Why automation should focus on confidence not coverage

𝐈𝐟 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐜𝐚𝐧’𝐭 𝐬𝐚𝐲 "𝐈 𝐝𝐨𝐧’𝐭 𝐤𝐧𝐨𝐰," 𝐢𝐭’𝐬 𝐝𝐚𝐧𝐠𝐞𝐫𝐨𝐮𝐬. Confidence without 𝐜𝐚𝐥𝐢𝐛𝐫𝐚𝐭𝐢𝐨𝐧 creates 𝐫𝐢𝐬𝐤, 𝐝𝐞𝐛𝐭, and 𝐫𝐞𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐝𝐚𝐦𝐚𝐠𝐞. The best systems know their limits and escalate to humans gracefully. 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Teach abstention with uncertainty estimates, retrieval gaps, and explicit policies. Use signals like entropy, consensus, or model disagreement to abstain. Require sources for critical claims; block actions if citations are stale or untrusted. Design escalation paths that show rationale, alternatives, and risks, not noise. Train with counterfactuals to explicitly discourage overreach. 𝐂𝐚𝐬𝐞 𝐢𝐧 𝐩𝐨𝐢𝐧𝐭 (𝐡𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞): Agents drafted discharge plans but withheld when vitals/orders conflicted. Nurses reviewed flagged cases with clear rationale + sources. ↳ Errors dropped ↳ Trust increased ↳ Uncertainty became actionable 𝐑𝐞𝐬𝐮𝐥𝐭: Saying "𝐈 𝐝𝐨𝐧’𝐭 𝐤𝐧𝐨𝐰" turned into a safety feature customers valued. → Where should your AI choose caution over confidence next, and why? Let’s make reliability the habit competitors can’t copy at scale. ♻️ Repost to your LinkedIn empower your network & follow Timothy Goebel for expert insights #GenerativeAI #EnterpriseAI #AIProductManagement #LLMAgents #ResponsibleAI

Too many teams treat testing as a metric rather than an opportunity. A developer is told to write tests, so they do the bare minimum to hit the required coverage percentage. A function runs inside a unit test, the coverage tool marks it as covered, and the developer moves on. The percentage goes up, leadership is satisfied, and the codebase is left with the illusion of quality. But what was actually tested? Too often, the answer is: almost nothing. The logic was executed, but its behavior was never challenged. The function was called, but its failure modes were ignored. The edge cases, error handling, and real-world complexity were never explored. The opportunity to truly exercise the code and ensure it works in every scenario was completely missed. This is a systemic failure in how organizations think about testing. Instead of seeing unit, integration, and end-to-end (E2E) testing as distinct silos, they should recognize that all testing is just exercising the same code. The farther you get from the code, the harder and more expensive it becomes to test. If logic is effectively tested at the unit and integration level, it does not suddenly behave differently at the E2E level. Software is a rational system. A well-tested function does not magically start failing in production unless something external—such as infrastructure or dependencies—introduces instability. When developers treat unit and integration testing as a checkbox exercise, they push the real burden of testing downstream. Bugs that should have been caught in milliseconds by a unit test are now caught minutes or hours later in an integration test, or even days later during E2E testing. Some are not caught at all until they reach production. Organizations then spend exponentially more time and money debugging issues that should never have existed in the first place. The best engineering teams do not chase code coverage numbers. They see testing as an opportunity to build confidence in their software at the lowest possible level. They write tests that ask hard questions of the code, not just ones that execute it. They recognize that when testing is done well at the unit and integration level, their E2E tests become simpler and more reliable—not a desperate last line of defense against failures that should have been prevented. But the very best testers go even further. They recognize the system for what it truly is—a beautiful, interconnected mosaic of logic, data, and dependencies. They do not just react to failures at the UX/UI layer, desperately trying to stop an avalanche of possible combinations. They seek to understand and control the system itself, shaping it in a way that prevents those avalanches from happening in the first place. Organizations that embrace this mindset build more stable systems, ship with more confidence, and spend less time firefighting production issues. #SoftwareTesting #QualityEngineering

Most teams chase the wrong trophy when designing evals. A spotless dashboard telling you every single test passed feels great, right until that first weird input drags your app off a cliff. Seasoned builders have learned the hard way: coverage numbers measure how many branches got exercised, not whether the tests actually challenge your system where it’s vulnerable. Here’s the thing: coverage tells you which lines ran, not whether your system can take a punch. Let’s break it down. 1. Quit Worshipping 100 % - Thesis: A perfect score masks shallow tests. - Green maps tempt us into “happy-path” assertions that miss logic bombs. - Coverage is a cosmetic metric; depth is the survival metric. - Klaviyo’s GenAI crew gets it, they track eval deltas, not line counts, on every pull request. 2. Curate Tests That Bite - Thesis: Evaluation-driven development celebrates red bars. - Build a brutal suite: messy inputs, adversarial prompts, ambiguous intent. - Run the gauntlet on every commit; gaps show up before users do. - Red means “found a blind spot.” That’s progress, not failure. 3. Lead With Edge Cases - Thesis: Corners, not corridors, break software. - Synthesize rare but plausible scenarios,multilingual tokens, tab-trick SQL, once-a-quarter glitches from your logs. - Automate adversaries: fuzzers and LLM-generated probes surface issues humans skip. - Keep a human eye on nuance; machines give speed, people give judgment. 4. Red Bars → Discussion → Guardrail - Thesis: Maturity is fixing what fails while the rest stays green. - Triage, patch, commit, watch that single red shard flip to green. - Each fix adds a new guardrail; the suite grows only with lessons learned. Core Principles: 1. Coverage ≠ depth. 2. Brutal evals over padded numbers. 3. Edge cases first, always. 4. Automate adversaries; review selectively. 5. Treat failures as free QA. Want to harden your Applied-AI stack? Steal this framework, drop it into your pipeline, and let the evals hunt the scary stuff, before your customers do.

Day 91 of IAM “The Myth of 100% Automation in IAM” In IAM, the pitch is tempting: “Let’s automate everything — provisioning, reviews, deprovisioning, SoD checks…” Sounds ideal, right? But here’s the uncomfortable truth: 100% automation is not just unrealistic — it’s risky. Why? Because automation assumes perfect inputs. But real-world IAM rarely works that way: 🔹 Provisioning is automated — but someone changed roles outside the system. 🔹 Deprovisioning is automated — but HR data wasn’t updated in time. 🔹 Access reviews are automated — but context and usage signals are missing. Automation can amplify risk as easily as it eliminates it. ✅ What Actually Works: 🔹Automate high-volume, low-risk tasks • Birthright access • Non-privileged system provisioning • Credential rotations 🔹 Keep humans in the loop • For privileged access approvals • SoD conflict handling • Policy exceptions and escalations 🔹Use AI/ML for signal detection, not decisions • Flag usage anomalies • Spot dormant or misaligned access 🔹 Build guardrails, not shortcuts • Time-bound access • Mandatory approvals • Expiry and re-certification policies 🔹 Review the automation — don’t “set and forget” IAM isn’t about replacing people, it’s about augmenting their judgment. Smart automation should empower oversight, not eliminate it. So — where do you draw the line in your IAM automation strategy? Drop your lessons and let’s talk real-world. #IAM #IdentityGovernance #AccessReviews #Automation #CyberSaina #CyberTweaks #ZeroTrust #AIinIAM #GIAM Cyber Saina

Stop obsessing about how to get from 60% code coverage to 99%. Code coverage does not guarantee that the covered lines have been tested correctly. Code coverage just guarantees that a test has executed them. Instead, do this. 👇 ✅ Define the testing you need by: - How often will you need to change the code? - Is this feature critical for your users?  - How much longer do you expect the code to live? ✅ Not all tests are equally important. The "Add to Favorites" feature is not as important as "Place Order"; instead of focusing on that coverage number, make sure the most critical code is covered. ✅ Low level, no problem. A low code coverage number guarantees that large product areas are going completely untested. But even though you inherited a legacy code base with poor testing, you can still change it. Adopt the boy-scout rule; you will get to a healthy location one test at a time. ✅ Unit test is just one piece. Integration and System test code coverage are important. ✅ Make TDD a habit; testing upfront will let you: - Measure progress - Understand requirements - It will work as documentation for other developers ✅ Automation matter At some point, "Bob" will not run all the tests while shipping that code to production. - Run your tests as part of the pipeline - Stop the deployment if there are failing tests - Check the trending on the coverage Will you let a number define the quality of your tests? 😅

Building Trust in Agentic Experiences Years ago, one of my first automation projects was in a bank. We built a system to automate a back-office workflow. It worked flawlessly, and the MVP was a success on paper. But adoption was low. The back office team didn’t trust it. They kept asking for a notification to confirm when the job was done. The system already sent alerts when it failed as silence meant success. But no matter how clearly we explained that logic, users still wanted reassurance. Eventually, we built the confirmation notification anyway. That experience taught me something I keep coming back to: trust in automation isn’t about accuracy in getting the job done. Fast forward to today, as we build agentic systems that can reason, decide, and act with less predictability. The same challenge remains, just on a new scale. When users can’t see how an agent reached its conclusion or don’t know how to validate its work, the gap isn’t technical; it’s emotional. So, while Evaluation frameworks are key in ensuring the quality of agent work but they are not sufficient in earning users trust. From experimenting with various agentic products and my personal experience in building agents, I’ve noticed a few design patterns that help close that gap: Show your work: Let users see what’s happening behind the scenes. Transparency creates confidence. Search agents have been pioneer in this pattern. Ask for confirmation wisely: autonomous agents feel more reliable when they pause at key points for user confirmation. Claude Code does it well. Allow undo: people need a way to reverse mistakes. I have not seen any app that does it well. For example all coding agents offer Undo, but sometimes they mess up the code, specially for novice users like me. Set guardrails: Let users define what the agent can and can’t do. Customer Service agents do it great by enabling users to define operational playbooks for the agent. I can see “agent playbook writing” becoming a critical operational skill. In the end, it’s the same story I lived years ago in that bank: even when the system works perfectly, people still want to see it, feel it, and trust it. That small "job completed" notification we built back then was not just another feature. It was a lesson learned in how to build trust in automation.

Why do AI models hallucinate?🤔 OpenAI's latest research paper reveals why AI systems confidently provide incorrect answers, and it changes everything about enterprise AI strategy. Research shows language models don't hallucinate because they're broken. They hallucinate because we trained them to guess confidently rather than admit uncertainty. Think about it: On a multiple-choice test, guessing might get you points. Leaving it blank guarantees zero. Our AI evaluation systems work the same way, rewarding confident wrong answers over honest "I don't know" responses. Most companies select AI using accuracy benchmarks that literally reward the behavior that destroys trust. We're optimizing for confident guessing instead of reliable uncertainty. This creates a massive blind spot for AI-Native organizations: → Strategic decisions based on confident but incorrect AI analysis → Compliance risks from fabricated but authoritative-sounding guidance → Employee trust erosion when AI confidently delivers false information → Legal liability from AI hallucinations in customer-facing applications. The real test for AI, especially agentic systems, isn’t how fast they respond, but whether they know when to hold back. Enterprise adoption won’t be driven by new features or raw speed. It will be driven by trust, the ability of agents to signal doubt as confidently as they deliver answers📈 At Beam AI, we tackle hallucinations by combining structured workflows with agent reasoning and continuous evaluation. Instead of relying on AI to guess, our agents follow SOP-based flows, apply intelligence only where judgment is needed, and escalate to humans when confidence is low. Every output is evaluated against accuracy criteria, and agents learn from feedback to improve over time. The result: automation you can trust, even in complex, high-stakes environments.

The biggest myth in QA?   100% automation.  I have heard it too many times:   “Let’s automate everything.”   “Once we cover all test cases, quality is guaranteed.”  But automation has limits.   It cannot test user experience.   It cannot explore the unexpected.   It cannot think like a real customer.  The goal is not 100% automation.   The goal is confidence in releases.  Automation is a tool.   QA is the strategy.