Best Practices to Navigate the Complexities of Evaluating AI Agents
AI is moving from simple conversation tools to robust systems driving automation in various industries. A recent "Chain of Thought" podcast episode hosted by Conor Bronsdon, Head of Developer Awareness at Galileo, Yash Sheth, Chief Operating Officer at Galileo, and Atindriyo Sanyal, Chief Technology Officer at Galileo, explored the transformative potential of AI in 2025.
This evolution extends beyond conversational AI to autonomous agents capable of handling complex tasks and making decisions independently, integrating seamlessly into workflows across industries from healthcare to finance.
The conversation revealed how AI development is entering a new paradigm where agent-based systems will fundamentally change how businesses operate and create value.
The Rising Demand for AI Agents
As we enter 2025, AI stands at a pivotal turning point. It is rapidly evolving beyond simple conversation interfaces toward autonomous systems capable of executing complex tasks independently.
This shift represents a fundamental change in how businesses leverage AI technology, moving from systems that merely respond to queries to AI agents that perform meaningful actions with minimal human supervision.
Moving Beyond Conversational AI
What began as a quest to make machines conversational is now evolving into automated systems that don't just talk, but act. This shift represents not merely a technological advancement but a fundamental reimagining of how businesses operate and how value is created in the digital economy.
"The real ROI is going to come from leveraging this technology to automate so many workflows out there across industries," notes Sheth. Traditionally, AI has focused on making interactions more conversational, but its role is now expanding beyond communication. This demonstrates the transformative role of AI in modern businesses.
This evolution means AI agents are transforming from passive responders to active doers. They will not only interpret data but also perform complex actions, delivering results that support strategic business goals. With multimodal capabilities, AI can now handle more than just text, processing images and audio as well.
The Maturation of AI Tool Stacks
A key factor in this transition is the maturation of AI tool stacks—what Sanyal calls moving "towards product tool stack fit." The industry is transitioning from prototypes to systems that are robust and ready for real-world use.
The tools needed to deploy these advanced agents are improving quickly, with faster model inference and reduced token generation times playing significant roles. The maturation of AI tool stacks includes considerations of AI agent architecture, as well as faster model inference and reduced token generation times.
As businesses need to demonstrate ROI, these advancements ensure efficiency and productivity are achievable. AI's ability to handle tasks like API calls, code execution, and large-scale data processing means businesses aren't just adopting AI—they're rethinking their workflows entirely.
Whether it's code automation or transforming legacy systems, the maturation of AI infrastructure points to a future where software development is fundamentally changed. Every piece of software will likely have AI components integrated into its functionality, delivering unprecedented operational efficiencies and continually expanding what's possible.
The Technical Challenges of Evaluating AI Agent Systems
Today's AI development resembles the early days of traditional software development in the 1980s, with evaluation tools still in their infancy. As AI advances, agent-based systems present unique technical AI agent evaluation challenges that distinguish them from simpler generative AI applications.
Action and Query Evaluations
Evaluating agent-based systems goes beyond the traditional query-response model. Shifting from assessing text responses to examining action-driven results requires metrics that evaluate not only the correctness of actions but also the order and context in which they occur.
The distinction between action and query evaluation is profound. In query evaluation, success is measured by the relevance and accuracy of the information provided. However, with agent-based systems, evaluation must consider the entire sequence of actions taken to accomplish a task. Did the agent choose the correct APIs to call? Did it make those calls in a logical order? Did it properly interpret the results of each call to inform subsequent actions?
Moreover, agent evaluation often requires understanding the intentionality behind actions. An agent might technically perform the correct sequence of operations but do so for the wrong reasons, indicating a fundamental misunderstanding that could lead to failures in slightly different scenarios.
Challenges in Accuracy
Accuracy in this context extends beyond the quality of a generated response to ensure an agent's actions are appropriate and reliable in real-time. This requires strong mechanisms for ongoing assessment and fine-tuning of metrics to keep them relevant as applications evolve. These agent development challenges become particularly acute when dealing with diverse environments where agents must operate.
The challenge becomes particularly acute when dealing with diverse environments where agents must operate. Unlike controlled development environments, real-world scenarios present unpredictable variations that can test an agent's decision-making capabilities.
Additionally, establishing ground truth for agent actions is difficult. In text generation, human evaluators can relatively easily judge whether an answer is correct or helpful. But for complex agent workflows involving dozens of decision points and potential action paths, determining the "correct" approach becomes significantly more challenging, especially when multiple valid solution paths exist.
Temporal Dynamics and State Management
Agent-based systems introduce temporal dynamics and state management into evaluation frameworks. Unlike stateless query systems, agents maintain an understanding of the current state of their task and environment, making decisions based on both immediate inputs and historical context. Effective performance testing for AI agents must account for these temporal dynamics and state management challenges.
This state management becomes particularly challenging when agents operate over extended periods or across multiple sessions. An agent might need to remember user preferences, previous interactions, or the current stage of a multi-step process. Evaluation frameworks must, therefore, assess not just individual actions but the agent's ability to maintain coherence across time.
The temporal aspect also introduces questions about efficiency and optimization. Is the agent completing tasks in the most direct way possible? Is it unnecessarily repeating actions or failing to leverage previously gathered information?
Multi-Objective Optimization Challenges
Agent systems often need to balance multiple competing objectives simultaneously, creating evaluation challenges not present in simpler AI applications. An effective agent might need to optimize for accuracy, efficiency, cost, user satisfaction, and safety all at once.
For example, an agent might need to decide whether to use a more accurate but computationally expensive approach or a faster but potentially less reliable method. The "correct" choice depends on the specific context and priority of the task.
Evaluation systems must be sophisticated enough to recognize these nuanced trade-offs and assess agent performance within the appropriate context-specific value framework.
This multi-objective nature also complicates the development process itself. Without clear evaluation frameworks that can quantify performance across various dimensions, teams risk optimizing for easily measured metrics at the expense of harder-to-quantify but equally important objectives.
Galileo's Role in Enhancing AI Agents Evaluations
As AI becomes more integral to our daily software interactions, platforms like Galileo are crucial in advancing how we evaluate AI agents. With advanced evaluation tools, Galileo addresses the growing need for accurate, adaptable, and easy-to-implement metrics essential for assessing complex AI agent systems, enabling real-world AI performance evaluation.
The Agent Evaluation Framework
To fully utilize AI's potential, especially in agentic applications, businesses need robust agent evaluation systems that go beyond traditional methods. Galileo's framework offers precise metrics that effectively evaluate agentic processes like API calls and code executions.
This framework incorporates multiple evaluation dimensions, including action correctness, sequence logic, and outcome achievement. This multi-faceted approach allows developers to gain comprehensive insights into their agents' performance beyond what traditional evaluation methods provide.
A standout feature is Galileo's ability to trace and evaluate entire chains of actions. Rather than treating each API call or function execution as an isolated event, the system analyzes the logical flow between actions, identifying potential inefficiencies or logical contradictions in the agent's decision-making process.
The framework also incorporates simulation capabilities that allow developers to test agents against various scenarios without real-world consequences. These controlled testing environments enable teams to stress-test their agents against edge cases and unexpected inputs, uncovering potential failure modes before deployment.
The Importance of Adaptable and Customizable Metrics
Galileo recognizes that AI tasks vary significantly in complexity and purpose, which is why it offers customizable evaluation metrics. "There's no two tasks which are exactly the same," notes Sanyal, highlighting the platform's ability to tailor metrics to specific application needs. This flexibility is vital as tasks change with user interactions, requiring real-time adjustments to maintain accuracy.
Galileo's adaptability extends to managing the changing data environments that AI agents operate in. Galileo can identify shifts in operational data and adjust evaluations accordingly, ensuring system performance remains optimized.
The customizable metrics framework allows organizations to define evaluation criteria that directly align with their specific business objectives and use cases. Rather than forcing all applications into standardized evaluation approaches, Galileo provides the tools for teams to create domain-specific metrics that capture the nuances of their particular implementation.
Galileo also offers a library of pre-built evaluation templates that can be modified and extended, providing starting points for common agent evaluation scenarios. These templates encapsulate industry best practices while remaining flexible enough to adapt to specific requirements.
Real-Time Monitoring and Continuous Evaluation
A critical aspect of Galileo's approach is its emphasis on real-time monitoring and continuous assessment. Traditional evaluation methods often focus on offline testing before deployment, but Galileo recognizes that agent performance must be continuously verified throughout the operational lifecycle.
Galileo's real-time monitoring capabilities allow teams to identify performance degradation or failure modes as they emerge, rather than discovering issues after they've already impacted users.
This approach includes benchmarking AI agents to ensure they perform effectively in real-world tasks. These automated monitoring capabilities reduce the burden on development teams while ensuring that problems are caught quickly, adhering to best practices for AI safety.
Galileo's monitoring systems further include anomaly detection algorithms that can identify unusual patterns in agent behavior or performance metrics, flagging potential issues for human review. These automated monitoring capabilities reduce the burden on development teams while ensuring that problems are caught quickly.
The continuous evaluation approach also supports progressive deployment strategies. Teams can roll out agent updates to limited user segments while closely monitoring performance metrics, expanding the deployment only when metrics confirm that the new version meets or exceeds the performance of the previous one.
Integration with Development Workflows
Understanding that evaluation is most effective when integrated throughout the development process, Galileo has designed its platform to seamlessly fit into existing development workflows. The system provides API access and integrations with popular development tools, making it easy for teams to incorporate evaluation metrics into their CI/CD pipelines.
These integrations enable automated testing and evaluation as part of the regular development cycle, ensuring that every code change is assessed for its impact on agent performance. By shifting evaluation earlier in the development process, teams can identify and address issues before they become embedded in the system architecture.
Galileo also provides developer-friendly visualization tools that make evaluation results accessible and actionable. Rather than presenting raw metrics that require significant expertise to interpret, the platform offers intuitive dashboards and visual reports that highlight key performance indicators and potential areas for improvement.
Building the Trust Layer for AI Applications
As businesses embed AI into their operations, the need for accurate and scalable evaluation tools becomes critical. The concept of a "trust layer" is gaining traction, similar to how security solutions enabled the widespread use of cloud technologies.
At the heart of this trust layer are two key components: precise measurement metrics that accurately quantify application behavior and scalability to implement these metrics in real-time environments. This requires evaluation tools that work efficiently at scale, providing accurate assessments of AI performance without human intervention.
The scalability component ensures these metrics can be used in real-time production settings. "It's vital to measure and enforce application behavior through metrics in real-time," explains Sheth. This involves not only verifying that AI agents execute tasks correctly but also ensuring they operate within the right parameters. With a scalable evaluation system, companies can integrate AI into their workflows safely and efficiently.
Galileo aims to build this trust layer by providing precise measurement tools and maintaining scalability. This ensures businesses can rely on their AI applications' reliability, unlocking AI's full potential.
The Path Forward for AI Agents Development and Evaluation
Adopting evaluation-driven development represents a revolutionary shift in crafting AI agents. Guided by advanced metrics and evaluation frameworks, this approach moves away from traditional software development, promising AI systems that are not just efficient but also exceptionally reliable.
As the next generation of AI applications emerges, sophisticated evaluation tools will be crucial. They will be the machinery that navigates the new era of AI software, moving from basic tools to precise, real-time evaluation and monitoring systems. Learn more about how Galileo is at the forefront of this movement, providing the essential evaluation intelligence needed for this evolution.
Tune into the rest of the conversation where Conor, Yash, and Atindriyo dive deeper into evaluation frameworks, the technical challenges of agent systems, and how businesses can prepare for AI automation in 2025.
And check out other Chain of Thought episodes, where we discuss Generative AI for software engineers and AI leaders through stories, strategies, and practical techniques.