Jan 21, 2025
Webinar – Lifting the Lid on AI Agents: Exposing Performance Through Evals
Shohil Kothari
Head of Growth
Shohil Kothari
Head of Growth
AI agents are beginning to transform industries from customer service to manufacturing, but understanding and improving agent decision-making remains a major challenge.
Agents often operate as black boxes, making tool selections — i.e., choosing which APIs, knowledge bases, or even other models to use — without clear reasoning. Traditional debugging methods fall short because we can’t fully decode these choices. Instead, we must expose agent behavior through structured evaluations, using data-driven diagnostics to assess performance and refine decision-making.
Watch our webinar to learn:
Why agent tool selection is opaque – How and why AI agents make decisions without explicit transparency.
How to evaluate tool selection – Using systematic evaluations to understand both individual tool effectiveness and system-wide performance.
Common pitfalls and real-world case studies – Where agentic workflows break down and how industries are applying these insights in areas like AI-driven customer support, autonomous research, and industrial automation.
Ways to optimize decision-making through evaluation – How feedback loops, error tracing, and performance benchmarking drive continuous improvements.
AI agents are beginning to transform industries from customer service to manufacturing, but understanding and improving agent decision-making remains a major challenge.
Agents often operate as black boxes, making tool selections — i.e., choosing which APIs, knowledge bases, or even other models to use — without clear reasoning. Traditional debugging methods fall short because we can’t fully decode these choices. Instead, we must expose agent behavior through structured evaluations, using data-driven diagnostics to assess performance and refine decision-making.
Watch our webinar to learn:
Why agent tool selection is opaque – How and why AI agents make decisions without explicit transparency.
How to evaluate tool selection – Using systematic evaluations to understand both individual tool effectiveness and system-wide performance.
Common pitfalls and real-world case studies – Where agentic workflows break down and how industries are applying these insights in areas like AI-driven customer support, autonomous research, and industrial automation.
Ways to optimize decision-making through evaluation – How feedback loops, error tracing, and performance benchmarking drive continuous improvements.
AI agents are beginning to transform industries from customer service to manufacturing, but understanding and improving agent decision-making remains a major challenge.
Agents often operate as black boxes, making tool selections — i.e., choosing which APIs, knowledge bases, or even other models to use — without clear reasoning. Traditional debugging methods fall short because we can’t fully decode these choices. Instead, we must expose agent behavior through structured evaluations, using data-driven diagnostics to assess performance and refine decision-making.
Watch our webinar to learn:
Why agent tool selection is opaque – How and why AI agents make decisions without explicit transparency.
How to evaluate tool selection – Using systematic evaluations to understand both individual tool effectiveness and system-wide performance.
Common pitfalls and real-world case studies – Where agentic workflows break down and how industries are applying these insights in areas like AI-driven customer support, autonomous research, and industrial automation.
Ways to optimize decision-making through evaluation – How feedback loops, error tracing, and performance benchmarking drive continuous improvements.
AI agents are beginning to transform industries from customer service to manufacturing, but understanding and improving agent decision-making remains a major challenge.
Agents often operate as black boxes, making tool selections — i.e., choosing which APIs, knowledge bases, or even other models to use — without clear reasoning. Traditional debugging methods fall short because we can’t fully decode these choices. Instead, we must expose agent behavior through structured evaluations, using data-driven diagnostics to assess performance and refine decision-making.
Watch our webinar to learn:
Why agent tool selection is opaque – How and why AI agents make decisions without explicit transparency.
How to evaluate tool selection – Using systematic evaluations to understand both individual tool effectiveness and system-wide performance.
Common pitfalls and real-world case studies – Where agentic workflows break down and how industries are applying these insights in areas like AI-driven customer support, autonomous research, and industrial automation.
Ways to optimize decision-making through evaluation – How feedback loops, error tracing, and performance benchmarking drive continuous improvements.