Dec 20, 2024
8 Essential Steps to Benchmarking AI Agents
Conor Bronsdon
Head of Developer Awareness
Conor Bronsdon
Head of Developer Awareness
Behind every successful AI deployment lies a critical factor most enterprises overlook: rigorous benchmarking. As AI agents increasingly drive customer interactions, automate workflows, and make business-critical decisions, the difference between market leaders and laggards often comes down to how well these systems are evaluated and optimized.
Organizations that implement robust benchmarking frameworks can get higher ROI on their AI investments and significantly reduce operational risks. By systematically measuring agent performance against clear standards, enterprises can identify inefficiencies, prevent costly failures, and continuously improve their AI capabilities while competitors struggle with unreliable systems.
This article explores the essential steps to effectively benchmark AI agents, helping your organization get measurable value from AI investments.
What is AI Agents’ Benchmarking?
AI Agents’ benchmarking is a systematic process of evaluating and comparing the performance of artificial intelligence systems across standardized tasks and datasets. Unlike traditional machine learning model evaluation, benchmarking AI agents focuses on assessing higher-level competencies such as decision-making, tool usage, and environmental interaction.
This approach is crucial for understanding how AI agents perform in real-world scenarios that often involve complex, multi-step reasoning and adaptability.
Benchmarking AI agents goes beyond simple metrics like accuracy or speed. It aims to capture an agent's ability to:
Make decisions in dynamic environments
Use tools and external resources effectively
Adapt to changing conditions and requirements
Collaborate with humans or other AI systems
Generalize knowledge across diverse tasks
These capabilities are essential for modern AI applications, where agents are expected to operate autonomously in unpredictable real-world settings.
The Evolution of AI Agents Benchmarking
Benchmarking AI agents has evolved dramatically alongside AI technology itself. Early benchmarks targeted narrow, task-specific evaluations. Chess engines were judged solely on defeating human grandmasters. These benchmarks missed the broader intelligence needed for general AI applications.
As machine learning advanced, benchmarks like ImageNet for computer vision and GLUE for natural language processing emerged. These standardized datasets enabled rigorous comparisons between AI approaches but still assessed isolated tasks rather than complete agent capabilities.
Large language models and complex AI systems drove the development of multi-task and multi-agent benchmarks. The Massive Multitask Language Understanding (MMLU) benchmark tests models across subjects from law to mathematics, recognizing that modern AI agents need competence across diverse domains.
Recently, benchmarks designed specifically for AI agents have appeared. Frameworks like Generalized AI Agent Benchmark (GAIA) assess an agent's ability to operate under diverse conditions, adapt to changing scenarios, collaborate, and generalize knowledge. These advanced benchmarks reflect the growing complexity of AI systems and their real-world applications.
Let’s explore the necessary steps to benchmark your AI agents with modern best practices effectively.
Step #1: Define Your Agent Benchmarking Goals and Metrics
Clear objectives aligned with business goals are the foundation for meaningful benchmarking of AI agents. When defining your benchmarking approach, you'll need both technical and business performance metrics for a complete view of performance.
Technical metrics like accuracy, precision, recall, and F1 score show your agent's core capabilities. But don't skip business-oriented metrics such as cost savings, user satisfaction, and operational efficiency. These show your AI agent's actual impact in the real world.
Use multiple metrics for a nuanced understanding of agent performance. Optimizing for a single metric often backfires in practice. Try composite scoring methods or multi-objective optimization to find the right balance.
For performance targets, look at industry standards and your specific use cases. Be ready to adjust based on your unique business needs and constraints. The best balance between different metrics depends on your application context.
The trade-off between model accuracy and resource usage matters greatly in benchmarking AI agents. For example, a quantized MobileNetV3 can achieve approximately 70% accuracy on ImageNet with 10x fewer computations than a full-precision ResNet-50, which achieves 76% accuracy. This shows how significant resource savings are possible with only a modest accuracy reduction, potentially creating more cost-effective and scalable solutions.
Step #2: Create Representative Test Scenarios and Datasets
Benchmarking AI agents effectively requires thoughtfully designed test scenarios. These scenarios must mirror real-world conditions to provide meaningful performance insights. Diverse test data is essential for a comprehensive evaluation.
Include common cases alongside edge cases and adversarial examples. This variety helps identify potential weaknesses and biases in your AI system. Quality assurance techniques must be applied rigorously throughout the testing of AI agents.
Bias mitigation and proper distribution representation ensure fair, reliable benchmarks. Without these safeguards, your evaluation results may mislead rather than inform. Always maintain complete separation between training and test datasets.
This separation prevents overfitting and ensures your agent's performance extends to new, unseen data. Additionally, complex environments require sophisticated modeling techniques. Leading organizations now use advanced simulation to create realistic testing conditions.
Your test scenarios should also evaluate the agent's ability to generalize. This capability determines how well your AI system handles unexpected inputs. The quality of your test data directly affects assessment validity.
Therefore, invest significant time in developing comprehensive datasets. Well-designed test scenarios reveal not just how your agent performs today, but how it will likely perform tomorrow. This foresight provides valuable guidance for ongoing development efforts.
Step #3: Implement Novel Benchmarking Frameworks
Traditional benchmarking approaches fall short when evaluating sophisticated AI agents in complex enterprise environments. New frameworks now offer more comprehensive evaluations that capture the nuanced capabilities of modern agents.
MultiAgentBench presents a powerful solution for evaluating LLM-based multi-agent systems. This framework assesses agents across diverse interactive scenarios using milestone-based KPIs. These metrics measure task completion quality and collaboration effectiveness.
The framework supports various coordination protocols and features a modular design for easy extension. Additionally, it offers hierarchical execution modes and shared memory for agent communication.
Another valuable framework is the GAIA benchmark. This comprehensive suite evaluates intelligent agents in both controlled and adaptive environments. GAIA tracks performance via a public leaderboard and emphasizes reproducible results through standardization.
The benchmark thoroughly evaluates planning, reasoning, and decision-making capabilities. It also assesses robustness against adversarial attacks and human-agent interaction quality.
For adaptive multi-modal evaluation, τ-bench (Tau-bench) offers specialized capabilities. This framework focuses on complex real-world tasks beyond synthetic skill tests. It allows open submission of agents and evaluations for ranking.
τ-bench particularly emphasizes tool-use and reasoning under uncertainty. The framework uses public protocols and open-source codebases for easy replication. Furthermore, it explicitly handles environmental randomness and agent initialization variables.
These advanced frameworks collectively improve standardization through containerization techniques. They provide transparent protocols via public leaderboards and ensure clear separation of training and test data. Their modular architectures adapt to emerging use cases.
Step #4: Ensure Standardization and Reproducibility
Standardized and reproducible benchmarking practices are essential for the reliable evaluation of AI agents. These techniques ensure consistent results across different models and implementations.
To begin creating standardized environments, containerization with Docker enables exact environment replication. Package your benchmarking code, dependencies, and configurations into a portable container to maintain testing environments consistent across different systems.
Building on this foundation, public leaderboards like Galileo’s Agent Leaderboard and transparent protocols provide centralized comparison platforms. These foster healthy competition and drive innovation while ensuring fair comparisons through clear guidelines.
Beyond environment controls, version control for benchmarking code and datasets maintains reproducibility. Tools like Git help track changes over time, revert to previous versions when needed, and support effective collaboration. This practice also helps identify and fix inconsistencies or errors in the benchmarking process.
Complementing these technical approaches, detailed documentation of testing conditions supports reproducibility. Records of hardware specifications, software versions, hyperparameters, and other relevant factors allow others to replicate your results accurately. This transparency builds trust and facilitates knowledge sharing.
Finally, for non-deterministic AI outputs, strategies like averaging results over multiple runs or using fixed random seeds can help. While these approaches won't eliminate variability completely, they create more reliable evaluations and clearer pictures of an AI agent's true performance.
Step #5: Address Security and Data Integrity
Maintaining data integrity and security during AI agent benchmarking protects your results and sensitive information. Following security best practices, such as implementing strong access controls with granular permissions, reduces intentional or accidental breaches that could compromise your benchmark integrity.
Building on these controls, keep detailed logs of all accesses, data changes, and AI model interactions during benchmarking. This transparency supports regulatory compliance and enables thorough auditing if issues arise. Robust management practices and audit strategies are essential to safeguard financial data and ensure compliance with regulatory standards.
With audit trails in place, implement continuous surveillance to detect unusual or unauthorized activity in your benchmarking environment, allowing a quick response to potential security threats or data integrity violations.
Working alongside monitoring systems, verify the authenticity and integrity of datasets before benchmarking using cryptographic hashes or other forensic methods to confirm data hasn't been tampered with or corrupted.
As a final layer of protection, use data segmentation techniques and avoid unnecessary sharing of raw data. Avoid submitting raw data to external AI models, sending only metadata to reduce exposure to potential breaches.
Step #6: Optimize for Cost and Performance
Finding the sweet spot between model accuracy and computational efficiency is key for successful AI deployment. Smart optimization strategies can dramatically improve performance while reducing resource usage and costs.
Pruning removes unnecessary model parameters, typically shrinking model size by 30-50% without sacrificing accuracy. Building on this foundation, quantization goes further by reducing the numerical precision of weights. Quantization can shrink models by 75-80% with minimal accuracy impact (usually under 2%), making it perfect for edge devices.
To illustrate these benefits, a quantized MobileNetV3 achieves about 70% accuracy on ImageNet using 10x fewer computations than a full-precision ResNet-50 (which reaches 76% accuracy). This trade-off makes sense for real-time or resource-constrained applications.
Complementing these parameter reduction techniques, knowledge distillation trains a smaller "student" model to mimic a larger "teacher" model. This technique retains 90-95% of the original model's performance while drastically reducing computational costs.
As a practical example, DistilBERT achieves 95% of BERT's language model performance with 40% fewer parameters and 60% faster inference. Further enhancing efficiency, tailoring models to specific hardware (like Apple's Core ML for iOS devices) leverages built-in accelerators for optimized computation.
Additionally, dynamic computation strategies, where not all model layers activate for every input, reduce processing for simpler cases. For maximum impact, applying these techniques sequentially creates compact, fast AI agent models.
When implementing these optimizations, your specific application context and business goals should determine the balance between accuracy and resource usage. Consider your deployment constraints when selecting optimization approaches, and continuously monitor and adapt as your AI agents evolve.
Step #7: Implement Continuous Monitoring and Evaluation
One-time benchmarking isn't enough. Continuous monitoring and AI observability of AI agents in production is critical for maintaining performance as environments change. Real-time dashboards and alerts help detect performance drops, drift, emerging biases, or unexpected behaviors.
To set up continuous monitoring, build automated evaluation pipelines that regularly test agents against benchmark datasets, providing ongoing performance assessment against established baselines. Deploy real-time analytics to support these pipelines. Galileo provides continuous monitoring and tracking performance in production.
Create feedback loops with end-users to complement automated systems, offering valuable insights into real-world performance and highlighting improvement areas that automated metrics might miss.
Use A/B testing alongside feedback loops to evaluate model improvements, letting you compare new versions against existing ones in live environments, confirming that changes truly enhance performance. Add drift detection algorithms to strengthen your monitoring suite, spotting when your AI agent's performance starts deviating from expected norms.
This matters especially as usage patterns and data distributions evolve. These continuous monitoring strategies work together to help you identify and fix issues before they impact users. Your AI agents will maintain high performance and reliability even in dynamic, real-world environments.
Elevate Your AI Agent Performance with Galileo
Comprehensive benchmarking of AI agents is crucial for enterprise success in today's AI landscape. Effective benchmarking ensures optimal performance, drives innovation, enhances decision-making, and builds trust in AI systems. The challenges of benchmarking AI agents in dynamic, real-world environments are significant, but so are the opportunities for improvement.
Galileo leads the AI agent benchmarking field with powerful tools and methodologies:
Comprehensive Evaluation Framework: Galileo offers robust benchmarking tools that assess AI agents across multiple dimensions, including accuracy, efficiency, and adaptability. This holistic approach ensures your agents are ready for real-world deployment.
Real-Time Monitoring and Adaptation: Galileo provides continuous monitoring of your AI agents, detecting performance issues, drift, or unexpected behaviors as they occur. This real-time insight enables rapid adjustments.
Standardized Benchmarking Protocols: Galileo delivers standardized benchmarking methodologies that enhance reproducibility and comparability across different AI systems. This standardization builds trust and supports scalable enterprise adoption.
Cost-Performance Optimization: With advanced techniques, Galileo helps balance trade-offs between model accuracy and resource usage. This ensures your AI solutions deliver high performance while remaining cost-effective.
Explore Galileo to enhance your AI agent benchmarking capabilities and take your AI initiatives to new heights.
Behind every successful AI deployment lies a critical factor most enterprises overlook: rigorous benchmarking. As AI agents increasingly drive customer interactions, automate workflows, and make business-critical decisions, the difference between market leaders and laggards often comes down to how well these systems are evaluated and optimized.
Organizations that implement robust benchmarking frameworks can get higher ROI on their AI investments and significantly reduce operational risks. By systematically measuring agent performance against clear standards, enterprises can identify inefficiencies, prevent costly failures, and continuously improve their AI capabilities while competitors struggle with unreliable systems.
This article explores the essential steps to effectively benchmark AI agents, helping your organization get measurable value from AI investments.
What is AI Agents’ Benchmarking?
AI Agents’ benchmarking is a systematic process of evaluating and comparing the performance of artificial intelligence systems across standardized tasks and datasets. Unlike traditional machine learning model evaluation, benchmarking AI agents focuses on assessing higher-level competencies such as decision-making, tool usage, and environmental interaction.
This approach is crucial for understanding how AI agents perform in real-world scenarios that often involve complex, multi-step reasoning and adaptability.
Benchmarking AI agents goes beyond simple metrics like accuracy or speed. It aims to capture an agent's ability to:
Make decisions in dynamic environments
Use tools and external resources effectively
Adapt to changing conditions and requirements
Collaborate with humans or other AI systems
Generalize knowledge across diverse tasks
These capabilities are essential for modern AI applications, where agents are expected to operate autonomously in unpredictable real-world settings.
The Evolution of AI Agents Benchmarking
Benchmarking AI agents has evolved dramatically alongside AI technology itself. Early benchmarks targeted narrow, task-specific evaluations. Chess engines were judged solely on defeating human grandmasters. These benchmarks missed the broader intelligence needed for general AI applications.
As machine learning advanced, benchmarks like ImageNet for computer vision and GLUE for natural language processing emerged. These standardized datasets enabled rigorous comparisons between AI approaches but still assessed isolated tasks rather than complete agent capabilities.
Large language models and complex AI systems drove the development of multi-task and multi-agent benchmarks. The Massive Multitask Language Understanding (MMLU) benchmark tests models across subjects from law to mathematics, recognizing that modern AI agents need competence across diverse domains.
Recently, benchmarks designed specifically for AI agents have appeared. Frameworks like Generalized AI Agent Benchmark (GAIA) assess an agent's ability to operate under diverse conditions, adapt to changing scenarios, collaborate, and generalize knowledge. These advanced benchmarks reflect the growing complexity of AI systems and their real-world applications.
Let’s explore the necessary steps to benchmark your AI agents with modern best practices effectively.
Step #1: Define Your Agent Benchmarking Goals and Metrics
Clear objectives aligned with business goals are the foundation for meaningful benchmarking of AI agents. When defining your benchmarking approach, you'll need both technical and business performance metrics for a complete view of performance.
Technical metrics like accuracy, precision, recall, and F1 score show your agent's core capabilities. But don't skip business-oriented metrics such as cost savings, user satisfaction, and operational efficiency. These show your AI agent's actual impact in the real world.
Use multiple metrics for a nuanced understanding of agent performance. Optimizing for a single metric often backfires in practice. Try composite scoring methods or multi-objective optimization to find the right balance.
For performance targets, look at industry standards and your specific use cases. Be ready to adjust based on your unique business needs and constraints. The best balance between different metrics depends on your application context.
The trade-off between model accuracy and resource usage matters greatly in benchmarking AI agents. For example, a quantized MobileNetV3 can achieve approximately 70% accuracy on ImageNet with 10x fewer computations than a full-precision ResNet-50, which achieves 76% accuracy. This shows how significant resource savings are possible with only a modest accuracy reduction, potentially creating more cost-effective and scalable solutions.
Step #2: Create Representative Test Scenarios and Datasets
Benchmarking AI agents effectively requires thoughtfully designed test scenarios. These scenarios must mirror real-world conditions to provide meaningful performance insights. Diverse test data is essential for a comprehensive evaluation.
Include common cases alongside edge cases and adversarial examples. This variety helps identify potential weaknesses and biases in your AI system. Quality assurance techniques must be applied rigorously throughout the testing of AI agents.
Bias mitigation and proper distribution representation ensure fair, reliable benchmarks. Without these safeguards, your evaluation results may mislead rather than inform. Always maintain complete separation between training and test datasets.
This separation prevents overfitting and ensures your agent's performance extends to new, unseen data. Additionally, complex environments require sophisticated modeling techniques. Leading organizations now use advanced simulation to create realistic testing conditions.
Your test scenarios should also evaluate the agent's ability to generalize. This capability determines how well your AI system handles unexpected inputs. The quality of your test data directly affects assessment validity.
Therefore, invest significant time in developing comprehensive datasets. Well-designed test scenarios reveal not just how your agent performs today, but how it will likely perform tomorrow. This foresight provides valuable guidance for ongoing development efforts.
Step #3: Implement Novel Benchmarking Frameworks
Traditional benchmarking approaches fall short when evaluating sophisticated AI agents in complex enterprise environments. New frameworks now offer more comprehensive evaluations that capture the nuanced capabilities of modern agents.
MultiAgentBench presents a powerful solution for evaluating LLM-based multi-agent systems. This framework assesses agents across diverse interactive scenarios using milestone-based KPIs. These metrics measure task completion quality and collaboration effectiveness.
The framework supports various coordination protocols and features a modular design for easy extension. Additionally, it offers hierarchical execution modes and shared memory for agent communication.
Another valuable framework is the GAIA benchmark. This comprehensive suite evaluates intelligent agents in both controlled and adaptive environments. GAIA tracks performance via a public leaderboard and emphasizes reproducible results through standardization.
The benchmark thoroughly evaluates planning, reasoning, and decision-making capabilities. It also assesses robustness against adversarial attacks and human-agent interaction quality.
For adaptive multi-modal evaluation, τ-bench (Tau-bench) offers specialized capabilities. This framework focuses on complex real-world tasks beyond synthetic skill tests. It allows open submission of agents and evaluations for ranking.
τ-bench particularly emphasizes tool-use and reasoning under uncertainty. The framework uses public protocols and open-source codebases for easy replication. Furthermore, it explicitly handles environmental randomness and agent initialization variables.
These advanced frameworks collectively improve standardization through containerization techniques. They provide transparent protocols via public leaderboards and ensure clear separation of training and test data. Their modular architectures adapt to emerging use cases.
Step #4: Ensure Standardization and Reproducibility
Standardized and reproducible benchmarking practices are essential for the reliable evaluation of AI agents. These techniques ensure consistent results across different models and implementations.
To begin creating standardized environments, containerization with Docker enables exact environment replication. Package your benchmarking code, dependencies, and configurations into a portable container to maintain testing environments consistent across different systems.
Building on this foundation, public leaderboards like Galileo’s Agent Leaderboard and transparent protocols provide centralized comparison platforms. These foster healthy competition and drive innovation while ensuring fair comparisons through clear guidelines.
Beyond environment controls, version control for benchmarking code and datasets maintains reproducibility. Tools like Git help track changes over time, revert to previous versions when needed, and support effective collaboration. This practice also helps identify and fix inconsistencies or errors in the benchmarking process.
Complementing these technical approaches, detailed documentation of testing conditions supports reproducibility. Records of hardware specifications, software versions, hyperparameters, and other relevant factors allow others to replicate your results accurately. This transparency builds trust and facilitates knowledge sharing.
Finally, for non-deterministic AI outputs, strategies like averaging results over multiple runs or using fixed random seeds can help. While these approaches won't eliminate variability completely, they create more reliable evaluations and clearer pictures of an AI agent's true performance.
Step #5: Address Security and Data Integrity
Maintaining data integrity and security during AI agent benchmarking protects your results and sensitive information. Following security best practices, such as implementing strong access controls with granular permissions, reduces intentional or accidental breaches that could compromise your benchmark integrity.
Building on these controls, keep detailed logs of all accesses, data changes, and AI model interactions during benchmarking. This transparency supports regulatory compliance and enables thorough auditing if issues arise. Robust management practices and audit strategies are essential to safeguard financial data and ensure compliance with regulatory standards.
With audit trails in place, implement continuous surveillance to detect unusual or unauthorized activity in your benchmarking environment, allowing a quick response to potential security threats or data integrity violations.
Working alongside monitoring systems, verify the authenticity and integrity of datasets before benchmarking using cryptographic hashes or other forensic methods to confirm data hasn't been tampered with or corrupted.
As a final layer of protection, use data segmentation techniques and avoid unnecessary sharing of raw data. Avoid submitting raw data to external AI models, sending only metadata to reduce exposure to potential breaches.
Step #6: Optimize for Cost and Performance
Finding the sweet spot between model accuracy and computational efficiency is key for successful AI deployment. Smart optimization strategies can dramatically improve performance while reducing resource usage and costs.
Pruning removes unnecessary model parameters, typically shrinking model size by 30-50% without sacrificing accuracy. Building on this foundation, quantization goes further by reducing the numerical precision of weights. Quantization can shrink models by 75-80% with minimal accuracy impact (usually under 2%), making it perfect for edge devices.
To illustrate these benefits, a quantized MobileNetV3 achieves about 70% accuracy on ImageNet using 10x fewer computations than a full-precision ResNet-50 (which reaches 76% accuracy). This trade-off makes sense for real-time or resource-constrained applications.
Complementing these parameter reduction techniques, knowledge distillation trains a smaller "student" model to mimic a larger "teacher" model. This technique retains 90-95% of the original model's performance while drastically reducing computational costs.
As a practical example, DistilBERT achieves 95% of BERT's language model performance with 40% fewer parameters and 60% faster inference. Further enhancing efficiency, tailoring models to specific hardware (like Apple's Core ML for iOS devices) leverages built-in accelerators for optimized computation.
Additionally, dynamic computation strategies, where not all model layers activate for every input, reduce processing for simpler cases. For maximum impact, applying these techniques sequentially creates compact, fast AI agent models.
When implementing these optimizations, your specific application context and business goals should determine the balance between accuracy and resource usage. Consider your deployment constraints when selecting optimization approaches, and continuously monitor and adapt as your AI agents evolve.
Step #7: Implement Continuous Monitoring and Evaluation
One-time benchmarking isn't enough. Continuous monitoring and AI observability of AI agents in production is critical for maintaining performance as environments change. Real-time dashboards and alerts help detect performance drops, drift, emerging biases, or unexpected behaviors.
To set up continuous monitoring, build automated evaluation pipelines that regularly test agents against benchmark datasets, providing ongoing performance assessment against established baselines. Deploy real-time analytics to support these pipelines. Galileo provides continuous monitoring and tracking performance in production.
Create feedback loops with end-users to complement automated systems, offering valuable insights into real-world performance and highlighting improvement areas that automated metrics might miss.
Use A/B testing alongside feedback loops to evaluate model improvements, letting you compare new versions against existing ones in live environments, confirming that changes truly enhance performance. Add drift detection algorithms to strengthen your monitoring suite, spotting when your AI agent's performance starts deviating from expected norms.
This matters especially as usage patterns and data distributions evolve. These continuous monitoring strategies work together to help you identify and fix issues before they impact users. Your AI agents will maintain high performance and reliability even in dynamic, real-world environments.
Elevate Your AI Agent Performance with Galileo
Comprehensive benchmarking of AI agents is crucial for enterprise success in today's AI landscape. Effective benchmarking ensures optimal performance, drives innovation, enhances decision-making, and builds trust in AI systems. The challenges of benchmarking AI agents in dynamic, real-world environments are significant, but so are the opportunities for improvement.
Galileo leads the AI agent benchmarking field with powerful tools and methodologies:
Comprehensive Evaluation Framework: Galileo offers robust benchmarking tools that assess AI agents across multiple dimensions, including accuracy, efficiency, and adaptability. This holistic approach ensures your agents are ready for real-world deployment.
Real-Time Monitoring and Adaptation: Galileo provides continuous monitoring of your AI agents, detecting performance issues, drift, or unexpected behaviors as they occur. This real-time insight enables rapid adjustments.
Standardized Benchmarking Protocols: Galileo delivers standardized benchmarking methodologies that enhance reproducibility and comparability across different AI systems. This standardization builds trust and supports scalable enterprise adoption.
Cost-Performance Optimization: With advanced techniques, Galileo helps balance trade-offs between model accuracy and resource usage. This ensures your AI solutions deliver high performance while remaining cost-effective.
Explore Galileo to enhance your AI agent benchmarking capabilities and take your AI initiatives to new heights.
Behind every successful AI deployment lies a critical factor most enterprises overlook: rigorous benchmarking. As AI agents increasingly drive customer interactions, automate workflows, and make business-critical decisions, the difference between market leaders and laggards often comes down to how well these systems are evaluated and optimized.
Organizations that implement robust benchmarking frameworks can get higher ROI on their AI investments and significantly reduce operational risks. By systematically measuring agent performance against clear standards, enterprises can identify inefficiencies, prevent costly failures, and continuously improve their AI capabilities while competitors struggle with unreliable systems.
This article explores the essential steps to effectively benchmark AI agents, helping your organization get measurable value from AI investments.
What is AI Agents’ Benchmarking?
AI Agents’ benchmarking is a systematic process of evaluating and comparing the performance of artificial intelligence systems across standardized tasks and datasets. Unlike traditional machine learning model evaluation, benchmarking AI agents focuses on assessing higher-level competencies such as decision-making, tool usage, and environmental interaction.
This approach is crucial for understanding how AI agents perform in real-world scenarios that often involve complex, multi-step reasoning and adaptability.
Benchmarking AI agents goes beyond simple metrics like accuracy or speed. It aims to capture an agent's ability to:
Make decisions in dynamic environments
Use tools and external resources effectively
Adapt to changing conditions and requirements
Collaborate with humans or other AI systems
Generalize knowledge across diverse tasks
These capabilities are essential for modern AI applications, where agents are expected to operate autonomously in unpredictable real-world settings.
The Evolution of AI Agents Benchmarking
Benchmarking AI agents has evolved dramatically alongside AI technology itself. Early benchmarks targeted narrow, task-specific evaluations. Chess engines were judged solely on defeating human grandmasters. These benchmarks missed the broader intelligence needed for general AI applications.
As machine learning advanced, benchmarks like ImageNet for computer vision and GLUE for natural language processing emerged. These standardized datasets enabled rigorous comparisons between AI approaches but still assessed isolated tasks rather than complete agent capabilities.
Large language models and complex AI systems drove the development of multi-task and multi-agent benchmarks. The Massive Multitask Language Understanding (MMLU) benchmark tests models across subjects from law to mathematics, recognizing that modern AI agents need competence across diverse domains.
Recently, benchmarks designed specifically for AI agents have appeared. Frameworks like Generalized AI Agent Benchmark (GAIA) assess an agent's ability to operate under diverse conditions, adapt to changing scenarios, collaborate, and generalize knowledge. These advanced benchmarks reflect the growing complexity of AI systems and their real-world applications.
Let’s explore the necessary steps to benchmark your AI agents with modern best practices effectively.
Step #1: Define Your Agent Benchmarking Goals and Metrics
Clear objectives aligned with business goals are the foundation for meaningful benchmarking of AI agents. When defining your benchmarking approach, you'll need both technical and business performance metrics for a complete view of performance.
Technical metrics like accuracy, precision, recall, and F1 score show your agent's core capabilities. But don't skip business-oriented metrics such as cost savings, user satisfaction, and operational efficiency. These show your AI agent's actual impact in the real world.
Use multiple metrics for a nuanced understanding of agent performance. Optimizing for a single metric often backfires in practice. Try composite scoring methods or multi-objective optimization to find the right balance.
For performance targets, look at industry standards and your specific use cases. Be ready to adjust based on your unique business needs and constraints. The best balance between different metrics depends on your application context.
The trade-off between model accuracy and resource usage matters greatly in benchmarking AI agents. For example, a quantized MobileNetV3 can achieve approximately 70% accuracy on ImageNet with 10x fewer computations than a full-precision ResNet-50, which achieves 76% accuracy. This shows how significant resource savings are possible with only a modest accuracy reduction, potentially creating more cost-effective and scalable solutions.
Step #2: Create Representative Test Scenarios and Datasets
Benchmarking AI agents effectively requires thoughtfully designed test scenarios. These scenarios must mirror real-world conditions to provide meaningful performance insights. Diverse test data is essential for a comprehensive evaluation.
Include common cases alongside edge cases and adversarial examples. This variety helps identify potential weaknesses and biases in your AI system. Quality assurance techniques must be applied rigorously throughout the testing of AI agents.
Bias mitigation and proper distribution representation ensure fair, reliable benchmarks. Without these safeguards, your evaluation results may mislead rather than inform. Always maintain complete separation between training and test datasets.
This separation prevents overfitting and ensures your agent's performance extends to new, unseen data. Additionally, complex environments require sophisticated modeling techniques. Leading organizations now use advanced simulation to create realistic testing conditions.
Your test scenarios should also evaluate the agent's ability to generalize. This capability determines how well your AI system handles unexpected inputs. The quality of your test data directly affects assessment validity.
Therefore, invest significant time in developing comprehensive datasets. Well-designed test scenarios reveal not just how your agent performs today, but how it will likely perform tomorrow. This foresight provides valuable guidance for ongoing development efforts.
Step #3: Implement Novel Benchmarking Frameworks
Traditional benchmarking approaches fall short when evaluating sophisticated AI agents in complex enterprise environments. New frameworks now offer more comprehensive evaluations that capture the nuanced capabilities of modern agents.
MultiAgentBench presents a powerful solution for evaluating LLM-based multi-agent systems. This framework assesses agents across diverse interactive scenarios using milestone-based KPIs. These metrics measure task completion quality and collaboration effectiveness.
The framework supports various coordination protocols and features a modular design for easy extension. Additionally, it offers hierarchical execution modes and shared memory for agent communication.
Another valuable framework is the GAIA benchmark. This comprehensive suite evaluates intelligent agents in both controlled and adaptive environments. GAIA tracks performance via a public leaderboard and emphasizes reproducible results through standardization.
The benchmark thoroughly evaluates planning, reasoning, and decision-making capabilities. It also assesses robustness against adversarial attacks and human-agent interaction quality.
For adaptive multi-modal evaluation, τ-bench (Tau-bench) offers specialized capabilities. This framework focuses on complex real-world tasks beyond synthetic skill tests. It allows open submission of agents and evaluations for ranking.
τ-bench particularly emphasizes tool-use and reasoning under uncertainty. The framework uses public protocols and open-source codebases for easy replication. Furthermore, it explicitly handles environmental randomness and agent initialization variables.
These advanced frameworks collectively improve standardization through containerization techniques. They provide transparent protocols via public leaderboards and ensure clear separation of training and test data. Their modular architectures adapt to emerging use cases.
Step #4: Ensure Standardization and Reproducibility
Standardized and reproducible benchmarking practices are essential for the reliable evaluation of AI agents. These techniques ensure consistent results across different models and implementations.
To begin creating standardized environments, containerization with Docker enables exact environment replication. Package your benchmarking code, dependencies, and configurations into a portable container to maintain testing environments consistent across different systems.
Building on this foundation, public leaderboards like Galileo’s Agent Leaderboard and transparent protocols provide centralized comparison platforms. These foster healthy competition and drive innovation while ensuring fair comparisons through clear guidelines.
Beyond environment controls, version control for benchmarking code and datasets maintains reproducibility. Tools like Git help track changes over time, revert to previous versions when needed, and support effective collaboration. This practice also helps identify and fix inconsistencies or errors in the benchmarking process.
Complementing these technical approaches, detailed documentation of testing conditions supports reproducibility. Records of hardware specifications, software versions, hyperparameters, and other relevant factors allow others to replicate your results accurately. This transparency builds trust and facilitates knowledge sharing.
Finally, for non-deterministic AI outputs, strategies like averaging results over multiple runs or using fixed random seeds can help. While these approaches won't eliminate variability completely, they create more reliable evaluations and clearer pictures of an AI agent's true performance.
Step #5: Address Security and Data Integrity
Maintaining data integrity and security during AI agent benchmarking protects your results and sensitive information. Following security best practices, such as implementing strong access controls with granular permissions, reduces intentional or accidental breaches that could compromise your benchmark integrity.
Building on these controls, keep detailed logs of all accesses, data changes, and AI model interactions during benchmarking. This transparency supports regulatory compliance and enables thorough auditing if issues arise. Robust management practices and audit strategies are essential to safeguard financial data and ensure compliance with regulatory standards.
With audit trails in place, implement continuous surveillance to detect unusual or unauthorized activity in your benchmarking environment, allowing a quick response to potential security threats or data integrity violations.
Working alongside monitoring systems, verify the authenticity and integrity of datasets before benchmarking using cryptographic hashes or other forensic methods to confirm data hasn't been tampered with or corrupted.
As a final layer of protection, use data segmentation techniques and avoid unnecessary sharing of raw data. Avoid submitting raw data to external AI models, sending only metadata to reduce exposure to potential breaches.
Step #6: Optimize for Cost and Performance
Finding the sweet spot between model accuracy and computational efficiency is key for successful AI deployment. Smart optimization strategies can dramatically improve performance while reducing resource usage and costs.
Pruning removes unnecessary model parameters, typically shrinking model size by 30-50% without sacrificing accuracy. Building on this foundation, quantization goes further by reducing the numerical precision of weights. Quantization can shrink models by 75-80% with minimal accuracy impact (usually under 2%), making it perfect for edge devices.
To illustrate these benefits, a quantized MobileNetV3 achieves about 70% accuracy on ImageNet using 10x fewer computations than a full-precision ResNet-50 (which reaches 76% accuracy). This trade-off makes sense for real-time or resource-constrained applications.
Complementing these parameter reduction techniques, knowledge distillation trains a smaller "student" model to mimic a larger "teacher" model. This technique retains 90-95% of the original model's performance while drastically reducing computational costs.
As a practical example, DistilBERT achieves 95% of BERT's language model performance with 40% fewer parameters and 60% faster inference. Further enhancing efficiency, tailoring models to specific hardware (like Apple's Core ML for iOS devices) leverages built-in accelerators for optimized computation.
Additionally, dynamic computation strategies, where not all model layers activate for every input, reduce processing for simpler cases. For maximum impact, applying these techniques sequentially creates compact, fast AI agent models.
When implementing these optimizations, your specific application context and business goals should determine the balance between accuracy and resource usage. Consider your deployment constraints when selecting optimization approaches, and continuously monitor and adapt as your AI agents evolve.
Step #7: Implement Continuous Monitoring and Evaluation
One-time benchmarking isn't enough. Continuous monitoring and AI observability of AI agents in production is critical for maintaining performance as environments change. Real-time dashboards and alerts help detect performance drops, drift, emerging biases, or unexpected behaviors.
To set up continuous monitoring, build automated evaluation pipelines that regularly test agents against benchmark datasets, providing ongoing performance assessment against established baselines. Deploy real-time analytics to support these pipelines. Galileo provides continuous monitoring and tracking performance in production.
Create feedback loops with end-users to complement automated systems, offering valuable insights into real-world performance and highlighting improvement areas that automated metrics might miss.
Use A/B testing alongside feedback loops to evaluate model improvements, letting you compare new versions against existing ones in live environments, confirming that changes truly enhance performance. Add drift detection algorithms to strengthen your monitoring suite, spotting when your AI agent's performance starts deviating from expected norms.
This matters especially as usage patterns and data distributions evolve. These continuous monitoring strategies work together to help you identify and fix issues before they impact users. Your AI agents will maintain high performance and reliability even in dynamic, real-world environments.
Elevate Your AI Agent Performance with Galileo
Comprehensive benchmarking of AI agents is crucial for enterprise success in today's AI landscape. Effective benchmarking ensures optimal performance, drives innovation, enhances decision-making, and builds trust in AI systems. The challenges of benchmarking AI agents in dynamic, real-world environments are significant, but so are the opportunities for improvement.
Galileo leads the AI agent benchmarking field with powerful tools and methodologies:
Comprehensive Evaluation Framework: Galileo offers robust benchmarking tools that assess AI agents across multiple dimensions, including accuracy, efficiency, and adaptability. This holistic approach ensures your agents are ready for real-world deployment.
Real-Time Monitoring and Adaptation: Galileo provides continuous monitoring of your AI agents, detecting performance issues, drift, or unexpected behaviors as they occur. This real-time insight enables rapid adjustments.
Standardized Benchmarking Protocols: Galileo delivers standardized benchmarking methodologies that enhance reproducibility and comparability across different AI systems. This standardization builds trust and supports scalable enterprise adoption.
Cost-Performance Optimization: With advanced techniques, Galileo helps balance trade-offs between model accuracy and resource usage. This ensures your AI solutions deliver high performance while remaining cost-effective.
Explore Galileo to enhance your AI agent benchmarking capabilities and take your AI initiatives to new heights.
Behind every successful AI deployment lies a critical factor most enterprises overlook: rigorous benchmarking. As AI agents increasingly drive customer interactions, automate workflows, and make business-critical decisions, the difference between market leaders and laggards often comes down to how well these systems are evaluated and optimized.
Organizations that implement robust benchmarking frameworks can get higher ROI on their AI investments and significantly reduce operational risks. By systematically measuring agent performance against clear standards, enterprises can identify inefficiencies, prevent costly failures, and continuously improve their AI capabilities while competitors struggle with unreliable systems.
This article explores the essential steps to effectively benchmark AI agents, helping your organization get measurable value from AI investments.
What is AI Agents’ Benchmarking?
AI Agents’ benchmarking is a systematic process of evaluating and comparing the performance of artificial intelligence systems across standardized tasks and datasets. Unlike traditional machine learning model evaluation, benchmarking AI agents focuses on assessing higher-level competencies such as decision-making, tool usage, and environmental interaction.
This approach is crucial for understanding how AI agents perform in real-world scenarios that often involve complex, multi-step reasoning and adaptability.
Benchmarking AI agents goes beyond simple metrics like accuracy or speed. It aims to capture an agent's ability to:
Make decisions in dynamic environments
Use tools and external resources effectively
Adapt to changing conditions and requirements
Collaborate with humans or other AI systems
Generalize knowledge across diverse tasks
These capabilities are essential for modern AI applications, where agents are expected to operate autonomously in unpredictable real-world settings.
The Evolution of AI Agents Benchmarking
Benchmarking AI agents has evolved dramatically alongside AI technology itself. Early benchmarks targeted narrow, task-specific evaluations. Chess engines were judged solely on defeating human grandmasters. These benchmarks missed the broader intelligence needed for general AI applications.
As machine learning advanced, benchmarks like ImageNet for computer vision and GLUE for natural language processing emerged. These standardized datasets enabled rigorous comparisons between AI approaches but still assessed isolated tasks rather than complete agent capabilities.
Large language models and complex AI systems drove the development of multi-task and multi-agent benchmarks. The Massive Multitask Language Understanding (MMLU) benchmark tests models across subjects from law to mathematics, recognizing that modern AI agents need competence across diverse domains.
Recently, benchmarks designed specifically for AI agents have appeared. Frameworks like Generalized AI Agent Benchmark (GAIA) assess an agent's ability to operate under diverse conditions, adapt to changing scenarios, collaborate, and generalize knowledge. These advanced benchmarks reflect the growing complexity of AI systems and their real-world applications.
Let’s explore the necessary steps to benchmark your AI agents with modern best practices effectively.
Step #1: Define Your Agent Benchmarking Goals and Metrics
Clear objectives aligned with business goals are the foundation for meaningful benchmarking of AI agents. When defining your benchmarking approach, you'll need both technical and business performance metrics for a complete view of performance.
Technical metrics like accuracy, precision, recall, and F1 score show your agent's core capabilities. But don't skip business-oriented metrics such as cost savings, user satisfaction, and operational efficiency. These show your AI agent's actual impact in the real world.
Use multiple metrics for a nuanced understanding of agent performance. Optimizing for a single metric often backfires in practice. Try composite scoring methods or multi-objective optimization to find the right balance.
For performance targets, look at industry standards and your specific use cases. Be ready to adjust based on your unique business needs and constraints. The best balance between different metrics depends on your application context.
The trade-off between model accuracy and resource usage matters greatly in benchmarking AI agents. For example, a quantized MobileNetV3 can achieve approximately 70% accuracy on ImageNet with 10x fewer computations than a full-precision ResNet-50, which achieves 76% accuracy. This shows how significant resource savings are possible with only a modest accuracy reduction, potentially creating more cost-effective and scalable solutions.
Step #2: Create Representative Test Scenarios and Datasets
Benchmarking AI agents effectively requires thoughtfully designed test scenarios. These scenarios must mirror real-world conditions to provide meaningful performance insights. Diverse test data is essential for a comprehensive evaluation.
Include common cases alongside edge cases and adversarial examples. This variety helps identify potential weaknesses and biases in your AI system. Quality assurance techniques must be applied rigorously throughout the testing of AI agents.
Bias mitigation and proper distribution representation ensure fair, reliable benchmarks. Without these safeguards, your evaluation results may mislead rather than inform. Always maintain complete separation between training and test datasets.
This separation prevents overfitting and ensures your agent's performance extends to new, unseen data. Additionally, complex environments require sophisticated modeling techniques. Leading organizations now use advanced simulation to create realistic testing conditions.
Your test scenarios should also evaluate the agent's ability to generalize. This capability determines how well your AI system handles unexpected inputs. The quality of your test data directly affects assessment validity.
Therefore, invest significant time in developing comprehensive datasets. Well-designed test scenarios reveal not just how your agent performs today, but how it will likely perform tomorrow. This foresight provides valuable guidance for ongoing development efforts.
Step #3: Implement Novel Benchmarking Frameworks
Traditional benchmarking approaches fall short when evaluating sophisticated AI agents in complex enterprise environments. New frameworks now offer more comprehensive evaluations that capture the nuanced capabilities of modern agents.
MultiAgentBench presents a powerful solution for evaluating LLM-based multi-agent systems. This framework assesses agents across diverse interactive scenarios using milestone-based KPIs. These metrics measure task completion quality and collaboration effectiveness.
The framework supports various coordination protocols and features a modular design for easy extension. Additionally, it offers hierarchical execution modes and shared memory for agent communication.
Another valuable framework is the GAIA benchmark. This comprehensive suite evaluates intelligent agents in both controlled and adaptive environments. GAIA tracks performance via a public leaderboard and emphasizes reproducible results through standardization.
The benchmark thoroughly evaluates planning, reasoning, and decision-making capabilities. It also assesses robustness against adversarial attacks and human-agent interaction quality.
For adaptive multi-modal evaluation, τ-bench (Tau-bench) offers specialized capabilities. This framework focuses on complex real-world tasks beyond synthetic skill tests. It allows open submission of agents and evaluations for ranking.
τ-bench particularly emphasizes tool-use and reasoning under uncertainty. The framework uses public protocols and open-source codebases for easy replication. Furthermore, it explicitly handles environmental randomness and agent initialization variables.
These advanced frameworks collectively improve standardization through containerization techniques. They provide transparent protocols via public leaderboards and ensure clear separation of training and test data. Their modular architectures adapt to emerging use cases.
Step #4: Ensure Standardization and Reproducibility
Standardized and reproducible benchmarking practices are essential for the reliable evaluation of AI agents. These techniques ensure consistent results across different models and implementations.
To begin creating standardized environments, containerization with Docker enables exact environment replication. Package your benchmarking code, dependencies, and configurations into a portable container to maintain testing environments consistent across different systems.
Building on this foundation, public leaderboards like Galileo’s Agent Leaderboard and transparent protocols provide centralized comparison platforms. These foster healthy competition and drive innovation while ensuring fair comparisons through clear guidelines.
Beyond environment controls, version control for benchmarking code and datasets maintains reproducibility. Tools like Git help track changes over time, revert to previous versions when needed, and support effective collaboration. This practice also helps identify and fix inconsistencies or errors in the benchmarking process.
Complementing these technical approaches, detailed documentation of testing conditions supports reproducibility. Records of hardware specifications, software versions, hyperparameters, and other relevant factors allow others to replicate your results accurately. This transparency builds trust and facilitates knowledge sharing.
Finally, for non-deterministic AI outputs, strategies like averaging results over multiple runs or using fixed random seeds can help. While these approaches won't eliminate variability completely, they create more reliable evaluations and clearer pictures of an AI agent's true performance.
Step #5: Address Security and Data Integrity
Maintaining data integrity and security during AI agent benchmarking protects your results and sensitive information. Following security best practices, such as implementing strong access controls with granular permissions, reduces intentional or accidental breaches that could compromise your benchmark integrity.
Building on these controls, keep detailed logs of all accesses, data changes, and AI model interactions during benchmarking. This transparency supports regulatory compliance and enables thorough auditing if issues arise. Robust management practices and audit strategies are essential to safeguard financial data and ensure compliance with regulatory standards.
With audit trails in place, implement continuous surveillance to detect unusual or unauthorized activity in your benchmarking environment, allowing a quick response to potential security threats or data integrity violations.
Working alongside monitoring systems, verify the authenticity and integrity of datasets before benchmarking using cryptographic hashes or other forensic methods to confirm data hasn't been tampered with or corrupted.
As a final layer of protection, use data segmentation techniques and avoid unnecessary sharing of raw data. Avoid submitting raw data to external AI models, sending only metadata to reduce exposure to potential breaches.
Step #6: Optimize for Cost and Performance
Finding the sweet spot between model accuracy and computational efficiency is key for successful AI deployment. Smart optimization strategies can dramatically improve performance while reducing resource usage and costs.
Pruning removes unnecessary model parameters, typically shrinking model size by 30-50% without sacrificing accuracy. Building on this foundation, quantization goes further by reducing the numerical precision of weights. Quantization can shrink models by 75-80% with minimal accuracy impact (usually under 2%), making it perfect for edge devices.
To illustrate these benefits, a quantized MobileNetV3 achieves about 70% accuracy on ImageNet using 10x fewer computations than a full-precision ResNet-50 (which reaches 76% accuracy). This trade-off makes sense for real-time or resource-constrained applications.
Complementing these parameter reduction techniques, knowledge distillation trains a smaller "student" model to mimic a larger "teacher" model. This technique retains 90-95% of the original model's performance while drastically reducing computational costs.
As a practical example, DistilBERT achieves 95% of BERT's language model performance with 40% fewer parameters and 60% faster inference. Further enhancing efficiency, tailoring models to specific hardware (like Apple's Core ML for iOS devices) leverages built-in accelerators for optimized computation.
Additionally, dynamic computation strategies, where not all model layers activate for every input, reduce processing for simpler cases. For maximum impact, applying these techniques sequentially creates compact, fast AI agent models.
When implementing these optimizations, your specific application context and business goals should determine the balance between accuracy and resource usage. Consider your deployment constraints when selecting optimization approaches, and continuously monitor and adapt as your AI agents evolve.
Step #7: Implement Continuous Monitoring and Evaluation
One-time benchmarking isn't enough. Continuous monitoring and AI observability of AI agents in production is critical for maintaining performance as environments change. Real-time dashboards and alerts help detect performance drops, drift, emerging biases, or unexpected behaviors.
To set up continuous monitoring, build automated evaluation pipelines that regularly test agents against benchmark datasets, providing ongoing performance assessment against established baselines. Deploy real-time analytics to support these pipelines. Galileo provides continuous monitoring and tracking performance in production.
Create feedback loops with end-users to complement automated systems, offering valuable insights into real-world performance and highlighting improvement areas that automated metrics might miss.
Use A/B testing alongside feedback loops to evaluate model improvements, letting you compare new versions against existing ones in live environments, confirming that changes truly enhance performance. Add drift detection algorithms to strengthen your monitoring suite, spotting when your AI agent's performance starts deviating from expected norms.
This matters especially as usage patterns and data distributions evolve. These continuous monitoring strategies work together to help you identify and fix issues before they impact users. Your AI agents will maintain high performance and reliability even in dynamic, real-world environments.
Elevate Your AI Agent Performance with Galileo
Comprehensive benchmarking of AI agents is crucial for enterprise success in today's AI landscape. Effective benchmarking ensures optimal performance, drives innovation, enhances decision-making, and builds trust in AI systems. The challenges of benchmarking AI agents in dynamic, real-world environments are significant, but so are the opportunities for improvement.
Galileo leads the AI agent benchmarking field with powerful tools and methodologies:
Comprehensive Evaluation Framework: Galileo offers robust benchmarking tools that assess AI agents across multiple dimensions, including accuracy, efficiency, and adaptability. This holistic approach ensures your agents are ready for real-world deployment.
Real-Time Monitoring and Adaptation: Galileo provides continuous monitoring of your AI agents, detecting performance issues, drift, or unexpected behaviors as they occur. This real-time insight enables rapid adjustments.
Standardized Benchmarking Protocols: Galileo delivers standardized benchmarking methodologies that enhance reproducibility and comparability across different AI systems. This standardization builds trust and supports scalable enterprise adoption.
Cost-Performance Optimization: With advanced techniques, Galileo helps balance trade-offs between model accuracy and resource usage. This ensures your AI solutions deliver high performance while remaining cost-effective.
Explore Galileo to enhance your AI agent benchmarking capabilities and take your AI initiatives to new heights.
Conor Bronsdon
Conor Bronsdon
Conor Bronsdon
Conor Bronsdon