Agent Evaluation: Understanding Agentic Systems and their Quality
This is Part 1 of our Agent Evaluations series. Here is Part 2 in this series
In today’s rapidly advancing world of artificial intelligence (AI), agentic systems are becoming an integral part of numerous industries, powering everything from customer support to robotics. But what exactly are these systems, and why is measuring their quality so critical for businesses and users alike? In this blog post, we will explore the nature of AI agents, their various types, real-world applications, and the importance of evaluating their quality for widespread adoption.
What are Agents?
To define agents, we can turn to Anthropic’s definition of building effective agents:
Systems that can autonomously perform tasks by perceiving their environment, processing the information, and acting upon it to achieve specific objectives.
This definition underscores the ability of agents to adapt and make decisions based on the context, setting them apart from simpler systems that only follow pre-determined instructions.
To understand the architecture of effective agents, it’s essential to consider key components such as tool use, planning, memory, and reasoning:
🛠️ Tool use: Agents can interact with external tools or systems to extend their capabilities. For instance, an AI agent might use a web browser to retrieve information or access a database to fetch relevant data. This interaction allows agents to perform tasks beyond their inherent capabilities.
📝 Planning: Effective agents can formulate plans to achieve specific objectives. This involves setting goals, determining the necessary steps, and executing actions in a sequence that leads to the desired outcome. Planning enables agents to handle complex tasks that require multiple steps and decision points.
🧠 Memory: Agents with memory can retain information from past interactions (long-term memory) or across multiple steps of the interaction (short-term memory). This capability allows them to provide contextually relevant responses, learn from previous encounters, and improve over time.
💭 Reflection: Reflection enables agents to evaluate past actions and outcomes, allowing them to draw inferences and make informed decisions based on available data. This cognitive ability helps agents handle ambiguity, solve problems, and adapt to new situations by learning from previous experiences and adjusting their strategies accordingly.
Some important architectural differences between simple workflows and agentic systems are:
Types of Agents
Agent architectures can be categorized into single-agent and multi-agent systems, each with distinct structures and levels of autonomy.
Single-Agent Architectures
A single-agent system consists of a dynamic entity responsible for perceiving its environment, making decisions, and executing actions to achieve specific goals. This dynamic behavior of these agents can be classified into three tiers:
Basic autonomous: Operates under direct human supervision, executing predefined commands without autonomous decision-making capabilities.
Intermediate autonomous: Performs tasks autonomously within a limited scope, handling simple decision-making processes and adapting to minor environmental changes.
Advanced autonomous: Possesses sophisticated decision-making abilities, allowing it to adapt to dynamic environments, learn from experiences, and perform complex tasks without human intervention. This level of independence is still a subject of ongoing research and development.
Multi-Agent Architectures
Multi-agent systems (MAS) consist of multiple dynamic agents that interact and collaborate to achieve collective objectives. These systems can be structured in two primary ways:
Hierarchical structure: Organized in a tree-like hierarchy with varying levels of autonomy. Higher-level agents oversee and coordinate the activities of subordinate agents, ensuring that tasks are completed efficiently and in alignment with overarching goals.
Heterarchical structure: Agents operate on an equal footing, collaborating and negotiating with each other without a central authority. This structure promotes flexibility and adaptability, as agents can dynamically form alliances and adjust their roles based on the situation.
AI Applications
AI agents are rapidly evolving and still in their early stages, yet they are already beginning to transform industries by streamlining operations, enhancing user experiences, and driving better outcomes. Some of the key areas where AI agents are making an impact include:
🤖 Coding agents: AI-powered coding agents, like Cursor and Copilot, assist with code generation, debugging, and optimization. They provide real-time suggestions, automate repetitive tasks, and enhance developer productivity by reducing errors and speeding up development.
👩💼 Personal assistants: Voice-activated AI agents, such as Google Assistant and Alexa, are widely used for daily tasks and smart home controls.
📞 Customer support: AI-powered chatbots and virtual assistants are revolutionizing customer service, providing 24/7 assistance, handling routine queries, and resolving issues swiftly, thus enhancing customer satisfaction.
✈️ Travel agents: AI-powered virtual assistants that enhance travel by providing personalized recommendations, itinerary planning, reservations, and real-time updates.
Key Reasons for Measuring Quality
Evaluating the quality of AI agents is not just about ensuring they function—it's about maximizing their effectiveness in delivering value to users and organizations alike. Here are some key reasons why measuring agent quality is a priority:
✅ Task completion: The primary goal is to ensure the AI agent effectively helps users complete their intended tasks, prioritizing real-world success over isolated accuracy metrics.
🚀 User experience: High-quality agents provide smooth, fast, and accurate interactions, boosting satisfaction and retention, while poor agents frustrate users and drive them away.
💰 Business impact: Efficient AI agents improve key metrics like response times, resolution rates, and cost savings, directly benefiting business performance.
📏Scalability: Well-designed agents can handle growing user demand without compromising service quality, enabling businesses to scale efficiently.
📈 Long-term viability: Regular evaluation ensures AI agents remain effective, especially in high-stakes industries like healthcare and finance, where errors can be costly.
Common Challenges in Evaluating Agent Quality
Despite the obvious benefits of agent evaluation, there are several challenges that organizations face in ensuring the consistent quality of their agents:
🧩 Real-world complexity: AI agents must function in unpredictable environments, handling diverse user queries, expectations, and contexts. For example, in customer support, an agent may need to handle queries from users with different backgrounds, expectations, and contexts. Evaluating an agent’s performance across such varied scenarios can be complex.
🎯 Long-term adaptability: Performance evolves as agents interact with users and collect data, making it difficult to assess sustained effectiveness.
👥 User-specific variations: Different users have different interaction styles, requiring the agent to adapt dynamically to meet varied needs.
🧠 Non-deterministic, dynamic systems: AI agents exhibit non-deterministic behavior due to their reliance on large language models (LLMs). This means that even with identical inputs, an agent’s decision-making process may produce different results each time. Evaluating performance in such probabilistic systems is difficult because the agent may perform well in some cases and fail in others, depending on the specific conditions it encounters.
⚠️ Unpredictable failure modes: AI agents can fail in unexpected ways, often only discovered in real-world deployment, necessitating ongoing monitoring and improvements.
These challenges make it clear that evaluating the quality of agentic systems is far from straightforward. Ensuring that an agent can handle the variety, unpredictability, and complexity of real-world interactions requires rigorous, ongoing testing and refinement.
Conclusion
The real-world impact of low-quality agentic systems is undeniable. Poorly designed or underperforming agents can erode customer trust, escalate operational costs, and significantly damage a brand’s reputation. The stakes are even higher in industries like healthcare, finance, and law, where the risks of error can be catastrophic. Therefore, businesses must prioritize the evaluation, testing, and ongoing refinement of AI agents to ensure they consistently meet both user expectations and business goals.
As we move forward, measuring the quality of agents at every stage—from development to post-release—will be key to maintaining high standards and driving long-term success. In the next part of this series, we will explore the metrics necessary to evaluate agentic workflows and ensure that AI systems deliver the best outcomes in real-world scenarios.
To learn more about the metrics for evaluating your agentic applications, refer to part 2 of our Agent Evaluation series.
References
- Anthropic. (2023). Building effective AI agents.