Assessing AI Reliability in Real-World Workflows

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

The Secure AI Lifecycle (SAIL) Framework is one of the actionable roadmaps for building trustworthy and secure AI systems. Key highlights include: • Mapping over 70 AI-specific risks across seven phases: Plan, Code, Build, Test, Deploy, Operate, Monitor • Introducing “Shift Up” security to protect AI abstraction layers like agents, prompts, and toolchains • Embedding AI threat modeling, governance alignment, and secure experimentation from day one • Addressing critical risks including prompt injection, model evasion, data poisoning, plugin misuse, and cross-domain prompt attacks • Integrating runtime guardrails, red teaming, sandboxing, and telemetry for continuous protection • Aligning with NIST AI RMF, ISO 42001, OWASP Top 10 for LLMs, and DASF v2.0 • Promoting cross-functional accountability across AppSec, MLOps, LLMOps, Legal, and GRC teams Who should take note: • Security architects deploying foundation models and AI-enhanced apps • MLOps and product teams working with agents, RAG pipelines, and autonomous workflows • CISOs aligning AI risk posture with compliance and regulatory needs • Policymakers and governance leaders setting enterprise-wide AI strategy Noteworthy aspects: • Built-in operational guidance with security embedded across the full AI lifecycle • Lifecycle-aware mitigations for risks like context evictions, prompt leaks, model theft, and abuse detection • Human-in-the-loop checkpoints, sandboxed execution, and audit trails for real-world assurance • Designed for both code and no-code AI platforms with complex dependency stacks Actionable step: Use the SAIL Framework to create a unified AI risk and security model with clear roles, security gates, and monitoring practices across teams. Consideration: Security in the AI era is more than a tech problem. It is an organizational imperative that demands shared responsibility, executive alignment, and continuous vigilance.

Today, we’re sharing more about one of the most important parts of building trustworthy and reliable AI agents: testing. Every CX and product leader wants agents that are fast, helpful, and on-brand. But with non-deterministic models, even small changes to prompts or your knowledge base can change how an agent behaves. That’s why we built a complete testing suite directly into Decagon: ➤ Unit tests for consistent, policy-aligned responses ➤ Integration checks to ensure the right data gets pulled, tools get triggered, and the agent behaves as intended ➤ Simulations to make sure agents perform reliably across entire workflows, over and over again It all lives in the same place where you define and edit Agent Operating Procedures (AOPs), so CX teams can ship fast without guessing how changes will impact customers. If you’re deploying agents without seeing how they’ll perform in real-world scenarios, you’re flying blind. Check out the full blog in the comments.

RAG solutions are failing, and a new Stanford study reveals why. 🚨 A Stanford study finds a 20-30% hallucination rate for AI RAG solutions claiming "100% hallucination-free results." Their analysis explains why many RAG systems fail to meet expectations. Why Hallucinations Still Happening: 1. A RAG (Retrieval Augmented Generation) solution retrieves some "relevant" document/chunk, but that reference may not "answer" the question. 2. In the case of legal research, a hallucination can occur when: - A response contains incorrect information. - A response includes a false assertion that a source supports a proposition. 3. Legal research involves an understanding of facts (not just style) beyond the information retrieved. This level of grounded knowledge and reasoning is beyond today's LLMs. What Should You Do: 🧠 Understand the limits of RAG. LLMs have been purposely trained to output information that "appears" accurate and trustworthy. You need to include domain experts early in the evaluation. This will let you know if RAG can "answer" questions correctly. 📖 Get educated. Go read the paper. It provides a wealth of examples. If you weren't an expert in the law, you would not be aware that the model is hallucinating. The Takeaway 📚 RAG helps us make sense of enormous amounts of information, so keep using it for preliminary research. - Don't expect RAG to bring a sophisticated understanding of the subject matter. - Evaluation must include subject matter experts! LLMs are designed to output information that "appears" accurate and trustworthy. Go check out the paper: Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools https://lnkd.in/gXb7zVzk

🗺 Navigating AI Impact Assessments with ISO 42005: Essential Areas for Compliance Leaders 🗺 In speaking with compliance, cybersecurity, and AI leaders around the world, one of the most common questions I have been getting of late is, “As we prepare for ISO 42001 certification, what blind spots should we be working to address?” Without hesitation, my response has been, and will continue to be, conducting and documenting a meaningful AI Impact assessment. Fortunately, though still in DRAFT status, ISO 42005 provides a structured framework for organizations to navigate that very concern effectively. As compliance executives, understanding and integrating the key components of this standard into your AI impact assessments is critical; below are the areas I feel are most essential for you to begin your journey. 1. Ethical Considerations and Bias Management:   - Address potential biases and ensure fairness across AI functionalities. Evaluate the design and operational parameters to mitigate unintended discriminatory outcomes. 2. Data Privacy and Security:   - Incorporate robust measures to protect sensitive data processed by AI systems. Assess the risks related to data breaches and establish protocols to secure personal and proprietary information. 3. Transparency and Explainability:   - Ensure that the workings of AI systems are understandable and transparent to stakeholders. This involves documenting the AI's decision-making processes and maintaining clear records that explain the logic and reasoning behind AI-driven decisions. 4. Operational Risks and Safeguards:   - Identify operational vulnerabilities that could affect the AI system’s performance. Implement necessary safeguards to ensure stability and reliability throughout the AI system's lifecycle. 5. Legal and Regulatory Compliance:   - Regularly update the impact assessments to reflect changing legal landscapes, especially concerning data protection laws and AI-specific regulations. 6. Stakeholder Impact:   - Consider the broader implications of AI implementation on all stakeholders, including customers, employees, and partners. Evaluate both potential benefits and harms to align AI strategies with organizational values and societal norms. By starting with these critical areas in your AI impact assessments as recommended by ISO 42005, you can steer your organization towards responsible AI use in a way that upholds ethical standards and complies with regulatory, and market, expectations. If you need help getting started, as always, please don't hesitate to let us know! A-LIGN #AICompliance #ISO42005 #EthicalAI #DataProtection #AItransparency #iso42001 #TheBusinessofCompliance #ComplianceAlignedtoYou

The vast majority of AI research effort seems to be going into improving capability rather than reliability, and I think it should be the opposite. If AI could *reliably* do all the things it's *capable* of, it would truly be a sweeping economic transformation. A good example is this brutal review of the Humane AI Pin. If the device worked reliably, it would be a magical user experience. But it fails more than half the time, making the experience so frustrating that no one would want to use it. https://lnkd.in/eMAfSzUw Most useful real-world tasks require agentic workflows. A flight-booking agent would need to make dozens of calls to LLMs. If each of those went wrong independently with a probability of say just 2%, the overall system have an unacceptable failure rate. Improving reliability from 98% to 99.5% will have a vastly greater impact on apps than building a 10x bigger model with an array of new capabilities. Building agents is like writing code in a programming language where individual instructions (LLM calls) are stochastic. The challenge is to figure out programming abstractions and error correction mechanisms that will enable reliable code on top of such an unreliable primitive. AI evaluation also needs to change. Current benchmarking practices measure only capability, not reliability. The variance matters as much as the mean. If a model scores 80%, does it mean it works 80% of the time on each instance or 100% of the time on a subset? The implications for downstream users are totally different.

We keep talking about model accuracy. But the real currency in AI systems is trust. Not just “do I trust the model output?” But: • Do I trust the data pipeline that fed it? • Do I trust the agent’s behavior across edge cases? • Do I trust the humans who labeled the training data? • Do I trust the update cycle not to break downstream dependencies? • Do I trust the org to intervene when things go wrong? In the enterprise, trust isn’t a feeling. It’s a systems property. It lives in audit logs, versioning protocols, human-in-the-loop workflows, escalation playbooks, and update governance. But here’s the challenge: Most AI systems today don’t earn trust. They borrow it. They inherit it from the badge of a brand, the gloss of a UI, the silence of users who don’t know how to question a prediction. Until trust fails. • When the AI outputs toxic content. • When an autonomous agent nukes an inbox or ignores a critical SLA. • When a board discovers that explainability was just a PowerPoint slide. Then you realize: Trust wasn’t designed into the system. It was implied. Assumed. Deferred. Good AI engineering isn’t just about “shipping the model.” It’s about engineering trust boundaries that don’t collapse under pressure. And that means: → Failover, not just fine-tuning. → Safeguards, not just sandboxing. → Explainability that holds up in court, not just demos. → Escalation paths designed like critical infrastructure, not Jira tickets. We don’t need to fear AI. We need to design for trust like we’re designing for failure. Because we are. Where are you seeing trust gaps in your AI stack today? Let’s move the conversation beyond prompts and toward architecture.