I am leading GTM adventures in AI, Insurance and iBanking. Building new and marvelous cloud apps and systems to make customers, advisors and agents lives easier. AI ++
Context//Building Smarter and More Resilient Systems: The Future of Testing in Software Development 🚀 --- The trend toward smarter, integrated testing for production applications is unmistakable—and it’s heading in the right direction. If you're building production-grade applications today, robust methods for evaluation and testing are no longer optional. They’re essential. These methods should include: 📊 Benchmark datasets 🛠️ Rubrics for control 📉 Metrics for speed, latency, and error rates at varying levels of scale 👨💻 which AI-framework(s)and LLM(s)will be used? When someone pitches you a new technology for a proof of concept (POC), here’s a checklist to guide your response: 🔍 What is their evaluation tooling? 📂 Which datasets will they make available for testing? How are those data sets be kept up to date? 🤖 What’s their automated testing strategy? Testing can’t be a “hand-off” to another team anymore. Modern systems must be inherently test-driven, with testing as a service built right into their architecture. The goal? Systems that: ✅ Continuously test themselves as they evolve ✅ Monitor their own performance ✅ Inform owners of instability—or better yet, fix themselves Artificial intelligence is the new leverage here, enabling the transformation of raw code into reliable, tested systems with quality engineered into every line. This isn’t about traditional A/B testing; this is "A-to-Z testing", covering every angle of system behavior and performance. this type of testing can go right out to every user and be distributed statistically in the system to help with it's self-improvement mechanisms. this type of testing replaces ~90% of manual testing for software. This Business Software Architecture Thinking is beyond the functionality being implemented. It's embedded meta-infrastructure. This is focusing on how the software will scale and roll out to your production system and thousands if not millions of users. The approach assumes that systems will change as the business changes. And, it also assumes that regression testing is necessary from time to time. But here’s the catch: you can’t just bolt this set of capabilities onto legacy systems. This new architecture likely requires a ground-up approach, and while legacy vendors might claim otherwise, bolting on these capabilities will rarely be effective. The fundamental problem about legacy architectures, especially those not based on micro•services, is that they are just too hard to retrofit for this method without restructuring the stores and flows of data across the system and it's rules and functions. The future belongs to systems engineered for reliability, adaptability, and intelligence. Auto-Testing isn't just a step in the process anymore—it is the process. 💡 Ready to embrace the shift? Let’s discuss how your business can stay ahead of the curve. #SoftwareDevelopment #ArtificialIntelligence #Innovation #Testing #Agile #Technology #Insurance #Engineering #iaadvice
Founder at JUTEQ | Empowering Businesses through Cloud Transformation & Solutions | Specializing in Cloud Architecture & Consultation | Generative AI | Entrepreneurship & Leadership | Let's connect & innovate together!🌟
Your AI agents might be fast, but are they efficient and accurate? Here's how to evaluate your AI Agents... Evaluating AI agents is crucial for building effective agentic applications. Given their complex architecture, different aspects require specific metrics and tools for evaluation. Let’s dive into key metrics to track: 📌 Technical Performance (For Engineers): Track how efficiently your agents handle tasks at the technical level: ↳ Latency per Tool Call - Measures the time taken for tool interactions ↳ API Call Frequency - Tracks the number of external API calls ↳ Context Window Utilization - Examines how well LLMs manage their context. ↳ LLM Call Error Rate - Evaluates the frequency of failures in model responses to address issues like limits or misaligned prompts. 📌 Cost and Resource Optimization (For Business Leaders) Evaluate cost efficiency and resource usage to ensure scalability: ↳ Total Task Completion Time - Tracks the overall time required for task completion, highlighting bottlenecks ↳ Cost per Task Completion - Measures financial resources spent per task ↳ Token Usage per Interaction - Monitors token consumption to optimize payloads and lower costs 📌 Output Quality (For Quality Assurance Teams) Ensure the outputs generated meet the required standards: ↳ Instruction Adherence - Validates compliance with task specifications to reduce errors. ↳ Hallucination Rate - How often an AI generates incorrect, irrelevant, or nonsensical outputs. ↳ Output Format Success Rate - Ensures the structure of outputs (e.g., JSON, CSV) is accurate, preventing compatibility ↳ Context Adherence - Assesses if responses align with input context 📌 Usability and Effectiveness (For Product Owners) Measure how well your agents meet user needs and achieve goals. ↳ Agent Success Rate - Tracks the percentage of Agentic tasks completed successfully. ↳ Event Recall Accuracy - Measures the accuracy of the agent's episodic memory recall ↳ Agent Wait Time - Measures the time an agent waits for a task, tool, or resource. ↳ Task Completion Rate - Monitors the ratio of tasks started versus completed. ↳ Steps per Task - Counts steps needed for task completion, highlighting inefficiencies. ↳ Number of Human Requests - Measures the frequency of user intervention to address gaps in automation. ↳ Tool Selection Accuracy - Assesses if agents choose appropriate tools for tasks. ↳ Tool Argument Accuracy - Validates the correctness of tool input parameters. ↳ Tool Failure Rate - Monitors tool failures to identify and fix unreliable components. Note: Not all metrics are necessary for every use case. Select those aligned with your specific objectives. What metrics are you prioritizing when evaluating AI agents? Let me know in the comments below 👇 Please make sure to, ♻️ Share 👍 React 💭 Comment to help more people learn
