Autogen Update: A New Way to Evaluate Agents for LLM-Powered Applications

As the landscape of artificial intelligence continues to evolve, one crucial element gaining attention is the effectiveness of agents in LLM (Large Language Model) powered applications. Despite skepticism about their relevancy and performance, the reality is that agents are not just here to stay; they are also continually improving. Microsoft’s Autogen project, which underlines significant investment in developing robust evaluation tools for these agents, showcases this evolution. Here, we explore the latest innovations in agent evaluation with the updated Autogen framework known as Agent Eval.

Introduction to Agent Evaluation

Evaluating agents within AI applications is crucial for ensuring that they meet specific performance standards required for practical deployment. Traditionally, human evaluators have undertaken this task, providing subjective assessments based on the agent’s ability to perform designated tasks. However, this method can be costly and less scalable. Enter Agent Eval—a sophisticated framework designed to automate and enhance the accuracy of agent evaluation.

Understanding the Agent Eval Framework

Agent Eval is structured around three primary components: the Critique Agent, the Quantifier Agent, and the Verifier Agent. Each plays a vital role in a comprehensive evaluation process, ensuring that an application’s agents are not only effective but also reliable and robust in various scenarios.

The Role of the Critique Agent

The Critique Agent begins the evaluation process by setting the criteria based on the given task’s description. It defines what a successful and failed execution looks like, which are critical for measuring the agent’s performance. For example, in a math tutoring application, the Critique Agent might focus on criteria such as efficiency, clarity, and correctness—attributes that directly impact the user’s learning experience.

Functionality of the Quantifier Agent

Once the evaluation criteria are established, the Quantifier Agent steps in to measure how well the application adheres to each criterion. This role involves a detailed quantification process that results in a multi-dimensional assessment of the app’s utility, allowing developers to understand various performance metrics and identify areas for improvement.

Verifying with the Verifier Agent

The final step lies with the Verifier Agent, which ensures that the criteria assessed are not only consistent and measurable but also truly beneficial for the end-user. This involves two critical actions: testing criteria stability and examining discriminative power. The Verifier Agent checks for redundancy, consistency, and the ability to distinguish between high-quality and compromised data outputs.

Practical Implementation of Agent Eval

In practice, the implementation of Agent Eval involves two main stages: criteria generation and criteria quantification. Criteria generation uses execution chains from sample tasks to create appropriate evaluation standards. This ensures that the evaluation mirrors real-world usage as closely as possible. An example might be assessing a problem-solving agent where criteria include accuracy, conciseness, and relevance.

Once criteria are set, the quantification process begins. Here, a sample output might score the agent based on each criterion, providing a detailed performance map. This score helps developers fine-tune the agent’s capabilities or adjust the evaluation metrics as required.

The Future of Agent Evaluation in Autogen Studio

While the framework is robust, integration into Autogen Studio and complete implementation of the Verifier Agent are still in the development stages. Future updates are expected to streamline these processes further, making Agent Eval an integral part of the development toolkit available in Autogen Studio.

Conclusion: Empowering Development with Advanced Tools

The evolution of agent evaluation represented by Agent Eval signifies a pivotal advancement in crafting AI applications that are not only effective but are also aligned with user needs and robust against varied conditions. For developers aiming to produce high-quality, production-ready applications, embracing such sophisticated evaluation tools becomes not just beneficial but essential.

As AI continues to progress, the tools and frameworks we use to gauge and enhance agent performance will be crucial in shaping applications that genuinely add value to end-users. Agent Eval is a testament to this ongoing innovation, promising a future where agent-based solutions meet the highest standards of utility and reliability.

[h3]Watch this video for the full details:[/h3]


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—
In this specific video, I’m a sharing a new update of the Autogen Project.
Agenteval- a method to evaluate LLMS performance using 3 different agents.
https://microsoft.github.io/autogen/blog/2024/06/21/AgentEval/

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[h3]Transcript[/h3]
how to evaluate agents I know I haven’t covered anything about agents lately and I saw many YouTubers many people who report about AI kind of saying that agents are irrelevant or that they don’t really work I’m not sure what I think about um these comments I think that agents are here to stay I do agree that at the moment they don’t perform well enough and you do need to give them boundaries and constraints in order to really produce something that is production level ready but I think they they keep on evolving and obviously the Microsoft team probably puts a lot of thought into them and the autogen project is a project that I personally believe in and I see that they keep on updating it and now they are relaunching or perhaps updating an agent evalu ation parameter or more like a developer tool to assess the utility of llm powered applications and we will cover this today I covered this a few months ago but this is an updated post and and I wanted to share it with you guys so basically what is an agent eval it’s a framework that consists of three agents that allow you to assess the performance of the agent or the llm so here is how it looks like so you you have the task and you have the critique agent the critique agent provides the you provide the task description and what does a successful execution and a failed execution look like then the criteria are being H send to the quantifier agent the quantifier agent basically gives score based on each of based on each of the parameters and then everything is moved to another agent which is called the verifier agent and I will show you more later on probably even now let’s see okay let’s just cover the the blog post and I will focus on anything that I find specifically relevant and skip over things that are irrelevant so tldr as a developer how can you assess the utility and effectiveness of an llm powered application in helping end users with their task to shed light on the question above they previously introduced a agent eval a framework to assess the multi-dimensional utility of any llm powered application crafted to assist user in specific task now they int they introduce an updated version of agent eval that includes a verification process to estimate the robustness of the of the quantifier agent more detail can be found in this paper we won’t cover this and so previously they have this agent eval which was a comprehensive framework designed to bridge the gap in assessing the utility of llm powered applications it leverages recent advancements in LM to offer a scalable and cost effective alternative to traditional human evaluations the framework comprises three May agents the critic agent the first one the quantifier agent the second one and the the verifier agent now let’s cover each one of them so the critique agent primary function is to suggest a set of criteria for Ev EV valuating an application based based on the task description an example of successful and failed executions for instance in the context of a mass tutoring application the critique agent might propose criteria such as efficiency Clarity Clarity and correctness these criteria are essential for understanding the various dimensions of the application’s performance then the quantifier agent basically once the criteria are established the quantifier agent agent takes over to quantify how well the application performs against each Criterion this quantification process results in a multi-dimensional assessment of the application utility providing a detailed view of its strengths and weaknesses and then the verifier verifier engent basically ensures the criteria used to evaluate a utility are effective for the end user that it maintains both robustness and high discriminative power it does through it does this through two main actions so first of all criteria stability ensure criteria are essential non redundant and consistently measurable iterates over generating and quantifying criteria eliminating redundancies and evaluating their stability and retains only the most robust criteria then discriminative power test the systems reliability by introducing adversarial examples noisy or comprised data this is very important assesses the system system ability to distinguish these from standard cases and if the system fails it indicates the need for a better criteria to handle varied conditions effectively [Music] um here are two use cases or two test or empirical validations that they did so the framework was tested on two applications a mass problem and a household task simulation um I won’t cover this let’s let’s just show you an example of how this things look like in the implementation so basically agent eval currently has two main stages the criteria generation and the criteria quantification the verification is still under development so basically for the criteria generation what you have to do is Agent eval uses example execution messages change chains to create a set of criteria for quantifying how well an application performed for a given tasks so basically these are the parameters you provide the task to evaluate additional instructions the maximum number of rounds to run the conversation and you can also assign a a sub critic which is basically um an agent that will generate sub criteria as as written here the sub critic agent will break down or generating criteria into small smaller criteria to be assessed so here’s an example called so the name math problem solving then the description given any question the system needs to solve the problem as concisely and accurately as possible the successful response is response successful and the fail response is response failed and the criteria is gener generate criteria now the example output is first of all we have the accuracy so this is the description and these are the accepted values correct minor eror major errors or incorrect conciseness very concise concise somewhat veros veros and relevance highly relevant relevant somewhat relevant or not relevant then the criteria quantification so basically this agent is taking this this this criteria and provides a score so basically let’s see an example over here so here’s an example the output will be adjacent object consisting of the ground tools and addictionary mapping each criteria to its score so estimated performance accuracy is correct conciseness concise and relevance highly relevant and here they are sharing what is next so basically enabling agent inval in autogen studio is still not available in autogen studio and fully implementing verifier agent in the agent eval framework um and one last thing that I wanted to share with you guys is I also use cloud I don’t know and don’t know if I updated in the videos in the last few videos but I Chang most of the work that I’m doing is with Cloud 3.5 Sonet and this is um a very tool a very cool feature that I’m using which is basically called artifacts so what I did I copied this whole article and I asked CL to create a visually appealing way to study the content of the blog post and this is what it created so it created this so the overview agenty Val is a framework for assessing the multi-dimension utility of llm powered applications and then it broke down each one of these so it wrote what does each agent do the key features of each agent here’s a sample evaluation results that it basically created and some examples so example applications so the ma problem problem solving is one another one is email writing assistant so let’s say the sample criteria would be tone appropriate appropriateness grammar correct correctness and content relevance or if we had a code debugger so bug detection rate fix accuracy and examination Clarity so these are the criteria that might be proved divided by the critic agent then we have the quantifier agent which basically will give score to each one of these and the verifier agent which is still relevant but basically we’ll verify that all of these makes sense that’s it for today guys I think this is very useful and progress in the agent game I think if you want to build something robust and ready for production level you must be able to implement these evaluations and this is a important step moving forward with regards to using agents that’s it for today if you enjoyed this video please like And subscribe and until next time keep on automating