What are AI agents?

AI agents are artificial intelligence-based applications that make decisions and perform tasks independently with minimal human supervision. Supported by advanced models, agents can decide a course of action and employ multiple software tools to execute. Their ability to reason, plan, and act allows agents to tackle a wide range of situations otherwise impractical or impossible to automate with preconfigured rules and logic.

This technology is transforming many modern amenities—from simple virtual assistants that respond to users with stock responses to self-driving vehicles navigating through traffic. With recent innovations in generative AI, today’s agents adopt even more challenging and dynamic roles with greater expertise. Multiple AI agents can also work together and coordinate with many users.

All agents act on a sliding scale of flexibility. Rule-based AI agents with no or limited memory represent the most rigid forms, performing tasks based on preset conditions. The most autonomous AI agents can tackle irregular, multistep problems and find effective solutions. They can also self-correct errors and adapt to new information.

These advanced abilities allow AI agents to automate complex business functions, making their potential use cases expansive. Through multi-agent systems, teams of AI agents collaborate across different departments and organisations. Companies can also build their own agents to fulfil their unique business processes and goals.

How do AI agents operate?

While ranging in complexity, intelligent agents are built following four core design patterns that allow them to adapt to diverse scenarios. Let’s break down these central agentic AI capabilities and follow how one advanced agent uses them to tackle a complex procurement order.

Design a scheme

To identify the steps needed to complete assigned tasks, AI agents use highly advanced, large-scale AI models called frontier models. This allows agents to adjust their course of action and create new workflows instead of strictly following predefined paths.

Example:  The user asks the AI agent to choose a third-party supplier that best matches company priorities such as cost-effectiveness. In response, the AI agent constructs a bespoke agentic workflow to find the best supplier. Steps include researching company selection criteria, identifying qualified suppliers, and soliciting and evaluating tenders to make a recommendation.

Use software tools

AI agents combine different tools to execute their plans. Common tools allow agents to collect and analyse data, perform calculations, and create and run new code. Application programming interfaces (APIs) streamline communication with other software, so agents can perform tasks within business systems. Large language models (LLMs)—a type of generative AI that interprets and creates computer code and natural language text—also allow agents to communicate conversationally with users. This intuitive interaction helps users easily review agents’ work.

Example:  The AI agent uses document and web search tools to scan supplier information scattered through company e-mails, PDF files, databases, and websites. Coding and calculator tools help the agent compare and choose between different supplier quotations and payment terms. Within minutes, the agent generates a detailed written report recommending a third-party supplier.

Reflect on performance

Using LLMs as reasoning engines, AI agents improve their performance by repeatedly self-evaluating and correcting their output. Multi-agent systems assess their performance through feedback mechanisms. Their ample memory also allows agents to store data from past scenarios, building a rich knowledge base to tackle new obstacles. This reflection process allows agents to troubleshoot problems as they arise and identify patterns for future predictions—all without extra programming.

Example:  By self-assessing the results, the AI agent improves its procurement selection quality and accuracy. The agent can also incorporate more decision factors like environmental sustainability.

Collaborate with team members and other agents

Instead of a single do-it-all agent, a network of agents specialised for specific roles can work together in multi-agent systems. This agentic collaboration allows the team of agents to solve complex problems more effectively. AI agents can also coordinate with different users where needed, asking for information or confirmation before proceeding.

Example:  Before submitting an order, the agent prompts the user to review the agentic workflow and approve the final selection. To handle more complex orders, the procurement AI agent can be replaced with multiple specialised agents, such as a purchasing clerk agent or contract manager agent. This multi-agent format helps automate more complex workflows, especially when embedded into the company’s unified data systems and applications.

What are the advantages of AI agents?

Equipped with nuanced reasoning and learning capabilities, autonomous AI agents offer deeper levels of specialisation when compared to other standard solutions. This increased functionality offers many benefits for companies as they expand. When integrated into business workflows, intelligent agents can:

• Increase productivity
  Agentic AI tools save teams time by taking over the constant decisions needed for complex tasks without heavy human intervention, boosting overall efficiency.
• Improve accuracy AI agents can self-examine their output, spotting information gaps and correcting errors. This allows agents to maintain high accuracy levels whilst accelerating multiple processes.
• Increase availability Agents can continue to work behind the scenes, from completing tasks for ongoing projects to troubleshooting customer queries beyond the usual office hours.
• Liberate team responsibilities Through adaptable agentic workflows, AI agents free teams from heavy operational workloads, so they can instead focus on big-picture investments and innovation.
• Save on costs AI agent automation can reduce operational expenses dramatically by removing the costly inefficiencies and errors of manual processes and cross-functional collaboration.
• Break down silos A network of interconnected collaborative agents can reduce the common obstacles of complex processes by streamlining data collection and workflows across different departments.
• Create specialised applications Organisations can create teams of bespoke agents to perform functions unique to their needs, training agents on internal data and workflows to automate custom business processes.
• Scale to changing needs AI agents can easily adapt to increasing volumes of tasks, allowing companies to expand while improving their operational agility and cost efficiency.
• Drive data-enabled decision-making Through data analysis, AI agents can identify patterns within complex datasets and suggest potential insights into future outcomes, empowering companies in their decision-making process.

What types of AI agents are there?

AI agents come in different types that vary in complexity, from simple to sophisticated. By combining them, organisations can create customised multi-agent systems to suit their specific needs. Here are six types of AI agents and how they work best for different scenarios:

Reactive agents

Reactive AI agents follow classic rule-based systems. Also known as reflex agents, they launch into action following users’ prompts, always adhering to preset rules. This approach works best for repetitive tasks. For example, a reactive AI agent can use a chatbot to process common requests such as resetting a password from conversational keywords or phrases.

Reactive agents generally lack substantial memory, which makes them better suited for limited, short-term scenarios. On the plus side, reactive AI agents prove low maintenance, needing minimal programming to function.

Proactive agents

Far more nimble than reactive agents, proactive AI agents use predictive algorithms to drive more nuanced functions. These models identify patterns, forecast probable outcomes, and choose the best course of action without human prompting. These agents can monitor complex systems such as supply chains, proactively identifying issues and recommending solutions.

Hybrid agents

As their name suggests, hybrid systems combine the efficiency of reactive agentic systems with the nuanced discernment of proactive AI agents. The combination offers the best of both worlds. They can react efficiently to routine scenarios following preset rules. They can also observe and respond to more nuanced situations.

Utility-based agents

Utility-based AI agents focus on finding the best possible sequence to achieve a desired outcome. They assess each potential course of action based on user satisfaction metrics, then select the option with the highest marks. Utility-based agents are the driving force behind car navigation systems, robotics, and financial trading.

Learning agents

Learning AI agents can refine their performance based on previous experiences. They use problem generators that create test scenarios to try new strategies, collect data, and evaluate the results. Learning AI agents also track user feedback and behaviour to hone the best approach, improving overall nuance and accuracy over time. Current learning AI agents help create sophisticated virtual assistants that adapt to users’ needs.

Collaborative agents

Collaborative AI agents describe a network of agentic AI systems coordinating together to complete complex tasks across organisational siloes. They can build custom workflows and delegate tasks to other entities, even people and other AI agents.

How do you utilise AI agents?

AI agents readily adapt to diverse use cases. Some agents are role-specific, serving as specialised assistants to individual departments. Others fulfil needs applicable to multiple lines of business—like an agent resolving transaction disputes, whether they originate from the customer service, accounts payable, or supply chain teams. Combined, they work together to solve tasks across the enterprise. Agents can be activated by user interactions or automatically by business events. Although their potential use cases are limitless, here’s how AI agents can cater to different operational needs:

Financial services

• Streamline cash flow management by automating ledger reports, billing, invoicing, receipts, and tax and compliance records
• Automate real-time accounting data documentation, processing, and retrieval, reducing the need for manual input
• Flag invoice disputes, offer recommendations based on internal knowledge sources, and automate resolution processes
• Use predictive analytics to create decision-making insights into budget allocations, credit decisions, revenue opportunities, and risk management

Human resources

• Simplify the recruitment process by generating job requisitions and descriptions, screening candidates, and automating onboarding processes
• Process employee holiday requests by consulting leave balances and policy compliance, determine if prerequisites are met, and submit for managerial approval
• Enhance employee skillsets by creating personalised learning plans, searching through internal and external sources for relevant training courses

IT and development

• Strengthen security by proactively detecting and mitigating potential threats, reducing system vulnerabilities
• Streamline development workflows including code review, automated testing, and continuous integration/continuous deployment

Marketing and commerce

• Analyse consumer data to predict activity, track preferences, and personalise engagement
• Monitor market trends and provide proactive tailored recommendations for potential growth opportunities
• Optimise audience engagement by tracking promotional content in real time, identifying underperforming ads, and proactively designing and running A/B tests

Procurement

• Research and recommend suppliers for specific bids, then develop negotiation strategies by reviewing past work and industry trends
• Automate supplier onboarding, purchase orders, and invoicing
• Predict fulfilment delays, recommend alternative suppliers that fit project requirements and timelines, and reroute production to minimise disruptions

Sales and service

• Proactively detect disputes, validate issues, and select and execute solutions, dramatically reducing waiting times
• Classify customer requests and service tickets, route them to the correct teams, and recommend resolutions for customer service representative to approve
• Produce individualised customer insights to identify and recommend sales opportunities
• Enhance team knowledge base by analysing novel closed cases and producing articles summarising key issues and solutions

Supply chain

• Forecast demand in real time, evaluating stock and delivery logistics to make proactive recommendations
• Adjust deliveries to minimise disruptions, choosing alternative routes that meet specific company goals like lower transportation costs and environmental footprints
• Enhance quality control by simplifying the inspection process, identifying errors in manufacturing, transportation, and storage
• Troubleshoot production outages by ordering repair parts, requesting maintenance services, and redirecting production to alternative equipment

What’s the best way to implement AI agents in the workplace?

The potential applications of autonomous AI agents are broad in scope. To achieve their full promise, however, agents work best with thoughtful integration and coordination. Consider these best practices before incorporating agent AI systems.

• Follow AI ethical principles
  Humans are ultimately responsible for creating ethical AI agents, maintaining the highest standards of fairness, transparency, accountability, and privacy. To achieve this, responsible AI procedures should follow a human-in-the-loop (HITL) design process, where humans monitor every stage of development and use. Data used for agent training should be carefully analysed to mitigate potential bias and discrimination.
• Emphasise human oversight
  Expert individuals should still have final authority over the agent AI decision-making process. They should establish the agents’ level of autonomy and require final approval before agents complete sensitive tasks. Human experts can also troubleshoot issues by reviewing agentic workflows for logical errors or missing essential data.
• Prepare internal data The performance of AI agents depends largely on a solid foundation of quality business data. Agents need access to a complete and context-rich data ecosystem to ground their decisions and actions. To get the most out of agentic AI, users can invest in management solutions that unify and govern data across their systems.
• Encourage a collaborative mindset
  AI agents only work if team members know how to use agentic autonomy effectively. Teams should carefully consider where AI agent automation can relieve operational obstacles to ease work responsibilities.
• Support ongoing training
  As AI agent technology evolves, organisations should prioritise continuous training. Regular educational sessions can help teams stay updated on the latest innovations, applications, and best practices.
• Measure and evaluate
  Organisations should regularly evaluate their AI agents’ overall efficiency and productivity. The review process should include monitoring feedback from both employees and customers. Regular evaluations can provide insights into possible areas for improvement and optimisation.

What’s the difference between AI agents and AI co-pilots?

At first glance, AI agents seem to overlap with a popular AI-based technology—AI co-pilots. Often integrated into everyday work applications, AI copilots are personal virtual AI assistants that work alongside users to support their business tasks using data and computation. In practical terms, however, both tools fulfil different operational functions and needs. When combined into multi-agent systems, their skills can complement each other, nurturing insightful decision-making and collaboration. Here’s how co-pilots and agents can work together to solve challenges and boost enterprise-wide productivity:

• Intuitive interaction and customisation
  Backed by conversational AI, copilots act as intuitive interfaces for AI agents and users to collaborate. Users can manage agents with natural human expression, all directly through copilots embedded inside their core business applications. Copilots also offer guided low-code or no-code platforms for building and scaling bespoke intelligent agents. They provide guided workflows to define the tools, data sources, and rules the agent needs to carry out.
• Collaborative partnership
  Deeply integrated into business data and operations, AI copilots and agents work together to complete tasks. Copilots can act as agent orchestrators, deciding which agents are needed to fulfil users’ requests. Embedded across different department applications, copilots also connect agents in collaborative networks, so they work together rather than in isolation.
• Dynamic functionality
  Some tasks benefit from total automation, whilst others need step-by-step human involvement. Working together in harmony, AI co-pilots and agents serve both scenarios. Copilots offer real-time assistance as users work—sourcing and summarising information, answering business questions, producing insights for decision-making, and recommending solutions. Agents cater to both needs. They can collaborate closely with users to gather more information or approve actions that affect business processes. They can also operate autonomously as stand-alone entities, resolving issues in the background without needing constant input.

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