What are AI agents?

You’ve probably already heard people talking about AI agents, but how do they really stack up against traditional software, and more importantly, how can they be practically implemented in expert workflows? Let’s break down the core differences and explore a realistic, step-by-step approach to building AI agents for professional use.

The fundamental difference lies in how they operate. Traditional software is deterministic. It follows predefined rules and instructions - you tell it exactly what to do, and it does it. AI agents, on the other hand, leverage generative models to make decisions based on their instructions and the data they process. Think of it as “reasoning,” allowing for more flexibility and, ideally, greater efficiency.

While some software might appear agent‑like due to the similar tools it employs, the key distinction is in decision‑making. It’s also crucial to remember that AI agents aren’t always the answer. If a task can be reliably handled by traditional software, sticking with the known and robust solution is often the smarter choice.

Phase 1: Task Efficiency Boost

The initial step is to equip experts with tools that enhance their individual task efficiency. This means providing personal tools to automate and optimize specific parts of their work. Examples include:

Custom prompts and templates for language model-based document generation. This helps standardize outputs and speed up drafting.
Building document templates and rule libraries. Creating reusable components ensures consistency and reduces repetitive work.
Traditional programming can still play a role in automating smaller, well-defined sub-tasks within this phase.

Crucially, in this phase, the expert remains in control, directing the AI and using it to improve their personal productivity. This stage is vital for identifying and making visible the different stages (tasks) and decision points within the overall workflow.

Phase 2: Workflow Modeling and Decision Support

Once individual task efficiency is improved, the next step is to pinpoint key decision points in the workflow where AI can assist or partially automate decisions. Here, AI agents can:

Guide workflow progression by suggesting the next steps based on the current state.
Offer recommendations to support expert decision-making, providing data-driven insights.
Combine and analyze information from various sources, leading to faster and more accurate decisions.

In this phase, AI agents become more active participants in the workflow, but the expert still holds the final decision-making authority. The focus is on building agent reliability and refining their performance through feedback. If an expert consistently accepts agent outputs without corrections, it signals readiness to consider moving to Phase 3 for that specific workflow.

Phase 3: Towards Full Automation and a New Expert Role

In the final phase, the workflow is sufficiently modeled, and AI agents are advanced enough to automate the majority of decision-making. The expert’s role transitions to system oversight and development:

AI agents operate independently, making decisions based on modeled rules and historical data. Robust automated checks and reporting are integrated to ensure reliability and transparency.
Experts monitor system performance, intervening only in exceptional cases. Development efforts shift to managing and improving the agent portfolio, and creating new agents for different tasks.

This phase unlocks the scalability of expert work. Experts move away from routine tasks and focus on strategically developing and managing the system that handles those tasks.

Conclusion

Building effective AI agents in expert work is an evolutionary journey, not a one-time project. It begins with optimizing individual tasks, progresses to AI-supported decision-making, and can ultimately lead to a fully automated system. This evolution fundamentally changes the expert’s role from operational execution to strategic system development.

Our experience shows that different workflows progress at varying speeds. Some may rapidly reach near-full automation (sometimes entirely without agent-like features), while others might remain in an assistant phase for extended periods. Some workflows, due to their low repeatability and diverse sub-tasks, may prove extremely challenging to automate, at least for now. Understanding this phased approach and the nature of your specific workflows is key to successfully integrating AI agents in a practical and impactful way.

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