a similar progression. Start with basic tasks, monitor performance, and slowly increase their scope of responsibility as they prove their reliability and alignment with business goals. Their early phase is not just about task execution – it’s about building a foundation of trust, observability, and performance feedback.

This structured approach supports responsible innovation, and reinforces the importance of ethical guardrails and governance. Rather than jumping straight to high-stakes AI automation, you build maturity incrementally to ensure their outputs remain useful, safe, and relevant.

Defining the Job Description of an AI Agent

Every AI agent should have a clearly defined role. A good agent “job description” includes:

• Purpose: What business problem or outcome is this agent supporting?
• Scope: What tasks is the agent responsible for? Where are the boundaries?
• Inputs: What data, prompts, or events trigger the agent’s activity?
• Outputs: What does the agent do in response?
• Constraints: What are the ethical, compliance, exceptions or business rules that shape its behaviour?

Without a well-defined scope, AI agents are likely to underperform, drift from objectives, or introduce risk. Just as we wouldn’t give an intern the same responsibilities as a senior executive, we shouldn’t expect a fresh agent to perform high-risk actions without checks in place. This clarity helps align expectations and guide technical design.

Oversight and Responsibility: The Role of the Human Manager

Even the most capable AI agent needs a manager – someone accountable for:

• Monitoring its performance and outputs
• Adjusting its data, rules, or configuration as business needs change
• Reviewing how its actions affect other systems and users
• Ensuring the agent’s actions remain compliant and ethical
• Escalating or removing the agent if it becomes unreliable

In agent-oriented architecture, this human oversight is critical. It supports transparency, ensures compliance, and provides a feedback loop for continuous improvement. Oversight isn’t a limitation – it’s a design principle. Observability is key to maintaining trust, especially as agents grow more autonomous.

Promotion Criteria

Like any graduate, not every agent is ready for promotion. But with consistent performance, agents can move from simple task automation to orchestrating workflows or making guided decisions.

Some criteria to assess readiness include:

• Consistent performance across a representative range of tasks
• Ability to handle complexity or exceptions with minimal error
• Smooth collaboration with other agents or systems
• Clear evidence of business value and trustworthiness

From here, agents may progress to higher-level roles – like orchestrating other agents, driving proactive decisions, or managing domain-specific processes.

Why This Maturity Model Matters

Framing AI agents within a maturity model makes AI more tangible and manageable. It also gives business and IT teams a common way to discuss capability, risk, and performance.

This approach allows for structured AI scaling. Rather than investing in large, monolithic AI programs, you evolve capability one agent at a time, with built-in feedback and course correction.

Conversely, when leaders frame AI adoption through familiar workforce structures, it becomes easier to:

• Communicate expectations across teams
• Gain buy-in from stakeholders
• Track ROI and maturity
• Govern AI systems effectively at scale

Build Your Team with Intention

AI agents are a powerful addition to your business. And while they don’t require coffee breaks or onboarding lunches, they do need structure, support, and oversight to thrive.

By thinking of them like graduates (eager but inexperienced) you can shape their contributions responsibly and strategically. Define their roles, supervise their early work and reward them with more autonomy as they prove themselves. You can unlock their potential while keeping your organisation safe and aligned – that’s what responsible, strategic AI adoption looks like.

But roles and responsibilities are only part of the story. To get the best from your AI agents, you also need to treat them as team members – integrated into your workflows, collaborating with people, and contributing to shared outcomes. This means designing for meaningful interaction. Consider how agents will hand over tasks to humans, escalate decisions, or share insights across the organisation. Like any good teammate, an AI agent should be transparent, dependable, and easy to work with.

When done well, this human-AI collaboration doesn’t just streamline processes, it enhances them. It builds trust between people and technology, increases adoption, and delivers better business outcomes. Strategic AI adoption isn’t about replacing the workforce. It’s about building one that’s augmented, resilient, and fit for the future.