How to Evaluate AI Performance in Complex Tasks

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.

Reliability, evaluation, and “hallucination anxiety” are where most AI programmes quietly stall. Not because the model is weak. Because the system around it is not built to scale trust. When companies move beyond demos, three hard questions appear: →Can we rely on this output? →Do we know what “good” actually looks like? →How much human oversight is enough? The fix is not better prompting. It is a strategy and operating discipline. 𝐅𝐢𝐫𝐬𝐭: ⁣Define reliability like a product, not a vibe. Every serious AI use case should have a one-page SLO sheet with measurable targets across: →Task success ↳Right-first-time rate and rubric-based acceptance →Factual grounding ↳Evidence coverage and unsupported-claim tracking →Safety and compliance ↳Policy violations and PII leakage →Operational quality ↳Latency, cost per task, escalation to humans Now “good” is no longer opinion. It is observable. 𝐒𝐞𝐜𝐨𝐧𝐝:  evaluation must be continuous, not a one-off demo test. Use a simple loop: 𝐏lan: Define rubrics, datasets, and risk tiers 𝐃⁣o: Run offline evaluations and limited pilots 𝐂heck: Monitor drift and regressions weekly 𝐀ct: Update prompts, data, guardrails, and workflows Support this with an AI test pyramid: →Unit checks for prompts and tool behaviour →Scenario tests for real edge failures →Regression benchmarks to prevent backsliding →Live monitoring in production Add statistical control charts, and you can detect silent degradation before users do. 𝐓𝐡𝐢𝐫𝐝: reduce hallucinations by design. →Run a short failure-mode workshop and engineer controls: →Require retrieval or evidence before answering →Allow safe abstention instead of confident guessing →Add claim checking and tool validation →Use structured intake and clarifying flows You are not asking the model to behave. You are designing a system that expects failure and contains it. 𝐅𝐨𝐮𝐫𝐭𝐡: make human-in-the-loop affordable. Tier risk: →Low risk: Light sampling →Medium risk: Triggered review →High risk: Mandatory approval Escalate only when signals demand it: low confidence, missing evidence, policy flags, or novelty spikes. Review becomes targeted, fast, and a source of improvement data. 𝐅𝐢𝐧𝐚𝐥𝐥𝐲: Operate it like a capability. Track outcomes, risk, delivery speed, and cost on a single dashboard. Hold a short weekly reliability stand-up focused on regressions, failure modes, and ownership. What you end up with is simple: ↳Use case catalogue with risk tiers ↳Clear SLOs and error budgets ↳Continuous evaluation harness ↳Built-in controls ↳Targeted human review ↳Reliability cadence AI does not scale on intelligence alone. It scales on measurable trust. ♻️ Share if you found thisuseful. ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI #AI #AIReliability #TrustAtScale #OperationalExcellence

We recently published Part 2 of our GenAI Evaluation series at Booking.com: A deep dive into AI Agents. Evaluating a standard LLM is hard, but evaluating an Agent is exponentially harder. Agents don't just generate text; they plan, loop, and execute tool calls. This means "good output" isn't enough anymore—you have to evaluate the logic behind the actions. Here is the playbook we’ve built to manage this complexity: - Black Box vs. Glass Box: You can't rely solely on the final answer (Black Box). You need to audit the intermediate steps (Glass Box). Did the agent fail because of a model limitation, or did the tool itself return an error that confused the context? - Debug your Tool Specs, not just the Model: Often, the "bug" isn't in the LLM—it's in your documentation. We use specific "Judge-LLMs" to score the quality of our tool names and descriptions. Clearer docs = smarter agents. - The "Tool Proficiency" Check: We split this into two: Validity (is the syntax executable?) and Correctness (did it actually need to call the Flight API, or could it have just answered?). - Don't skip the Baseline: Agents are expensive and slow. Always benchmark against a simple zero-shot prompt or a deterministic flow. If the Agent isn't significantly outperforming the cheap baseline, the complexity isn’t justified. - Consistency is the new Accuracy: Agents are non-deterministic loops. We measure pass^k (probability of success across k trials) to catch flaky behaviors that single-run tests miss. We share the exact protocols, metrics, and "Glass Box" methodologies we use to deploy reliable Agents. Read the full post here: https://lnkd.in/ende8RXg Missed Part 1? Catch up on our guide to standard LLM evaluation here: https://lnkd.in/dmaTWiMB Big thanks to Zeno Belligoli & Antonio Castelli for pushing this work forward. If you are building Agents: do you evaluate your tool documentation? #AIAgents #LLM #MachineLearning #GenAI #BookingAI #Evaluation 

🧠 Don’t Just Build AI Agents. Evaluate Them Ruthlessly. Everyone’s shipping agents. Few are measuring them. In the rush to integrate agentic AI into clinical operations, we’re missing a critical step: 👉 Evaluations — the disciplined, structured process of testing whether your AI actually delivers value. As Andrew Ng puts it, “Disciplined evals are the single biggest predictor of agentic AI progress.” Yet in life sciences, evaluations are often: 🫥 Vague 💭 Subjective 🧪 Done too late Let’s fix that. Here’s why it matters. 👇 💡 What is Agentic AI? Unlike single-shot prompts, Agentic AI chains together multiple steps, tools, or models to complete complex tasks. Think of them as junior team members with a task list and tools at hand. In clinical settings, these agents now support: ✍️ Medical writing and protocol drafting 📄 Document abstraction and QC 💬 Site communication bots 🧪 Lab data ingestion 📈 Feasibility analysis 🧍♂️ Patient concierge agents But if we don't evaluate their work like we would a new team member, we're flying blind. 🔍 Why Evaluations Are the Backbone of AI Readiness Let’s say your agent helps draft a clinical study synopsis. Great — but how do you know if it got the population, endpoint, or visit structure right? Without evaluations, you risk: ❌ Bad data entering downstream systems ❌ Increased human review costs ❌ Regulatory risk and rework ❌ False confidence in automation Evaluations act like clinical QA for your AI — a must-have, not a nice-to-have. Use a mix of: 🧑⚖️ Human spot checks 🤖 Automated schema checks 🧠 LLM-as-Judge evaluations 📌 Start early. Don’t wait until deployment — bake this into your prototype phase. 💥 Takeaways ✅ Agentic AI is only as strong as the evaluations behind it 🛑 Don’t ship agents without defining what “good” looks like 🔬 Clinical use cases need contextual, field-aware evaluation plans 🧠 Focus on structured output, factual accuracy, and safety 📈 Better evals = faster iteration, lower risk, higher ROI 💬 Let’s Talk Are you evaluating your agents before you trust them? Drop your eval tactics, tools, or hard-won lessons in the comments. Let’s crowdsource the Agentic AI QA Playbook for our industry. 🏷️ Hashtags #AgenticAI #AIevaluations #ClinicalAI #GenerativeAI #ResponsibleAI

What if an AI could evaluate a physician’s reasoning on a complex case as well as another physician? 🤔 That’s the question behind our new paper, "Automated Evaluation of Large Language Model Response Concordance with Human Specialist Responses on Physician-to-Physician eConsult Cases" led by David JH Wu and accepted to Pacific Symposium on Biocomputing 2026. His behind-the-scenes piece for our ARiSE Blog captures not just the science, but also the curiosity and wonder that drove it. Why it matters: Specialist eConsults are where real-world clinical nuance lives: complex questions that don’t fit standard guidelines. Evaluating how well AI handles these isn’t easy; it usually takes hours of expert review per case. Our study tested whether LLMs could do that evaluation themselves. We found that an “LLM-as-Judge” approach reached human-level accuracy (κ = 0.75) in judging whether an AI’s answer agreed with a specialist’s. That means scalable, low-cost evaluation of medical AI may finally be possible, allowing faster, safer iteration on models that support real clinicians. David’s reflection goes deeper, exploring what it feels like to work with “intelligence that isn’t alive,” and how it challenges our sense of what good medicine means. Dive into the full paper and David’s behind-the-scenes reflection → [links in comments]