AI agents are artificial intelligence-based applications that make decisions and perform tasks independently with minimal human oversight. Backed by advanced models, agents can decide a course of action and employ multiple software tools to execute. Their ability to reason, plan, and act lets agents 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.
How do AI agents work?
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 plan
To identify the steps needed to complete assigned tasks, AI agents use highly advanced, large-scale AI models called frontier models. This lets agents 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 like cost effectiveness. In response, the AI agent builds a custom agentic workflow to find the best supplier. Steps include researching company selection criteria, identifying qualified suppliers, and soliciting and evaluating bids to make a recommendation.
Use software tools
AI agents combine different tools to carry out their plans. Common tools allow agents to collect and analyze 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 let agents 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 specialized 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 specialized 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 benefits of AI agents?
Equipped with nuanced reasoning and learning capabilities, autonomous AI agents offer deeper levels of specialization when compared to other standard solutions. This increased functionality offers many benefits for companies as they grow. When integrated into business workflows, intelligent agents can:
- Increase productivityAgentic AI tools save teams time by taking over the constant decisions needed for complex tasks without heavy human intervention, boosting overall efficiency.
- Improve accuracyAI agents can self-examine their output, spotting information gaps and correcting errors. This allows agents to maintain high accuracy levels while accelerating multiple processes.
- Expand availabilityAgents can continue to work behind the scenes, from completing tasks for ongoing projects to troubleshooting customer questions beyond the usual office hours.
- Liberate team responsibilitiesThrough adaptable agentic workflows, AI agents free teams from heavy operational workloads, so they can instead focus on big-picture investments and innovation.
- Save on costsAI agent automation can reduce operational expenses dramatically by removing the costly inefficiencies and errors of manual processes and cross-functional collaboration.
- Break down silosA network of interconnected collaborative agents can reduce the common obstacles of complex processes by streamlining data collection and workflows across different departments.
- Create specialized applicationsOrganizations 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 needsAI agents can easily adapt to increasing volumes of tasks, letting companies expand while improving their operational agility and cost efficiency.
- Drive data-enabled decision-makingThrough data analysis, AI agents can identify patterns within complex datasets and suggest potential insights into future outcomes, empowering companies in their decision-making process.
