The advent of LLMs has driven advances in ai for decades. One of those advanced applications of LLM are agents, which remarkably replicate human reasoning. An agent is a system that can perform complicated tasks following a reasoning process similar to that of humans: think (solution to the problem), collect (context of past information), analyze (situations and data) and adapt (according to style). . and feedback). The agents power the system through dynamic and intelligent activities, including planning, data analysis, data retrieval, and utilization of the model's past experiences.
A typical agent has four components:
- Brain: An LLM with advanced processing capabilities, such as prompts.
- Memory: To store and retrieve information.
- Planning: Break tasks down into subsequences and create plans for each one.
- Tools: Connectors that integrate LLMs with the external environment, similar to joining two LEGO pieces. The tools allow agents to perform unique tasks by combining LLM with databases, calculators or APIs.
Now that we have established the wonders of agents to transform an ordinary LLM into a specialized and intelligent tool, it is necessary to evaluate the effectiveness and reliability of an agent. Agent Evaluation It not only checks the quality of the framework in question, but also identifies the best processes and reduces inefficiencies and bottlenecks. This article discusses four ways to measure agent effectiveness.
- Agent as judge: It is the evaluation of ai by ai and for ai. LLMs take on the roles of judge, supervisor and examinee in this arrangement. The judge examines the examinee's response and renders his or her decision based on accuracy, completeness, relevance, timeliness, and cost-effectiveness. The examiner coordinates between the judge and the examinee by providing the target tasks and obtaining the judge's response. The examiner also provides descriptions and clarifications to the LLM examinee. The “Agent as Judge” framework has eight interactive modules. Agents play the role of judges much better than LLMs, and this approach has a high alignment rate with human evaluation. An example of this is the OpenHands evaluation, where the agent evaluation performed 30% better than the LLM evaluation.
- <a target="_blank" href="https://docs.raga.ai/ragaai-aaef-agentic-application-evaluation-framework” target=”_blank” rel=”noreferrer noopener”>Agent Application Evaluation Framework (AAEF) Evaluate the performance of agents on specific tasks. Qualitative outcomes such as effectiveness, efficiency, and adaptability are measured for agents through four components: Tool Utilization Effectiveness (TUE), Memory Coherence and Recall (MCR), Strategic Planning Index (SPI) and component synergy score (CSS). Each of them specializes in different evaluation criteria, from the selection of appropriate tools to the measurement of memory, the ability to plan and execute and the ability to work coherently.
- IA MOSAIC: The Mosaic ai Agent evaluation framework, announced by Databricks, solves multiple challenges simultaneously. It offers a unified set of metrics, including but not limited to accuracy, precision, recall, and F1 score, to facilitate the process of choosing the right metrics for evaluation. Further integrate human review and feedback to define high-quality responses. In addition to providing a robust evaluation pipeline, Mosaic ai also has MLFlow integration to take the model from development to production while improving it. Mosaic ai also provides a simplified SDK for application lifecycle management.
- WORFEVAL: It is a systematic protocol that helps evaluate the workflow capabilities of an LLM agent through quantitative algorithms based on advanced subsequences and subgraph matching. This evaluation technique compares the predicted node chains and workflow graphs with the correct flows. WORFEVAL is at the advanced end of the spectrum, where the application of the agent is done on complex structures such as directed acyclic graphs in a multifaceted scenario.
Each of the above methods helps developers test whether their agent is working satisfactorily and find the optimal configuration, but they have their drawbacks. Discussing the agent's judgment could first be questioned in complex tasks that require deep knowledge. One could always ask about the teacher's competence! Even agents trained on specific data can have biases that make generalization difficult. The AAEF faces a similar fate in complex and dynamic tasks. MOSAIC ai is good, but its credibility decreases as the scale and diversity of data increases. At the higher end of the spectrum, WORFEVAL works well even with complex data, but its performance depends on the correct workflow, which is a random variable: the definition of the correct workflow changes from computer to computer.
Conclusion: Agents are an attempt to make LLMs more human with intelligent reasoning and decision-making capabilities. Therefore, evaluation of agents is imperative to ensure their claims and quality. Agents as a judge, Agentic application evaluation framework, Mosaic ai and WORFEVAL are the current main evaluation techniques. While Agents as Judge starts with the basic intuitive idea of peer review, WORFEVAL deals with complex data. Although these assessment methods work well in their respective contexts, they face difficulties as tasks become more complex with complicated structures.
Adeeba Alam Ansari is currently pursuing her dual degree from the Indian Institute of technology (IIT) Kharagpur, where she earned a bachelor's degree in Industrial Engineering and a master's degree in Financial Engineering. With a keen interest in machine learning and artificial intelligence, she is an avid reader and curious person. Adeeba firmly believes in the power of technology to empower society and promote well-being through innovative solutions driven by empathy and a deep understanding of real-world challenges.
