Assessing Agentic AI Project Viability

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.

Generative AI is transforming industries, but as adoption grows, so does the need for trust and reliability. Evaluation frameworks ensure that generative AI models perform as intended—not just in controlled environments, but in the real world. Key Insights from GCP Blog : Scalable Evaluation - new batch evaluation API allows you to assess large datasets efficiently, making it easier to validate model performance at scale. Customizable Autoraters - Benchmark automated raters against human judgments to build confidence in your evaluation process and highlight areas for improvement. Agentic Workflow Assessment - For AI agents, evaluate not just the final output, but also the reasoning process, tool usage, and decision trajectory. Continuous Monitoring - Implement ongoing evaluation to detect performance drift and ensure models remain reliable as data and user needs evolve. - Key Security Considerations: - Data Privacy: Ensure models do not leak sensitive information and comply with data protection regulations - Bias and Fairness: Regularly test for unintended bias and implement mitigation strategies[3]. - Access Controls:Restrict model access and implement audit trails to track usage and changes. - Adversarial Testing:Simulate attacks to identify vulnerabilities and strengthen model robustness **My Perspective: ** I see robust evaluation and security as the twin pillars of trustworthy AI. #Agent Evaluation is Evolving : Modern AI agent evaluation goes beyond simple output checks. It now includes programmatic assertions, embedding-based similarity scoring, and grading the reasoning path—ensuring agents not only answer correctly but also think logically and adapt to edge cases. Automated evaluation frameworks, augmented by human-in-the-loop reviewers, bring both scale and nuance to the process. - Security is a Lifecycle Concern: Leading frameworks like OWASP Top 10 for LLMs, Google’s Secure AI Framework (SAIF), and NIST’s AI Risk Management Framework emphasize security by design—from initial development through deployment and ongoing monitoring. Customizing AI architecture, hardening models against adversarial attacks, and prioritizing input sanitization are now standard best practices. - Continuous Improvement: The best teams integrate evaluation and security into every stage of the AI lifecycle, using continuous monitoring, anomaly detection, and regular threat modeling to stay ahead of risks and maintain high performance. - Benchmarking and Transparency: Standardized benchmarks and clear evaluation criteria not only drive innovation but also foster transparency and reproducibility—key factors for building trust with users and stakeholders. Check GCP blog post here: [How to Evaluate Your Gen AI at Every Stage](https://lnkd.in/gDkfzBs8) How are you ensuring your AI solutions are both reliable and secure?

Are you making a choice about the best LLM to use for building your AI Agent? You may have seen many benchmarks that reflect performance on math problems, exam papers and language reasoning but what about building AI Agents and practical use-cases? Very few test real agents doing real work. I found this great AI Agent Leaderboard developed by Galileo that solves that gap! This is the closest we are to measuring real-world model performance. Why does this Matter ⁉️ Most AI Agents are already being tasked with booking appointments, processing documents, and making decisions in workflows. But most current benchmarks don’t measure whether agents can actually do this well. They focus on static academic tasks like MMLU or GSM8K not on what happens in production environments. The Galileo Agent Leaderboard measures what truly matters when you deploy agents: → Tool Selection Quality (TSQ) – Can the agent choose the right tool and parameters? → Action Completion (AC) – Can the agent actually finish a multi-step task correctly, across domains like banking, healthcare, telecom, and insurance? It’s one of the first benchmarks that combines accuracy, safety, and cost-effectiveness for agents operating in real-world business workflows. Why is this important for you ⁉️ If you’re building with AI agents, this helps you answer critical questions: → Which model handles tool use and decision-making best? → How do different models compare in completing full tasks, not just responding with text? → What are the trade-offs between model cost, task completion, and reliability? Galileo has also open-sourced parts of the evaluation stack, making it easier for teams to run their own assessments. My favourite feature - The ability to filter the leaderboard by industry such as banking, investment and healthcare. If you’re working on agent systems and are leading an organization interested in deploying agents in production, this is a benchmark worth checking out. #AI #AgenticAI #Agents #LLM #AIEngineering #AutonomousAgents #EnterpriseAI #GalileoAI #AIinProduction #GalileoPartner

Most AI agent projects struggle—long before they launch. Not because of poor models. But because evaluation is often an afterthought. I recently came across a thoughtful breakdown by Aurimas Griciūnas, the author of the SwirlAI newsletter, who’s spent the past two years building Agentic Systems. He shares a practical step-by-step framework for Evaluation-Driven Development—something I think more PMs and AI builders should explore: 🔹 How to go from idea → prototype → PoC → MVP → production 🔹 How to define eval rules before writing a single prompt 🔹 How to align input/output metrics with business value 🔹 How to set up observability and evolve your agent with failing evals 🔹 Why a good LLM PoC can be... just an Excel spreadsheet If you’re experimenting with LLM-based applications or trying to make GenAI work in the real world, this guide offers a lot of clarity. 💬 One thing that stuck with me: Evaluation isn’t the final step. It’s the foundation. You can check out the full post in his newsletter: https://lnkd.in/e288drdU It’s a great read for anyone building or evaluating agent-based systems. — How have you tried using evals to guide iteration in your AI projects? What’s been most helpful—or most surprising? 👇 Would love to hear how others are approaching this. #AI #ProductManagement #ProductDevelopment #Eval #AgenticSystems