What are AI agents and how do they improve productivity at work?

In the following sections we’ll explain how this looks like in action:

1- Perception: Understanding the world 

Agents are focused on “seeing” and “hearing” the world around them. They use Natural Language Processing (NLP) to understand text and speech, computer vision to interpret imagery and videos, and sensors to process physical inputs.

Where HITL may be baked in: Humans, at this stage, may be looped in to confirm whether clause extraction or anomaly detection is correct.

2- Reasoning: Making sense of it all 

Once the data is collected, agents process and interpret it ready to extract what’s important and generate outputs, transforming raw data into actions. 

Where HITL may be baked in: Humans may be looped in to validate recommendations before action is taken.

3- Action: Turning thoughts into results 

After reasoning, agents springs into action. This might include executing a task, such as auto generating a report, sending an alert or triggering a workflow. 

Where HITL may be baked in: Humans may supervise or approve final actions at this stage, or some actions may require a human to manually trigger it. 

4- Learning: Getting smarter over time 

Finally, using reinforcement learning, AI agents learn what actions led to rewards or penalties, evolving their practices accordingly. In essence, they adapt to unique workflows, styles and processes, ensuring continual improvement.

Where HITL may be baked in: Humans become trainers or feedback providers, ensuring all stages of work that AI produces are improving, without bias.

Types of AI agents

AI agents can be categorised in many ways based on their behaviour, capabilities, roles, and environments. Classifying agents by type helps us understand which agents could be most suitable for our needs. See below for some examples.

1- Simple reflex agents 

Agents that act solely based on current inputs, without considering past or future consequences. They respond to specific conditions using pre-defined rules.

Advantages: Good for high-volume, low-risk, repetitive tasks.

Disadvantages: No memory, no goals, not very adaptable, just rules-based.

Example: "If a staff member clocks in late three times, send a manager alert.”

2- Model-based agents 

Agents that maintain an internal model of their environment (for example, your workflow) but understand past states (memory) and inform their current decisions.

Advantages: Good for workflows with historical dependencies or state tracking.

Disadvantages: They’re still rules-based, not learning.

Example: "Based on historical data around your budget, here’s your forecast.”

3- Goal-based agents 

Agents that achieve specific objectives by strategically planning and executing actions that move them closer to their desired goals.

Advantages: Good for outcome-driven workflows where flexibility is needed.

Disadvantages: Can misfire if goals are misconfigured.

Example: “Here’s your next-best action to hit your service KPIs”.

4- Utility-based agents 

Agents that maximise value by evaluating multiple criteria and selecting the best course of action based on predefined conditions like time, efficiency, or cost.

Advantages: They choose actions based on value or preference.

Disadvantages: They’re more complex and require trust in the system’s trade-offs.

Example: “Based on staff preferences, costs, and patient needs, here’s how I would allocate your team’s resources this month”.

5- Learning agents 

Agents that improve their performance over time by learning from interactions with their environment and historical data. 

Advantages: Continuous learning, pattern recognition, predictive modelling,

Disadvantages: Needs strong HITL and data governance.

Example: "I suggest this particular treatment plan based on your patient’s outcomes so far.”

Examples of OneAdvanced AI agents

Below are some examples from our newly launched AI Agent marketplace. These examples belong to the healthcare sector.

AI Clinical Coding Agent: Using a predefined understanding of clinical workflows, diagnoses, and coding systems, this agent leverages machine learning to analyse patient records and turn them into official industry code, categories and formats.

AI Clinical Summarisation Agent: By analysing patient records, prioritised based on urgency, this agent summarises critical details like medications, diagnoses, and recommended next steps, helping GPs and healthcare providers save time, reduce administrative burdens, and make informed decisions.  

Here’s what customers have to say about these: 

“It's probably sped up our processes by cutting admin time in half – I’d be amazed if it wasn’t more!” – Dee Turner, Practice Business Manager, New Islington Medical Practice

“My team's welfare is of utmost importance. I have seen first-hand their positivity and the improvements the platform has made to streamlining their way of working. The new capabilities have definitely contributed to improving morale in the team. The coders feel valued, with requested development listened to, there is a good support offering from the OneAdvanced project team.” – Cheryl Flude, Operations Manager, Red Roofs

Learn more about our recently launched AI agents here.

Benefits of using AI agents

PwC found that 79% of companies are already adopting AI agents, and 66% of them are reporting measurable value through increased productivity. 

When seamlessly integrated into workflows, AI agents have the potential to unlock powerful benefits, including: 

1- Efficiency 

AI agents can handle several tasks simultaneously such as researching, cross-referencing and updating records. This speeds up processes and can address delays caused by handoffs between teams.

2- Consistency

Humans vary – AI doesn’t (unless we want it to). Agents apply the same logic, standards, and formatting every single time. Whether processing data, drafting reports, or following compliance protocols, they operate without subjective interpretation. In critical sectors like healthcare, legal, and government, consistency is key to ensure trust, safety, and continuity.

3- Testing

Because AI agents operate quickly and without fatigue, they can simulate multiple scenarios, identify bottlenecks and recommend optimisation strategies, removing a lot of the initial guesswork. This rapid, low-cost testing cycle allows us to fine-tune strategies before putting a whole team behind them.

4- Collaboration

AI agents act as a connective layer between teams, systems, and even geographies; sharing updates in real time, moving information between platforms, and making sure everyone works from the same, up-to-date data.

Limitations of AI agents

1- Trust

Over half (54%) of organisations are still cautious about trusting AI. This is mainly due to ethical implications such as privacy concerns, bias, accountability, and transparency. Without confidence, there is reluctant to rely on AI systems. 

We must find ways to balance efficiency gains with the need to prove how and why an agent makes a particular decision.  

2- Tech readiness

AI agents aren’t plug-and-play for most organisations. They require stable infrastructure, clean data, API integrations, and sometimes even upgrades to legacy systems. A report by Capgemini found 80% of companies lack mature AI infrastructure for effective AI implementation, and only one in five organisations have high levels of data readiness. 

We must invest in the right foundations for AI agents to function. Quick patchwork deployments lead to inconsistencies that drive frustrations.

3- Human touch

While AI agents are excellent at providing quick and accurate responses for some type of tasks, they lack the emotional intelligence of humans, which are essentials for other types of tasks. This limitation becomes evident where nuanced judgement is required, like for certain customer service tasks. It’s important to identify the right use cases for AI agents and have them support teams, not replace them.

We must find the right balance between human and AI involvement. Too much AI involvement risks damaging relationships and trust.

4- AI knowledge gap

Only half of organisations report having sufficient knowledge about AI agents. Without a clear understanding it's hard to fully harness their potential. This can lead to underusage (treating them like chatbots) or over-trusting (assuming all their outputs are correct).

Bridging AI knowledge gaps is critical to realise the full ROI of AI agents, avoiding costly mistakes that could impact our trust, quality, and output.

Check out our latest article “Understanding and managing AI risks” for more information around how to manage AI risks effectively.

How to start using AI agents?

Unlocking the growing potential of AI agents requires more than just adoption – it requires a deliberate, strategic approach to their implementation. One that starts by defining the problems we want to solve, and the agents that can help us solve it. 

On top of this foundation, we must ensure we: 

• Adhere to ethical principles: Ensuring fairness, privacy, transparency, and accountability are baked into agent design and deployment.
• Prioritise human oversight: Keeping humans in the loop to guide outputs, make critical decisions, and balance control. This minimises risks.
• Maintain quality data: Input data that is clean, diverse, relevant, and well-organised to optimise AI performance and reduce errors or biases.
• Promote collaboration: Using AI agents as a connective layer between teams, systems, and geographies; to share updates in real-time, not to replace us.
• Educate teams: Without a clear understanding of how AI works, its advantages, and potential challenges, it's hard to fully harness AI’s real potential.
• Monitor impact: Tracking key metrics such as accuracy, efficiency, and user satisfaction to ensure AI agents deliver tangible business value.

How can OneAdvanced AI agents help?

OneAdvanced’s AI Agents are designed to support organisations across critical sectors like healthcare, legal, and government. They are embedded into workflows to automate repetitive tasks, simplify complex decisions, analyse large internal datasets, and summarise documents. By optimising processes to support organisational goals, they can help us deliver impact.

Ready to take the leap? Visit OneAdvanced’s AI agent marketplace today.

Frequently asked questions (FAQs) 

What is the difference between AI agents and AI assistants? 

AI agents can work independently, making decisions and taking actions with minimal human input. AI assistants, on the other hand, are reactive and rely on user instructions to perform tasks. 

Are AI agents the future of business operations? 

No. While today’s AI agents, semi-automated tools, are transforming businesses by streamlining tasks and improving decisions, the future, however, is in fully autonomous systems that work without human interventions.  

Can AI agents work independently? 

Yes, AI agents can work independently to some extent. They’re designed to perform tasks and make decisions with minimal human intervention, often functioning independently to achieve defined goals.  

How do you build or deploy an AI agent? 

Building and deploying an AI Agent involves defining its objectives, refining data repository, selecting a platform, integrating AI capabilities, and testing the performance. To dive deeper into the process, explore: What are AI agents and how do they improve productivity at work?