Introduction

When most people think of artificial intelligence, they picture tools like chatbots or systems that respond to specific prompts. In short, AI that generates text, answers questions, or provides recommendations. While impressive, these systems are often limited to single, isolated tasks.

AI agents, on the other hand, go a step further. They’re designed to handle multi-step processes autonomously, coordinating between different tools and systems to achieve a goal. Instead of responding to a single request, AI agents can perform complex workflows, automating processes that traditionally require significant human effort.

As an example let’s say you are planning a business event in a new city. You have a long to-do list: find a venue, check availability, invite the right attendees, and create a detailed agenda. Typically, this means hours of research, emails, and coordination. But what if most of this could be automated?

With AI agents, it can. You provide a few key details, such as the event location, dates, expected number of attendees, and the topic. From there, the system springs into action:

• It searches the internet for potential venues, filters them by availability, and compiles a list for you to review (or it picks the best one if you prefer).

• It queries your CRM to identify relevant contacts and drafts and sends personalized invitations.

• Once attendees confirm, it generates and sends a tailored agenda for the event.

This is the power of AI agents. They streamline operations, saving time and resources while reducing the risk of human error.

In this post, we will explore what AI agents are in more detail, how they work, and how businesses can use them to automate workflows, boost efficiency, and drive growth. By the end, you will see how these systems can transform tasks like event planning and beyond.

Understanding AI Agents: Key Concepts and System Structure

Let’s begin by seeing how agentic systems differ from traditional AI applications. While traditional systems often rely on a single model to complete an entire task, agentic systems leverage multiple specialized agents, each focused on a specific part of the process. This division of labor, sometimes called role-playing, enhances overall system efficiency and effectiveness [1].

An agentic system typically consists of the following building blocks.

Building Blocks

Agent: An agent is an autonomous program designed to perform specific tasks. It gathers information, makes decisions, and executes actions to achieve a goal. For instance, one agent might search the internet for venues, while another drafts invitation emails. Agents are the building blocks of agentic systems.

Task: A task is a clearly defined action executed by an agent, such as querying a database, filtering search results, or sending notifications. Tasks can be executed in sequence or in parallel. Tasks often involve interacting with external tools, like search engines, APIs, or databases, to complete their objectives [2].

Tools: Tools extend an agent’s capabilities by providing access to external systems, such as search engines, data repositories, or specialized software. For example, a document analysis agent might use a natural language processing library, while a customer support agent could interface with a CRM system. Tools are essential for enabling agents to handle a broad range of tasks effectively.

Manager: The manager acts as the system's coordinator, orchestrating agents and their tasks. It may define the sequence of operations, manages dependencies, and ensures smooth workflow execution.

Shared Memory: Agentic systems often incorporate shared memory to enable agents to collaborate and build upon previous outcomes. This allows agents to avoid repetitive mistakes and improve over time, enhancing system performance.

Connecting the Building Blocks

Agents don’t operate in isolation, they collaborate within specific structural configurations:

1. Hierarchical/Centralized: A central manager oversees agents, organizing workflows in a top-down manner.

2. Distributed: Agents work independently but communicate and coordinate directly with each other. This means an agent can delegate to another agent.

3. Hybrid: A combination of hierarchical and distributed structures, providing flexibility to adapt to different workflows.

In distributed and hybrid setups, agents can autonomously plan multi-step workflows, determining which agent to activate next [3]. Research on multi-agent systems highlights how these configurations model and optimize workflows based on real-world processes [4].

Integrating with External Tools

To perform their tasks effectively, agents often leverage external tools, such as APIs, web search engines, or local databases. These tools extend the agents’ capabilities, allowing them to retrieve, analyze, and act on information from diverse sources. For example, a scheduling agent might use a calendar API to coordinate events, while a research agent could query legal databases for relevant case law. By integrating with external systems, agents can handle a wider range of tasks and deliver more impactful results [2].

By combining these components, agents, tasks, tools, a manager, and shared memory, agentic systems enable businesses to streamline processes, adapt to new challenges, and achieve significant efficiency gains. In the next section, we will explore practical applications of these systems in real-world scenarios.

Agentic Systems and Business Processes

Before implementing an agentic system, it is crucial to evaluate your existing workflows through value stream analysis. This analysis helps identify value-adding steps versus wasteful ones, ensuring that you don’t automate something that should not exist in the first place. For a deeper dive into this topic, check out my post.

Once processes are streamlined, agentic systems can unlock significant business value, including:

• Cost Reduction: Automating routine tasks reduces overhead and operational expenses.

• Increased Productivity: By handling time-consuming tasks, AI agents free employees to focus on strategic and creative work.

• Improved Accuracy: Automation reduces the risk of human error, enhancing the quality and consistency of outputs.

• Adaptability: Agentic systems quickly adapt to new challenges, such as regulatory changes or shifting business priorities, providing businesses with a competitive edge.

With these benefits in mind, let’s explore a real-world application of agentic systems in the legal industry.

Use Case: Streamlining Legal Case Preparation with AI Agents

Consider a law firm preparing for a major court case. This process traditionally involves extensive research, document review, and regulatory analysis. By adopting an agentic system, much of this routine work can be automated, allowing the legal team to focus on high-value tasks like strategy and argumentation.

Here’s how an AI agent system could streamline case preparation:

1. Legal Research Agent

   • Task: Search legal databases for relevant case law and precedents.
   • Action: Query public and subscription-based legal repositories, extract applicable cases, and summarize their relevance.

2. Document Analysis Agent

   • Task: Analyze internal case files and client-provided documents.
   • Action: Scan for key clauses, discrepancies, or missing information and flag potential issues for review.

3. Regulation Update Agent

   • Task: Retrieve recent regulatory changes or updates from government APIs.
   • Action: Check for new laws or amendments that might impact the case.

4. Summary and Reporting Agent

   • Task: Compile findings into a comprehensive report.
   • Action: Organize legal precedents, document highlights, and regulatory updates into a clear, concise format for the legal team.

5. Follow-Up Agent

   • Task: Schedule and send reminders for upcoming deadlines or required client inputs.
   • Action: Integrate with the firm’s calendar and email system to ensure timely follow-ups.

System workflow

Figure 1 provides a visual representation of the agentic system at work. The system begins with user input, prompting the Legal Research Agent to search relevant databases and retrieve applicable case law. Once this initial research is complete, the Regulation Update Agent evaluates the findings against recent legislative or regulatory changes, ensuring the information is current and accurate. If updates are required, the system seamlessly integrates them into the records.

Following this, the Document Analysis Agent examines internal files and client-provided documents for missing details, inconsistencies, or potential issues. At this stage, the system dynamically assesses whether further research is necessary. If gaps or ambiguities are detected, the Legal Research Agent is re-engaged to address them. If no additional research is required, the process advances to the Summary and Reporting Agent, which consolidates findings into a comprehensive report for the legal team. Finally, the Follow-Up Agent coordinates deadlines and client inputs by scheduling reminders and sending notifications, ensuring smooth communication and timely task completion. This adaptive, goal-oriented workflow exemplifies the efficiency and precision of agentic systems.

Figure 1: Use case of AI Agents for legal case preparation.

This example demonstrates how an agentic system leverages its internal state to make informed decisions and adapt its workflow to optimize for an external goal. This ability to dynamically adjust and refine processes is what makes agentic systems so powerful and transformative for businesses.

Conclusion

AI agents are transforming how businesses approach complex workflows by automating multi-step processes and improving efficiency. As we saw in the legal case preparation example, these systems can handle tedious, time-consuming tasks, allowing professionals to focus on high-value work like strategy and decision-making. Whether it’s legal, healthcare, or manufacturing, the potential applications are vast, and the benefits are clear: reduced costs, faster turnaround times, and improved accuracy.

However, successfully implementing an AI agent system requires more than just technology. It involves understanding your business processes, identifying high-impact use cases, and ensuring that only valuable tasks are automated. This is where careful process analysis, such as value stream mapping, becomes critical.

If you’re curious about how AI agents could benefit your business, I can help. Whether it’s identifying potential use cases, optimizing existing workflows, or implementing a tailored agentic system, my consulting services are designed to guide you through every step of the process.

Ready to explore the possibilities? Contact me to start your journey toward smarter, more efficient business operations.

References

[1] Kaplan, Jared, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling Laws for Neural Language Models. 2020. https://doi.org/10.48550/arXiv.2001.08361

[2] Schick, Timo, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language Models Can Teach Themselves to Use Tools. 2023. https://doi.org/10.48550/arXiv.2302.04761

[3] Liu, Zhiwei, Weiran Yao, Jianguo Zhang, Le Xue, Shelby Heinecke, Rithesh Murthy, Yihao Feng, et al. BOLAA: Benchmarking and Orchestrating LLM-Augmented Autonomous Agents. 2023. https://doi.org/10.48550/arXiv.2308.05960

[4] Han, Shanshan, Qifan Zhang, Yuhang Yao, Weizhao Jin, Zhaozhuo Xu, and Chaoyang He. LLM Multi-Agent Systems: Challenges and Open Problems. 2024. https://doi.org/10.48550/arXiv.2402.03578