Algorithmic Fairness in Lending: What Enterprises Need to Know
There is no single measure of fairness. Understanding the trade-offs between different fairness definitions is essential for building AI systems that are both effective and equitable.
AI Insights & News
The Biggest Myth About AI Safety: Someone Else Is Handling It
The most dangerous assumption in AI deployment isn't technical—it's organizational. Most executives believe AI safety is handled by their vendor. It's not.
DevOps Can't Govern AI: Why Infrastructure Metrics Miss the Point
A viral article claims AI governance is just good DevOps. We disagree. DevOps manages whether systems are running. Governance manages whether systems are behaving. These are not the same thing.
AI Safety in Generative AI: Priorities and Practices
Safety should be a top priority in all AI endeavors. The true threat lies not in chatbot vulnerabilities but in AI systems synthesizing hard-to-find disruptive information.
From Policy to Protocol: Why AI Governance Must Become Infrastructure in 2026
The era of PDF policies is over. In 2026, AI governance moves from manual compliance to "Validation-as-a-Service"—real-time, protocol-driven guardrails integrated directly into the stack.
The Trust Crisis of Agentic AI: Securing the New Autonomous Workforce
Agentic AI promises autonomy, but autonomy requires trust. Learn how to bridge the 'Trust Gap' with a dedicated supervision layer that monitors, governs, and secures your digital workforce.
AI Regulations Are Here: Preparing for Compliance
New regulations in the EU and US require algorithmic transparency and explainability. Organizations that prepare now will have competitive advantages over those that wait.
AI Observability: The Build vs Buy Decision
Every organization deploying ML models faces the build vs buy decision for observability. The right choice depends on factors that most teams underestimate at the outset.
AI Needs a New Developer Stack
The tools we use to build software were designed for code written by humans. Machine learning demands a fundamentally different approach: tools designed for systems where behavior emerges from data.
AI Innovation and Ethics: Aligning Language Models with Human Values
As LLMs become more capable, aligning them with human values grows more complex. The path forward requires coordinated research across oversight, robustness, interpretability, and governance.
AI Governance in the Age of Generative AI
Governance is not a constraint on innovation. It is what makes innovation sustainable. Organizations that embed governance into their AI workflows move faster than those that treat it as an afterthought.
Adversarial Attacks on Machine Learning Models
Machine learning models can be fooled by carefully crafted inputs that appear normal to humans. Understanding adversarial attacks is essential for building secure AI systems.
AI Agents vs. Prompts: When Simple Is Enough
Not every AI problem needs an autonomous agent. Here's how to choose between agents, prompts, and API calls, and why overengineering is the real risk.
Healthcare AI Ethics Are Operational, Not Aspirational
Healthcare AI ethics aren't about principles on a wall. They're about what happens when an algorithm influences whether someone gets treated. Here's what operational ethics actually looks like.
MLOps is How You Actually Deploy AI
80% of ML projects never make it to production. The problem isn't modeling. It's everything that happens after. MLOps is the discipline that bridges the gap.
MLOps vs. DevOps: Data Changes Everything
DevOps practices don't translate directly to ML systems. Here's why data makes MLOps fundamentally different—and what that means for teams trying to operationalize AI.
Responsible AI is Operational, Not Philosophical
Responsible AI isn't about ethics committees and principles documents. It's about operational practices that produce trustworthy outcomes. Here's what that actually looks like.
The Agentic Framework Landscape: What Actually Matters
The AI agent framework space is exploding. Here's how to evaluate options without getting lost in feature lists—and what to look for in an agentic architecture.
The Guardrails-Velocity Trap: Why Speed and Safety Aren't a Tradeoff
The conventional wisdom says you can move fast or move safely. That's a false choice. Here's how to build AI systems that are both fast and trustworthy.
Why Model Monitoring is Essential, Not Optional
91% of ML models degrade over time. Without monitoring, you won't know until your customers do. Here's why monitoring is the difference between AI that works and AI that worked.
The Patient Navigation Crisis Has an Answer: Trustworthy AI at Scale
DiMe is launching a multi-stakeholder initiative to define and scale AI-enabled care navigation that works for patients and the healthcare system. Here's why it matters.
You've Selected an AI Help Desk Agent. Now What?
Selection is just the beginning. Learn why 80% of enterprises deploy AI customer service agents without proper governance, the pitfalls that emerge in months 3-6, and what proper supervision infrastructure looks like.
AI Customer Service Agents: Build vs. Buy and the 10 Concerns Nobody Talks About
The build vs. buy debate for AI help desk agents misses the point. Both paths fail for the same reason. Here are the 10 concerns that actually matter when deploying AI customer service agents, and why supervision is the missing layer.
The Deflection Rate Dilemma: 5 Ways to Ensure Your AI Help Agent's Numbers Are Real
Deflection rate is a powerful metric for AI help desk success. Here are five ways to ensure your numbers represent genuine customer resolution, not just closed tickets.
From Babysitting Bots to Managing Armies: The Future of AI Supervision at Scale
Most teams think adding AI agents increases output. What they get instead is babysitting. Learn how supervision infrastructure transforms agents from toys into scalable tools.
The Responsibility Gap: Why AI Builders Won't Save Us
Why expecting AI labs to prioritize safety over capability is the wrong approach. The real power lies with enterprise buyers who can demand audit-ready evidence and supervision layers.
From Line Cooks to Chefs: Why Goal-Based Programming Is the Next Era of AI Engineering
Software is shifting from deterministic “recipe-following” code to agentic, goal-driven systems that can adapt to changing inputs, contexts, and user intent. Using a line-cooks-vs-chefs metaphor, you argue that agents should be given goals, constraints, and tools—then trusted to plan and iterate—illustrated by your Swept AI Airtable enrichment workflow and by agentic red teaming. The larger takeaway: teams that embrace goal-based programming and AI-first/API-first interfaces will build more resilient, scalable systems than those clinging to brittle procedural scripts.
Noise Is the Real Test: AI Quality Assurance Needs a New Foundation
Clean-input testing creates a false sense of reliability in AI systems. By mapping normal behavior, gradually increasing noise, finding collapse thresholds, and supervising based on deviations, teams can build AI that holds up under real-world messiness.
Guardrails Are Not Enough, Real AI Safety Requires Hard Policy Boundaries
Stacking LLMs to supervise other LLMs looks like “defense in depth,” but it actually multiplies probabilistic failure points. If a judge model is consistently better than the base model, that’s a sign the architecture is backwards. Real AI supervision for safety-sensitive use cases requires deterministic policies enforced in code, paired with distribution-aware evaluation that detects drift and deviations. Guardrails can help understand behavior, but hard boundaries protect systems when behavior goes wrong.
Gemini 3 and the New Era of Autonomous AI: What It Unlocks and Why Supervision Now Matters More Than Ever
Google’s release of Gemini 3 marks a real turning point in how we think about agentic systems, autonomous workflows and the role of human supervision. Over the last year we have seen steady progress across the major model labs, but most of those advances still required a heavy human touch. Developers were effectively babysitting agents, guiding them step by step, correcting them as they went, and patching the same blind spots over and over.
Building Trustworthy AI: Navigating the Challenges and Future of Agentic Software with Shane Emmons
In this episode of The Innovators & Investors Podcast, host Kristian Marquez sits down with Shane Emmons, founder and CEO of Swept, to explore the complexities and challenges surrounding AI trust and reliability
Your AI Works But Nobody Trusts It
Companies aren’t abandoning AI because models fail — they abandon them because nobody can explain decisions. Learn why trust, explainability, and an “evidence layer” matter more than accuracy scores, and how to build AI systems that operators actually adopt.
When AI Mistakes Chips for Guns
AI detection systems aren’t consistent — and schools are discovering the cost. When prediction becomes action without verification, students get harmed. Here’s how to fix the AI verification gap before the next false alarm.
Everyone Says AI Is Failing But The Numbers Tell A Different Story
AI adoption is accelerating, but measurable ROI still lags. Learn how the gap between deployment metrics and behavioral supervision causes 80% of AI systems to fail at impact — and why tracking refusal patterns reveals true AI performance.
GPT-5 Removed the One Thing Digital Health & the Enterprise AI Needs
GPT-5 dropped temperature control, eroding repeatability and auditability. Swept AI explains why determinism matters and how to certify agentic workflows.
Swept AI Building the Trust Layer for Artificial Intelligence
Alex Mysinek sits down with Shane Emmons, Founder and CEO of Swept AI, to talk about the missing piece in the AI revolution—trust. We are creating a system that supervises, evaluates, and protects AI models to ensure safety, accuracy, and alignment.
Why Most Digital Health AI Validation Completely Misses The Point
AI systems like GRACE 3.0 prove that real validation goes beyond accuracy scores. Learn why behavioral consistency, edge case testing, and drift detection matter more than single-run accuracy when deploying AI in healthcare and enterprise systems.
Lawyers, AI Won't Take Your Job, But It Could Get You Fired
AI won’t replace lawyers, but careless use can jeopardize careers. Treat AI as an assistant to speed research, review, and drafting—while enforcing oversight to catch drift, verify outputs, and protect client data. Maintain monitoring, training, and strict privacy controls. Tools like Swept.AI help detect drift early so you stay compliant and in control.
Currently Most AI Implementations Are Expensive Corporate Theater
AI deployment in enterprises is no longer hindered by capability or integration challenges but by a systemic trust gap. Organizations can’t reliably build processes around systems that produce inconsistent or hallucinated outputs. Swept’s Trust Framework addresses this through nine pillars—Security, Reliability, Integrity, Privacy, Explainability, Ethical Use, Model Provenance, Vendor Risk, and Incident Response—with reliability and security as the most common failure points. The solution lies in context engineering: a structured, auditable way to control variance and ensure AI outputs remain within defined, acceptable bounds. The future of enterprise AI isn’t more power—it’s trustworthy performance.
The "S" in AI Doesn't Stand for Safety—But It Should
The AI ecosystem is riddled with gaps between promise and proof. At Swept AI, we're building the missing infrastructure layer for AI reliability—testing agents like attackers would and monitoring them in production so teams can move fast and stay in control.
AI Hallucinations vs. AI Drift: Understanding and Managing AI Drift for Long-Term Success
In the dynamic world of AI, ensuring system reliability and accuracy is challenging due to two critical issues: AI hallucinations and AI drift. While hallucinations are dramatic and often headline-grabbing, AI drift is a more insidious, long-term threat.
Inside Every LLM Is the Algorithm You’re Looking For
At Swept, we don’t see LLMs as chatbots. We see them as something bigger: a universal engine for function discovery. Need a parser, a scoring system, or a triage rule? The model already contains it—you just have to find it.
Why Every AI Race Ends In Expensive Disasters
Organizations rushing AI to market without proper validation face millions in avoidable losses. This analysis examines real cases like IBM's $4 billion Watson Health writedown and reveals why 42% of AI projects now fail before production. Learn the difference between structured and unstructured AI deployment, discover proven validation frameworks that prevent costly failures, and understand how thorough testing actually accelerates successful implementation rather than delaying it.
AI Blind Spots: Uncovering Hidden Biases and Risks in Your Data (Before They Derail Your Business)
AI can supercharge your business—but hidden biases in your data can quietly undermine it. Discover how to spot and fix these blind spots before they lead to unfair outcomes, legal trouble, or lost trust.
Swept AI Raises $1.4M to Supervise the Next Generation of Autonomous Systems
Swept AI, a startup focused on supervising, interrogating, and optimizing autonomous AI agents, has raised $1.4M in pre-seed funding led by M25, with participation from Wellington Management Company, BuffGold Ventures, SPARK Capital, Service Provider Capital, The Unicorn Group, and angel investors.
Does AI Pose an Existential Risk? Examining Current Threats and Limitations
This article explores how AI is transforming the accounting profession. It positions AI as a powerful assistant rather than a replacement—helping automate repetitive tasks and freeing accountants to focus on higher-value strategic work. However, the post emphasizes that blind reliance on AI can be dangerous: without oversight, data drifts, compliance breaches, or misconfigurations could expose firms to serious risks. The key takeaway is that accountants must remain vigilant, continuously update their knowledge, and actively monitor AI systems. Those who embrace AI responsibly will thrive, while neglect may put their careers at risk.
Accountants, AI Won't Take Your Job, But It Will Get You Fired
AI is becoming an essential tool for accountants, helping automate repetitive tasks like data entry and anomaly detection so professionals can focus on strategy and client advisory. However, blind reliance on AI without oversight can cause serious issues. Risks include data drift leading to inaccuracies, security breaches involving sensitive financial data, and compliance failures with privacy regulations. Accountants must remain vigilant by monitoring outputs, configuring tools properly, and staying trained on AI’s strengths and limitations. With proactive oversight and tools like Swept.AI, accountants can maximize efficiency and maintain trust without being replaced.
Accountants, AI Won't Take Your Job, But It Will Get You Fired
It seems as though time is always of the essence for accountants, and AI stands as a powerful ally to manage this pressure. By embracing it, you can handle tedious tasks more efficiently, freeing you up to focus on complex, strategic work. However, blind trust in AI without proper oversight can lead to significant issues, including severe drifts and potential breaches of sensitive information.
AI Accuracy Under Attack: How to Red Team Your LLMs Before They Explode (and Damage Your Business)
AI systems are vulnerable to security risks like prompt injection and data poisoning. Learn how AI red teaming can help protect your business from threats before they cause damage.
AI Trust Validation: Agents Testing Agents at Scale
The biggest takeaway from this event isn’t that an AI system can be compromised. We already knew that. It’s that many teams are still pushing updates without a clear, enforceable model for trust validation before release.
Beyond Unit Tests: Level Up Your AI Testing Strategy (Variant and Invariant Testing Explained)
Unit tests aren’t enough for AI. Discover how variant and invariant testing can reveal blind spots in your models and help you build smarter, more reliable AI systems.
Designers, AI Won't Take Your Job, But It Could Get You Fired
AI is quickly becoming an essential design tool. By embracing AI, you can automate repetitive tasks, enhance your creative process, and streamline your workflow. However, blindly trusting AI without proper oversight can lead to significant issues, including design inconsistencies and potential breaches of sensitive information.
Developers, AI Won't Take Your Job, But It Could Get You Fired
Software development requires innovation and speed. AI copilots have merged as vital instruments. Utilizing its capabilities, repetitive coding tasks can be delegated, allowing developers to dedicate their time to addressing intricate problems and fostering innovation. However, relying solely on AI without diligent supervision may result in substantial complications, including significant deviations and potential breaches of sensitive information.
Founders, AI Won't Take Your Business, But It Will Destroy It If Mismanaged
As a founder, you're constantly juggling multiple responsibilities, from securing funding to scaling operations. In this demanding environment, AI stands as a powerful ally. By embracing it, you can streamline operations, enhance decision-making, and focus on strategic growth. However, blind trust in AI without proper oversight can lead to significant issues, including severe drifts and potential breaches of sensitive information.
From Demo to Deployment: The AI Consistency Crisis (and How Swept.AI Solves It)
Your AI wowed in the demo—but can it deliver in production? Learn how model drift, hidden biases, and lack of observability fuel the AI Consistency Crisis—and how to solve it with Swept.AI.
Google’s AI: Brilliant, Bloated, and Barreling Ahead
If OpenAI is lurking in the shadows and Rabbit stumbled publicly, Google is going full-throttle—unleashing a torrent of AI models everywhere at once.
Investors, Don't Let AI Snake Oil Hurt Your Fund
AI has reached peak hype. Every investment has the chance to make—or possibly break—your fund. With numerous startups boasting groundbreaking AI solutions, it’s easy to get swept up in the hype. However, not all AI is created equal. Blindly trusting AI without thorough due diligence can lead to significant risks, including poor investment choices and potential breaches of sensitive information.
Jony Ive, a Prototype, and $6.5B of Belief
OpenAI just bought Jony Ive’s secretive AI hardware startup, io, for $6.5 billion. No product. No launch. Just a prototype and a promise.
MCP Is Not the USB of AI—It’s Just HTTP
There’s a growing tendency in AI marketing circles to refer to MCP—the Model Context Protocol—as the “USB of AI.” The idea, presumably, is that it offers some kind of plug-and-play universal interface between language models and tools. But this metaphor is worse than lazy—it’s actively misleading.Let’s dig into why this comparison doesn’t work, and why we should be framing MCP for what it really is: the HTTP of agentic AI.
Navigating the AI Hype: Practical Observability and Realistic Expectations
This article explores the practical aspects of AI implementation, emphasizing the importance of observability, testing, and realistic expectations. It offers valuable insights for developers seeking to leverage AI effectively in solving real-world problems.
Product Managers, AI Won't Take Your Job, But It Could Get You Fired
Product management is constantly under pressure to innovate. Customer requests are relentless. AI stands as a potential powerful ally. By leveraging AI, you can streamline product development, enhance user experiences, and drive data-driven decisions. However, blindly trusting AI without proper oversight can lead to significant issues, including severe drifts and potential breaches of sensitive information.
Rabbit R1 Security Breach Highlights the Need for Robust Validation Mechanisms in AI Compani
The Rabbit R1 security breach serves as a cautionary tale for the AI industry. It highlights the urgent need for comprehensive validation mechanisms to ensure that AI companies maintain high standards of security and quality.
Stop Training Your Own Model! A Pragmatic Guide to AI Implementation (When to Say No to LLMs)
In the gold rush of AI, it's easy to get caught up in the hype. The siren song of "train your own model!" echoes through boardrooms and tech conferences. But before you dive headfirst into the deep end of AI development, ask yourself a crucial question: Do you really need to train your own AI model?
The Bold Vision and Harsh Reality of the Humane AI Pin
Despite its elegant design, the AI Pin faced significant challenges in replacing smartphones. Convincing a skeptical public and investors of its viability proved difficult, as integrating advanced technology into everyday use is fraught with hurdles, particularly in ensuring user adoption and trust.